From c2736d30b1662c5d87fa44c8c8e37fa121f0c1a5 Mon Sep 17 00:00:00 2001 From: dongsung Date: Sun, 2 Dec 2018 23:23:25 +0000 Subject: [PATCH] initial commit --- Knowledge Distillation.docx | Bin 0 -> 910936 bytes README.md | 41 + Untitled5.ipynb | 1146 + __pycache__/mobilenet.cpython-35.pyc | Bin 0 -> 1057 bytes __pycache__/xception.cpython-35.pyc | Bin 0 -> 5093 bytes caltech-256.ipynb | 405 + fine_tuning_with_keras.py | 0 get_logits_from_xception.ipynb | 215 + keras_model_practice.ipynb | 350 + knowledge_distillation_for_mobilenet.ipynb | 726 + knowledge_distillation_for_squeezenet.ipynb | 587 + mnist_data_test.ipynb | 97 + mobilenet.py | 23 + numpy_append_test.ipynb | 247 + squeezenet.py | 96 + squeezenet_weights.hdf5 | Bin 0 -> 3034616 bytes test_weight.h5 | Bin 0 -> 13064 bytes .../train_val_split-checkpoint.ipynb | 366 + train_val_split/decode.npy | Bin 0 -> 4943 bytes train_val_split/train_metadata.csv | 25468 ++++++++++++++++ train_val_split/train_val_split.ipynb | 349 + train_val_split/val_metadata.csv | 5141 ++++ train_xception.ipynb | 361 + utils.py | 14 + .../image_preprocessing_ver1.cpython-35.pyc | Bin 0 -> 36209 bytes .../image_preprocessing_ver2.cpython-35.pyc | Bin 0 -> 36343 bytes utils/image_preprocessing_ver1.py | 1065 + utils/image_preprocessing_ver2.py | 1066 + utils/split_and_resize_data.ipynb | 393 + utils/split_but_no_resizing.ipynb | 277 + vanilla_mobilenet.ipynb | 722 + vanilla_squeezenet.ipynb | 323 + xception.py | 130 + xception_fine_tuning.ipynb | 1191 + 34 files changed, 40799 insertions(+) create mode 100644 Knowledge Distillation.docx create mode 100644 README.md create mode 100644 Untitled5.ipynb create mode 100644 __pycache__/mobilenet.cpython-35.pyc create mode 100644 __pycache__/xception.cpython-35.pyc create mode 100644 caltech-256.ipynb create mode 100644 fine_tuning_with_keras.py create mode 100644 get_logits_from_xception.ipynb create mode 100644 keras_model_practice.ipynb create mode 100644 knowledge_distillation_for_mobilenet.ipynb create mode 100644 knowledge_distillation_for_squeezenet.ipynb create mode 100644 mnist_data_test.ipynb create mode 100644 mobilenet.py create mode 100644 numpy_append_test.ipynb create mode 100644 squeezenet.py create mode 100644 squeezenet_weights.hdf5 create mode 100644 test_weight.h5 create mode 100644 train_val_split/.ipynb_checkpoints/train_val_split-checkpoint.ipynb create mode 100644 train_val_split/decode.npy create mode 100644 train_val_split/train_metadata.csv create mode 100644 train_val_split/train_val_split.ipynb create mode 100644 train_val_split/val_metadata.csv create mode 100644 train_xception.ipynb create mode 100644 utils.py create mode 100644 utils/__pycache__/image_preprocessing_ver1.cpython-35.pyc create mode 100644 utils/__pycache__/image_preprocessing_ver2.cpython-35.pyc create mode 100644 utils/image_preprocessing_ver1.py create mode 100644 utils/image_preprocessing_ver2.py create mode 100644 utils/split_and_resize_data.ipynb create mode 100644 utils/split_but_no_resizing.ipynb create mode 100644 vanilla_mobilenet.ipynb create mode 100644 vanilla_squeezenet.ipynb create mode 100644 xception.py create mode 100644 xception_fine_tuning.ipynb diff --git a/Knowledge Distillation.docx b/Knowledge Distillation.docx new file mode 100644 index 0000000000000000000000000000000000000000..af5b3a2105c11e448ace5fa2af20ebecfdff77f2 GIT binary patch literal 910936 zcmaI6Q?MveldZdK+qP}nwr$(k%eHOXwr$(CvG4x7qfeasaJn99&Zwu#%&$ge&KL^P zz#u395D*Xm?hTSSqWPUm53bEBahyTpK^3+P?`y?44W zrm|36v=ALB06~a>w$Ei}9D|>5Q>@(D5AJ_IwW$Fqm#NMSw75t`a5DAii?iqL?G?9q zl|?QF4w)JWaSLp25(IcE0TOKQbN^t-nH?0k7!Mu~73*6+1hq}0r(@5H*Myo%W@kXq z{)q7?SnhhPnJPqRrVt|%+~HUoVxo{$wXFd&ImUrSG(c275vfq!~SX9S18o-tCbToFSX$O7Al_GQ{>QsRiJ z9Qm?qjD&x$eDV2kBW>v)14TV@#lz!8z7%xd|E|;oU#ru!+@=TdLAG^yl#w&4Wm-QS zSF*2F;!7bC4!;|fGEXEcTIn~WaG@<~u!FYiq*`KBr~xXVzrVE?C^{D7PT1ZZGa;)YT0A!fYF|Yv<#eXh(+2Fq884-^y*c 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x_P;RxH<8QV{r#U$uz$Tc{NFaV0sBv{5pt5CU-uBe*Wkbe9DSW>Zoz*2`#\n", + "\n", + "Tensor(\"predictions/Softmax:0\", shape=(?, 1000), dtype=float32)\n" + ] + } + ], + "source": [ + "print(base_model.layers[-1].get_weights())\n", + "print(base_model.layers[-1])\n", + "print(base_model.layers[-2])\n", + "print(base_model.output)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "base_model.layers.pop()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Tensor(\"avg_pool_2/Mean:0\", shape=(?, 2048), dtype=float32)\n", + "Tensor(\"predictions_2/Softmax:0\", shape=(?, 1000), dtype=float32)\n" + ] + } + ], + "source": [ + "print(base_model.layers[-1])\n", + "print(base_model.layers[-1].output)\n", + "print(base_model.output)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "__________________________________________________________________________________________________\n", + "Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + "input_3 (InputLayer) (None, None, None, 3 0 \n", + "__________________________________________________________________________________________________\n", + "block1_conv1 (Conv2D) (None, None, None, 3 864 input_3[0][0] \n", + "__________________________________________________________________________________________________\n", + "block1_conv1_bn (BatchNormaliza (None, None, None, 3 128 block1_conv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block1_conv1_act (Activation) (None, None, None, 3 0 block1_conv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block1_conv2 (Conv2D) (None, None, None, 6 18432 block1_conv1_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block1_conv2_bn (BatchNormaliza (None, None, None, 6 256 block1_conv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "block1_conv2_act (Activation) (None, None, None, 6 0 block1_conv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block2_sepconv1 (SeparableConv2 (None, None, None, 1 8768 block1_conv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block2_sepconv1_bn (BatchNormal (None, None, None, 1 512 block2_sepconv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block2_sepconv2_act (Activation (None, None, None, 1 0 block2_sepconv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block2_sepconv2 (SeparableConv2 (None, None, None, 1 17536 block2_sepconv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block2_sepconv2_bn (BatchNormal (None, None, None, 1 512 block2_sepconv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_9 (Conv2D) (None, None, None, 1 8192 block1_conv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block2_pool (MaxPooling2D) (None, None, None, 1 0 block2_sepconv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_9 (BatchNor (None, None, None, 1 512 conv2d_9[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_25 (Add) (None, None, None, 1 0 block2_pool[0][0] \n", + " batch_normalization_9[0][0] \n", + "__________________________________________________________________________________________________\n", + "block3_sepconv1_act (Activation (None, None, None, 1 0 add_25[0][0] \n", + "__________________________________________________________________________________________________\n", + "block3_sepconv1 (SeparableConv2 (None, None, None, 2 33920 block3_sepconv1_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block3_sepconv1_bn (BatchNormal (None, None, None, 2 1024 block3_sepconv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block3_sepconv2_act (Activation (None, None, None, 2 0 block3_sepconv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block3_sepconv2 (SeparableConv2 (None, None, None, 2 67840 block3_sepconv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block3_sepconv2_bn (BatchNormal (None, None, None, 2 1024 block3_sepconv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_10 (Conv2D) (None, None, None, 2 32768 add_25[0][0] \n", + "__________________________________________________________________________________________________\n", + "block3_pool (MaxPooling2D) (None, None, None, 2 0 block3_sepconv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_10 (BatchNo (None, None, None, 2 1024 conv2d_10[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_26 (Add) (None, None, None, 2 0 block3_pool[0][0] \n", + " batch_normalization_10[0][0] \n", + "__________________________________________________________________________________________________\n", + "block4_sepconv1_act (Activation (None, None, None, 2 0 add_26[0][0] \n", + "__________________________________________________________________________________________________\n", + "block4_sepconv1 (SeparableConv2 (None, None, None, 7 188672 block4_sepconv1_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block4_sepconv1_bn (BatchNormal (None, None, None, 7 2912 block4_sepconv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block4_sepconv2_act (Activation (None, None, None, 7 0 block4_sepconv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block4_sepconv2 (SeparableConv2 (None, None, None, 7 536536 block4_sepconv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block4_sepconv2_bn (BatchNormal (None, None, None, 7 2912 block4_sepconv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_11 (Conv2D) (None, None, None, 7 186368 add_26[0][0] \n", + "__________________________________________________________________________________________________\n", + "block4_pool (MaxPooling2D) (None, None, None, 7 0 block4_sepconv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_11 (BatchNo (None, None, None, 7 2912 conv2d_11[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_27 (Add) (None, None, None, 7 0 block4_pool[0][0] \n", + " batch_normalization_11[0][0] \n", + "__________________________________________________________________________________________________\n", + "block5_sepconv1_act (Activation (None, None, None, 7 0 add_27[0][0] \n", + "__________________________________________________________________________________________________\n", + "block5_sepconv1 (SeparableConv2 (None, None, None, 7 536536 block5_sepconv1_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block5_sepconv1_bn (BatchNormal (None, None, None, 7 2912 block5_sepconv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block5_sepconv2_act (Activation (None, None, None, 7 0 block5_sepconv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block5_sepconv2 (SeparableConv2 (None, None, None, 7 536536 block5_sepconv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block5_sepconv2_bn (BatchNormal (None, None, None, 7 2912 block5_sepconv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "block5_sepconv3_act (Activation (None, None, None, 7 0 block5_sepconv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block5_sepconv3 (SeparableConv2 (None, None, None, 7 536536 block5_sepconv3_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block5_sepconv3_bn (BatchNormal (None, None, None, 7 2912 block5_sepconv3[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_28 (Add) (None, None, None, 7 0 block5_sepconv3_bn[0][0] \n", + " add_27[0][0] \n", + "__________________________________________________________________________________________________\n", + "block6_sepconv1_act (Activation (None, None, None, 7 0 add_28[0][0] \n", + "__________________________________________________________________________________________________\n", + "block6_sepconv1 (SeparableConv2 (None, None, None, 7 536536 block6_sepconv1_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block6_sepconv1_bn (BatchNormal (None, None, None, 7 2912 block6_sepconv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block6_sepconv2_act (Activation (None, None, None, 7 0 block6_sepconv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block6_sepconv2 (SeparableConv2 (None, None, None, 7 536536 block6_sepconv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block6_sepconv2_bn (BatchNormal (None, None, None, 7 2912 block6_sepconv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "block6_sepconv3_act (Activation (None, None, None, 7 0 block6_sepconv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block6_sepconv3 (SeparableConv2 (None, None, None, 7 536536 block6_sepconv3_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block6_sepconv3_bn (BatchNormal (None, None, None, 7 2912 block6_sepconv3[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_29 (Add) (None, None, None, 7 0 block6_sepconv3_bn[0][0] \n", + " add_28[0][0] \n", + "__________________________________________________________________________________________________\n", + "block7_sepconv1_act (Activation (None, None, None, 7 0 add_29[0][0] \n", + "__________________________________________________________________________________________________\n", + "block7_sepconv1 (SeparableConv2 (None, None, None, 7 536536 block7_sepconv1_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block7_sepconv1_bn (BatchNormal (None, None, None, 7 2912 block7_sepconv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block7_sepconv2_act (Activation (None, None, None, 7 0 block7_sepconv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block7_sepconv2 (SeparableConv2 (None, None, None, 7 536536 block7_sepconv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block7_sepconv2_bn (BatchNormal (None, None, None, 7 2912 block7_sepconv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "block7_sepconv3_act (Activation (None, None, None, 7 0 block7_sepconv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block7_sepconv3 (SeparableConv2 (None, None, None, 7 536536 block7_sepconv3_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block7_sepconv3_bn (BatchNormal (None, None, None, 7 2912 block7_sepconv3[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_30 (Add) (None, None, None, 7 0 block7_sepconv3_bn[0][0] \n", + " add_29[0][0] \n", + "__________________________________________________________________________________________________\n", + "block8_sepconv1_act (Activation (None, None, None, 7 0 add_30[0][0] \n", + "__________________________________________________________________________________________________\n", + "block8_sepconv1 (SeparableConv2 (None, None, None, 7 536536 block8_sepconv1_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block8_sepconv1_bn (BatchNormal (None, None, None, 7 2912 block8_sepconv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block8_sepconv2_act (Activation (None, None, None, 7 0 block8_sepconv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block8_sepconv2 (SeparableConv2 (None, None, None, 7 536536 block8_sepconv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block8_sepconv2_bn (BatchNormal (None, None, None, 7 2912 block8_sepconv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "block8_sepconv3_act (Activation (None, None, None, 7 0 block8_sepconv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block8_sepconv3 (SeparableConv2 (None, None, None, 7 536536 block8_sepconv3_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block8_sepconv3_bn (BatchNormal (None, None, None, 7 2912 block8_sepconv3[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_31 (Add) (None, None, None, 7 0 block8_sepconv3_bn[0][0] \n", + " add_30[0][0] \n", + "__________________________________________________________________________________________________\n", + "block9_sepconv1_act (Activation (None, None, None, 7 0 add_31[0][0] \n", + "__________________________________________________________________________________________________\n", + "block9_sepconv1 (SeparableConv2 (None, None, None, 7 536536 block9_sepconv1_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block9_sepconv1_bn (BatchNormal (None, None, None, 7 2912 block9_sepconv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block9_sepconv2_act (Activation (None, None, None, 7 0 block9_sepconv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block9_sepconv2 (SeparableConv2 (None, None, None, 7 536536 block9_sepconv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block9_sepconv2_bn (BatchNormal (None, None, None, 7 2912 block9_sepconv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "block9_sepconv3_act (Activation (None, None, None, 7 0 block9_sepconv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block9_sepconv3 (SeparableConv2 (None, None, None, 7 536536 block9_sepconv3_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block9_sepconv3_bn (BatchNormal (None, None, None, 7 2912 block9_sepconv3[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_32 (Add) (None, None, None, 7 0 block9_sepconv3_bn[0][0] \n", + " add_31[0][0] \n", + "__________________________________________________________________________________________________\n", + "block10_sepconv1_act (Activatio (None, None, None, 7 0 add_32[0][0] \n", + "__________________________________________________________________________________________________\n", + "block10_sepconv1 (SeparableConv (None, None, None, 7 536536 block10_sepconv1_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block10_sepconv1_bn (BatchNorma (None, None, None, 7 2912 block10_sepconv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block10_sepconv2_act (Activatio (None, None, None, 7 0 block10_sepconv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block10_sepconv2 (SeparableConv (None, None, None, 7 536536 block10_sepconv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block10_sepconv2_bn (BatchNorma (None, None, None, 7 2912 block10_sepconv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "block10_sepconv3_act (Activatio (None, None, None, 7 0 block10_sepconv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block10_sepconv3 (SeparableConv (None, None, None, 7 536536 block10_sepconv3_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block10_sepconv3_bn (BatchNorma (None, None, None, 7 2912 block10_sepconv3[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_33 (Add) (None, None, None, 7 0 block10_sepconv3_bn[0][0] \n", + " add_32[0][0] \n", + "__________________________________________________________________________________________________\n", + "block11_sepconv1_act (Activatio (None, None, None, 7 0 add_33[0][0] \n", + "__________________________________________________________________________________________________\n", + "block11_sepconv1 (SeparableConv (None, None, None, 7 536536 block11_sepconv1_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block11_sepconv1_bn (BatchNorma (None, None, None, 7 2912 block11_sepconv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block11_sepconv2_act (Activatio (None, None, None, 7 0 block11_sepconv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block11_sepconv2 (SeparableConv (None, None, None, 7 536536 block11_sepconv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block11_sepconv2_bn (BatchNorma (None, None, None, 7 2912 block11_sepconv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "block11_sepconv3_act (Activatio (None, None, None, 7 0 block11_sepconv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block11_sepconv3 (SeparableConv (None, None, None, 7 536536 block11_sepconv3_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block11_sepconv3_bn (BatchNorma (None, None, None, 7 2912 block11_sepconv3[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_34 (Add) (None, None, None, 7 0 block11_sepconv3_bn[0][0] \n", + " add_33[0][0] \n", + "__________________________________________________________________________________________________\n", + "block12_sepconv1_act (Activatio (None, None, None, 7 0 add_34[0][0] \n", + "__________________________________________________________________________________________________\n", + "block12_sepconv1 (SeparableConv (None, None, None, 7 536536 block12_sepconv1_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block12_sepconv1_bn (BatchNorma (None, None, None, 7 2912 block12_sepconv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block12_sepconv2_act (Activatio (None, None, None, 7 0 block12_sepconv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block12_sepconv2 (SeparableConv (None, None, None, 7 536536 block12_sepconv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block12_sepconv2_bn (BatchNorma (None, None, None, 7 2912 block12_sepconv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "block12_sepconv3_act (Activatio (None, None, None, 7 0 block12_sepconv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block12_sepconv3 (SeparableConv (None, None, None, 7 536536 block12_sepconv3_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block12_sepconv3_bn (BatchNorma (None, None, None, 7 2912 block12_sepconv3[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_35 (Add) (None, None, None, 7 0 block12_sepconv3_bn[0][0] \n", + " add_34[0][0] \n", + "__________________________________________________________________________________________________\n", + "block13_sepconv1_act (Activatio (None, None, None, 7 0 add_35[0][0] \n", + "__________________________________________________________________________________________________\n", + "block13_sepconv1 (SeparableConv (None, None, None, 7 536536 block13_sepconv1_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block13_sepconv1_bn (BatchNorma (None, None, None, 7 2912 block13_sepconv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block13_sepconv2_act (Activatio (None, None, None, 7 0 block13_sepconv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block13_sepconv2 (SeparableConv (None, None, None, 1 752024 block13_sepconv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block13_sepconv2_bn (BatchNorma (None, None, None, 1 4096 block13_sepconv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_12 (Conv2D) (None, None, None, 1 745472 add_35[0][0] \n", + "__________________________________________________________________________________________________\n", + "block13_pool (MaxPooling2D) (None, None, None, 1 0 block13_sepconv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_12 (BatchNo (None, None, None, 1 4096 conv2d_12[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_36 (Add) (None, None, None, 1 0 block13_pool[0][0] \n", + " batch_normalization_12[0][0] \n", + "__________________________________________________________________________________________________\n", + "block14_sepconv1 (SeparableConv (None, None, None, 1 1582080 add_36[0][0] \n", + "__________________________________________________________________________________________________\n", + "block14_sepconv1_bn (BatchNorma (None, None, None, 1 6144 block14_sepconv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block14_sepconv1_act (Activatio (None, None, None, 1 0 block14_sepconv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block14_sepconv2 (SeparableConv (None, None, None, 2 3159552 block14_sepconv1_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block14_sepconv2_bn (BatchNorma (None, None, None, 2 8192 block14_sepconv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "block14_sepconv2_act (Activatio (None, None, None, 2 0 block14_sepconv2_bn[0][0] \n", + "==================================================================================================\n", + "Total params: 20,861,480\n", + "Trainable params: 20,806,952\n", + "Non-trainable params: 54,528\n", + "__________________________________________________________________________________________________\n" + ] + } + ], + "source": [ + "base_model_w = Xception(include_top=False,weights='imagenet')\n", + "base_model_w.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tensor(\"block14_sepconv2_act_2/Relu:0\", shape=(?, ?, ?, 2048), dtype=float32)\n", + "Tensor(\"block14_sepconv2_act_2/Relu:0\", shape=(?, ?, ?, 2048), dtype=float32)\n", + "\n" + ] + } + ], + "source": [ + "x = base_model_w.output\n", + "print(x)\n", + "print (base_model_w.layers[-1].output)\n", + "print(base_model.layers[-2])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x = Dropout(0.5)(x)\n", + "logits = Dense(classes, kernel_regularizer=keras.regularizers.l2(weight_decay))(x)\n", + "probabilities = Activation('softmax')(logits)\n", + "\n", + "model = Model(model.input, probabilities, name='xception')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/__pycache__/mobilenet.cpython-35.pyc b/__pycache__/mobilenet.cpython-35.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ab950e5d948dd95646189ebce2f2d1007248aab2 GIT binary patch literal 1057 zcmY*X&2G~`5T12nJ9cb>Dp5qm33?z08mZ?BQ4xUFpr{$Y31Hpn>@ z9)=g-EqH;wa>^SZ4wPBDK(I5D+40ObGvCg39ES(Df4muV0KUV<A7tf!u+_0b;*(Ky)DQLeg!|T@Wth9wZ**J|sTmJxF?x2ap7ihmeFA=R)eiC&cVq z@ROmx#!7HJBqlg2r(CdOrt#n&l_?WL*Mx^N&CjUjWns3TiE>KC;Te-OV=v26@FLqE 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"/home/dongsung/Downloads/caltech-256-image-dataset/256_ObjectCategories/001.ak47\n", + "98\n", + "['001_0089.jpg', '001_0033.jpg']\n", + "['001_0001.jpg', '001_0002.jpg']\n" + ] + } + ], + "source": [ + "# image data file handling\n", + "\n", + "object_categories = listdir(data_dir)\n", + "object_categories.sort()\n", + "# for i in range(len(object_categories)):\n", + "# print (object_categories[i])\n", + "\n", + "category_dir = join(data_dir,object_categories[0])\n", + "print(category_dir)\n", + "\n", + "# store full path\n", + "onlyfiles = [f for f in listdir(category_dir) if isfile(join(category_dir, f))]\n", + "\n", + "print(len(onlyfiles))\n", + "print (onlyfiles[:2])\n", + "\n", + "onlyfiles.sort()\n", + "print (onlyfiles[:2])" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "# save data\n", + "saved_dir ='/home/dongsung/Downloads/caltech-256-image-dataset/temp/'" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "# slpit data (train,val)\n", + "# val has 20 images from each class of train dataset\n", + "def split_data(data_dir,saved_dir):\n", + " for i in range(len(object_categories)):\n", + " # dataset\n", + " category_dir = join(data_dir,object_categories[i])\n", + " # read files\n", + " onlyfiles = [f for f in listdir(category_dir) if isfile(join(category_dir, f))]\n", + " onlyfiles.sort()\n", + " \n", + " # copy traing_data\n", + " train_saved_dir=saved_dir+'train_no_resizing/'\n", + " for image_file in onlyfiles[:-20]:\n", + " shutil.copy(join(category_dir,image_file), join(train_saved_dir,image_file)) \n", + " \n", + " # copy validation data\n", + " \n", + " val_saved_dir = saved_dir+'val/'\n", + " for image_file in onlyfiles[-20:]:\n", + " shutil.copy(join(category_dir,image_file), join(val_saved_dir,image_file)) \n", + " \n", + "# break" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "split_data(data_dir,saved_dir)\n", + "#print(onlyfiles)\n", + "# print(onlyfiles[-20:])\n", + "# print(onlyfiles[:-20])\n", + "# val_saved_dir = saved_dir+'/val/'\n", + "# print (val_saved_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "25468 5139 30607\n", + "['001_0098.jpg', '002_0078.jpg', '002_0079.jpg']\n", + "[ 0 1 2 ... 25465 25466 25467]\n", + "[18026 21009 9244 ... 10397 2164 10072]\n", + "192_0026.jpg 192\n", + "228_0018.jpg 228\n", + "103_0084.jpg 103\n", + "009_0021.jpg 9\n", + "136_0066.jpg 136\n" + ] + } + ], + "source": [ + "# storing image as index and filename(including the path)\n", + "# data_set has index and filename\n", + "# label has index and lable\n", + "train_data=[]\n", + "train_labels=[]\n", + "val_data=[]\n", + "val_labels=[]\n", + "\n", + "train_saved_dir ='/home/dongsung/Downloads/caltech-256-image-dataset/temp/train_no_resizing'\n", + "val_saved_dir ='/home/dongsung/Downloads/caltech-256-image-dataset/temp/val'\n", + "\n", + "train_image_files = [f for f in listdir(train_saved_dir) if isfile(join(train_saved_dir, f))]\n", + "val_image_files = [f for f in listdir(val_saved_dir) if isfile(join(val_saved_dir, f))]\n", + "train_image_files.sort()\n", + "val_image_files.sort()\n", + "print(len(train_image_files), len(val_image_files), np.add(len(train_image_files),len(val_image_files)))\n", + "print(val_image_files[19:22])\n", + "\n", + "# parsing logics for getting a label from a file name\n", + "\n", + "# def readimages(data_dir,data_list,label_list):\n", + "# for i in range(len(object_categories)):\n", + "# # dataset\n", + "# category_dir = join(data_dir,object_categories[i])\n", + "# #print(category_dir)\n", + "# onlyfiles = [f for f in listdir(category_dir) if isfile(join(category_dir, f))]\n", + "# onlyfiles.sort()\n", + "# num_images_per_class = len(onlyfiles)\n", + "# print(i+1, num_images_per_class)\n", + "# # handling dataset\n", + "# for j in range(len(onlyfiles)):\n", + "# train_data.append(onlyfiles[j])\n", + "# train_labels.append(i+1)\n", + " \n", + "# handling data, label from\n", + "train_data_images = [f for f in listdir(train_saved_dir) if isfile(join(train_saved_dir, f))]\n", + "train_data_images.sort()\n", + "\n", + "for image_file in train_data_images:\n", + " train_data.append(image_file)\n", + " label = int(image_file.split('_')[0])\n", + " train_labels.append(label)\n", + " \n", + "val_data_images = [f for f in listdir(val_saved_dir) if isfile(join(val_saved_dir, f))]\n", + "val_data_images.sort()\n", + " \n", + "for image_file in val_data_images:\n", + " val_data.append(image_file)\n", + " label = int(image_file.split('_')[0])\n", + " val_labels.append(label) \n", + "\n", + "\n", + "arr_indexes = np.arange(len(train_data))\n", + "print(arr_indexes)\n", + "# shuffle\n", + "np.random.shuffle(arr_indexes)\n", + "print(arr_indexes)\n", + "\n", + "for i in arr_indexes[0:5]:\n", + " print(train_data[i],train_labels[i])\n", + "\n", + "\n", + "# index handlig for data_provider\n", + "# batch_x = np.zeros((current_batch_size,) + self.image_shape, dtype=K.floatx())\n", + "# batch_logits = np.zeros((current_batch_size, self.num_class), dtype=K.floatx())\n", + "# grayscale = self.color_mode == 'grayscale'\n", + "# # build batch of image data\n", + "# for i, j in enumerate(index_array):\n", + "# fname = self.filenames[j]\n", + "# img = load_img(os.path.join(self.directory, fname),\n", + "# grayscale=grayscale,\n", + "# target_size=self.target_size)\n", + "# x = img_to_array(img, data_format=self.data_format)\n", + "# x = self.image_data_generator.random_transform(x)\n", + "# x = self.image_data_generator.standardize(x)\n", + "# batch_x[i] = x\n", + " batch_logits[i] = self.logits_dict[fname]\n", + " \n", + " \n", + " for i, label in enumerate(self.classes[index_array]):\n", + " batch_y[i, label] = 1." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def data_generator(data, labels, batch_size=32, input_shape=(299,299,3),n_classes=10, shuffle=True):\n", + " \n", + " num_data = len(data)\n", + " arr_indexes = np.arange(num_data)\n", + " \n", + " # shuffle\n", + " if shuffle:\n", + " np.random.shuffle(arr_indexes)\n", + " \n", + " num_batch = num_data/batch_size\n", + " if (num_data/batch_size) > 0:\n", + " num_batch = num_batch+1\n", + " \n", + " batch_x = np.zeros((batch_size,)+ input_shape)\n", + " batch_y = np.zeros((batch_size,)+ n_classes)\n", + " \n", + " offset=0\n", + " end =0\n", + " \n", + " for i in range(num_batch):\n", + " \n", + " offset=end\n", + " end=batch_size+offsest\n", + " if end > num_data:\n", + " end = num_data\n", + "\n", + " batch_arr_indexes = arr_indexes[offset:end]\n", + " \n", + " # preparing the batch and yield batches\n", + " for i, idx in enumerate(batch_arr_indexes):\n", + " fname = data[idx]\n", + " # image load from index\n", + " \n", + " # data resizing\n", + " # img = load_img(os.path.join(self.directory, fname),\n", + " # grayscale=grayscale,\n", + " # target_size=self.target_size)\n", + "\n", + " label = labes[idx]\n", + " batch_y[i, label]= 1\n", + "\n", + " yield batch_x, batch_y" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0 1 2 3 4 5 6 7 8 9]\n", + "[7 1 9 6 2 5 0 3 4 8]\n", + "(32, 299, 299, 3)\n" + ] + } + ], + "source": [ + "indexes = np.arange(10)\n", + "print(indexes)\n", + "np.random.shuffle(indexes)\n", + "print(indexes)\n", + "test = np.zeros((32,)+(299,299,3))\n", + "print(np.shape(test))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['001_0071.jpg', '001_0072.jpg', '001_0073.jpg', '001_0074.jpg', '001_0075.jpg', '001_0076.jpg', '001_0077.jpg', '001_0078.jpg', '002_0001.jpg', '002_0002.jpg', '002_0003.jpg', '002_0004.jpg', '002_0005.jpg', '002_0006.jpg', '002_0007.jpg', '002_0008.jpg', '002_0009.jpg', '002_0010.jpg', '002_0011.jpg', '002_0012.jpg', '002_0013.jpg', '002_0014.jpg', '002_0015.jpg', '002_0016.jpg', '002_0017.jpg', '002_0018.jpg', '002_0019.jpg', '002_0020.jpg', '002_0021.jpg', '002_0022.jpg', '002_0023.jpg', '002_0024.jpg'] [1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]\n" + ] + } + ], + "source": [ + "# readimages(data_dir)\n", + "# print(len(train_data), len(train_labels))\n", + "# for i in range(90,110):\n", + "# print (train_data[i],train_labels[i] )\n", + "\n", + "# M = pd.DataFrame(train_metadata)\n", + "# M.columns = ['directory', 'img_name', 'height', 'width', 'channels', 'byte_size', 'bit_depth']\n", + "\n", + "# M['category_name'] = M.directory.apply(lambda x: x.split('.')[-1].lower())\n", + "# M['img_extension'] = M.img_name.apply(lambda x: x.split('.')[-1])\n", + "# M['category_number'] = M.directory.apply(lambda x: int(x.split('.')[0]))\n", + "\n", + "# # remove '101' from some category names\n", + "# M.category_name = M.category_name.apply(lambda x: x[:-4] if '101' in x else \n", + "print(train_data[70:102],train_labels[70:102])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def readimages():\n", + "\t'''\n", + "\tGoes through every image and gets the image, putting them into an array\n", + "\tArgs:\n", + "\t\tNone\n", + "\tReturns:\n", + "\t\tdata: array of information including:\n", + "\t\t\timage: string\n", + "\t\t\tshape: int \n", + "\t\t\tlabel: index from 0 to 256 for the classes\n", + "\t'''\n", + "\tdata = []\n", + "\tj = 0\n", + "\tobject_dir = os.path.join(main_dir, '256_ObjectCategories')\n", + "\tobjectCategories = os.listdir(object_dir)\n", + "\tfor i in range(len(objectCategories)):\n", + "\t\tlabel = i\n", + "\t\texamp_dir = os.path.join(object_dir, objectCategories[i])\n", + "\t\texamples = os.listdir(examp_dir)\n", + "\t\tprint 'compiling data: %d/%d'%(i+1, len(objectCategories))\n", + "\t\tfor examp in examples:\n", + "\t\t\timage_dir = os.path.join(examp_dir, examp)\n", + "\t\t\timage, shape = get_image(image_dir)\n", + "\t\t\ttry:\n", + "\t\t\t\tdata[j]\n", + "\t\t\texcept IndexError:\n", + "\t\t\t\tdata.append([])\n", + "\n", + "\t\t\tdata[j].append([image, shape, label])\n", + "\t\t\tj = (j+1)%num_tffile\n", + "\treturn data\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/fine_tuning_with_keras.py b/fine_tuning_with_keras.py new file mode 100644 index 0000000..e69de29 diff --git a/get_logits_from_xception.ipynb b/get_logits_from_xception.ipynb new file mode 100644 index 0000000..7c9da67 --- /dev/null +++ b/get_logits_from_xception.ipynb @@ -0,0 +1,215 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "from tqdm import tqdm\n", + "import sys\n", + "sys.path.append('utils/')\n", + "\n", + "# use non standard flow_from_directory\n", + "from image_preprocessing_ver1 import ImageDataGenerator\n", + "# it outputs not only x_batch and y_batch but also image names\n", + "\n", + "from keras.models import Model\n", + "from xception import Xception, preprocess_input" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "data_dir = '/home/dongsung/study/machine_learning/distillation/data'" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 0 images belonging to 0 classes.\n", + "Found 0 images belonging to 0 classes.\n" + ] + } + ], + "source": [ + "data_generator = ImageDataGenerator(\n", + " data_format='channels_last',\n", + " preprocessing_function=preprocess_input\n", + ")\n", + "\n", + "train_generator = data_generator.flow_from_directory(\n", + " data_dir + 'train', \n", + " target_size=(299, 299),\n", + " batch_size=64, shuffle=False\n", + ")\n", + "\n", + "val_generator = data_generator.flow_from_directory(\n", + " data_dir + 'val', \n", + " target_size=(299, 299),\n", + " batch_size=64, shuffle=False\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Get model and remove the last layer" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "model = Xception()\n", + "model.load_weights('xception_weights.hdf5')\n", + "# remove softmax\n", + "model.layers.pop()\n", + "model = Model(model.input, model.layers[-1].output)\n", + "# now model outputs logits" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Save logits as a dict: image name -> logit (256 dimensional vector)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "399it [04:04, 1.63it/s]" + ] + } + ], + "source": [ + "batches = 0\n", + "train_logits = {}\n", + "\n", + "for x_batch, _, name_batch in tqdm(train_generator):\n", + " \n", + " batch_logits = model.predict_on_batch(x_batch)\n", + " \n", + " for i, n in enumerate(name_batch):\n", + " train_logits[n] = batch_logits[i]\n", + " \n", + " batches += 1\n", + " if batches >= 400: # 25600/64\n", + " break" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "0it [00:00, ?it/s]\u001b[A\n", + "1it [00:00, 1.63it/s]\u001b[A\n", + "2it [00:01, 1.63it/s]\u001b[A\n", + "3it [00:01, 1.63it/s]\u001b[A\n", + "4it [00:02, 1.63it/s]\u001b[A\n", + "5it [00:03, 1.63it/s]\u001b[A\n", + "6it [00:03, 1.63it/s]\u001b[A\n", + "7it [00:04, 1.63it/s]\u001b[A\n", + "79it [00:48, 1.63it/s]" + ] + } + ], + "source": [ + "batches = 0\n", + "val_logits = {}\n", + "\n", + "for x_batch, _, name_batch in tqdm(val_generator):\n", + " \n", + " batch_logits = model.predict_on_batch(x_batch)\n", + " \n", + " for i, n in enumerate(name_batch):\n", + " val_logits[n] = batch_logits[i]\n", + " \n", + " batches += 1\n", + " if batches >= 80: # 5120/64\n", + " break" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "np.save(data_dir + 'train_logits.npy', train_logits)\n", + "np.save(data_dir + 'val_logits.npy', val_logits)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/keras_model_practice.ipynb b/keras_model_practice.ipynb new file mode 100644 index 0000000..0a8b551 --- /dev/null +++ b/keras_model_practice.ipynb @@ -0,0 +1,350 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "import keras\n", + "from keras.models import Model\n", + "\n", + "from keras.models import Sequential\n", + "from keras.layers import Dense, Activation" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "dense_1 (Dense) (None, 2) 8 \n", + "_________________________________________________________________\n", + "dense_2 (Dense) (None, 3) 9 \n", + "=================================================================\n", + "Total params: 17\n", + "Trainable params: 17\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n", + "[array([[ 0.5467241 , -0.57646537],\n", + " [-0.88949263, 0.03872693],\n", + " [ 0.56926394, -0.27453983]], dtype=float32), array([0., 0.], dtype=float32)]\n", + "[array([[ 0.7123369 , 1.0482664 , 0.25098026],\n", + " [-0.6575545 , 0.29635358, 0.00701773]], dtype=float32), array([0., 0., 0.], dtype=float32)]\n" + ] + } + ], + "source": [ + "model = Sequential()\n", + "model.add(Dense(2, input_dim=3, name='dense_1'))\n", + "model.add(Dense(3, name='dense_2'))\n", + "fname ='./test_weight.h5'\n", + "model.save_weights(fname)\n", + "\n", + "# model summary\n", + "model.summary()\n", + "\n", + "for layer in model.layers:\n", + "# print( model.get_layer(layer_name))\n", + " print(layer.get_weights())" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[array([[ 0.3634112 , -0.03811157],\n", + " [ 0.97057843, -0.46573573],\n", + " [-0.5910582 , 0.11941993]], dtype=float32), array([0., 0.], dtype=float32)]\n", + "[array([[-0.63099915, 0.02219301, 0.49612063, -0.3468259 , 0.01588768,\n", + " 0.26914436, -0.10066926, 0.20100826, -0.50348556, -0.03977805],\n", + " [-0.5218549 , -0.60437787, -0.35720903, 0.56756467, -0.20298344,\n", + " -0.00881898, -0.20957452, -0.01430649, 0.5764455 , -0.32512668]],\n", + " dtype=float32), array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)]\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "dense_1 (Dense) (None, 2) 8 \n", + "_________________________________________________________________\n", + "new_dense (Dense) (None, 10) 30 \n", + "=================================================================\n", + "Total params: 38\n", + "Trainable params: 38\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n", + "[array([[ 0.5467241 , -0.57646537],\n", + " [-0.88949263, 0.03872693],\n", + " [ 0.56926394, -0.27453983]], dtype=float32), array([0., 0.], dtype=float32)]\n" + ] + } + ], + "source": [ + "# new model\n", + "model = Sequential()\n", + "model.add(Dense(2, input_dim=3, name='dense_1')) # will be loaded\n", + "model.add(Dense(10, name='new_dense', activation='softmax')) # will not be loaded\n", + "\n", + "for layer in model.layers:\n", + "# print( model.get_layer(layer_name))\n", + " print(layer.get_weights())\n", + "\n", + "# load weights from first model; will only affect the first layer, dense_1.\n", + "model.load_weights(fname, by_name=True)\n", + "\n", + "# model summary\n", + "model.summary()\n", + "\n", + "for layer in model.layers[:-1]:\n", + "# print( model.get_layer(layer_name))\n", + " print(layer.get_weights())" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[array([[ 0.2649634 , -0.21551806],\n", + " [ 1.0733602 , -0.10063785],\n", + " [ 0.28879964, 0.7637768 ]], dtype=float32), array([0., 0.], dtype=float32)]\n", + "[array([[ 0.34625965, 0.3822468 , -0.43370456, -0.15196651, -0.465474 ,\n", + " 0.3951307 , -0.42655745, 0.5202661 , 0.00820518, 0.55745953],\n", + " [ 0.60681754, -0.6765404 , -0.20293522, 0.35511118, 0.2578935 ,\n", + " 0.35924274, 0.46151727, 0.70193976, -0.08491611, 0.02645051]],\n", + " dtype=float32), array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)]\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "dense_1 (Dense) (None, 2) 8 \n", + "_________________________________________________________________\n", + "new_dense (Dense) (None, 10) 30 \n", + "=================================================================\n", + "Total params: 38\n", + "Trainable params: 38\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n", + "[array([[ 0.5467241 , -0.57646537],\n", + " [-0.88949263, 0.03872693],\n", + " [ 0.56926394, -0.27453983]], dtype=float32), array([0., 0.], dtype=float32)]\n" + ] + } + ], + "source": [ + "# new model\n", + "model = Sequential()\n", + "model.add(Dense(2, input_dim=3, name='dense_1')) # will be loaded\n", + "model.add(Dense(10, name='new_dense')) # will not be loaded\n", + "\n", + "for layer in model.layers:\n", + "# print( model.get_layer(layer_name))\n", + " print(layer.get_weights())\n", + "\n", + "# load weights from first model; will only affect the first layer, dense_1.\n", + "model.load_weights(fname, by_name=True)\n", + "\n", + "# model summary\n", + "model.summary()\n", + "\n", + "for layer in model.layers[:-1]:\n", + "# print( model.get_layer(layer_name))\n", + " print(layer.get_weights())" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "new_dense\n", + "Tensor(\"dense_1_2/BiasAdd:0\", shape=(?, 2), dtype=float32)\n", + "(None, 2)\n", + "Tensor(\"new_dense_1/Softmax:0\", shape=(?, 10), dtype=float32)\n", + "(None, 10)\n", + "Tensor(\"new_dense_1/Softmax:0\", shape=(?, 10), dtype=float32)\n" + ] + } + ], + "source": [ + "print(model.layers[-1].name) # no matrix multiple?\n", + "print(model.layers[-1].input)\n", + "print(model.layers[-1].input_shape)\n", + "print(model.layers[-1].output)\n", + "print(model.layers[-1].output_shape)\n", + "print(model.output) # the same as the output of the last layer" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0.5467241 -0.57646537]\n", + " [-0.88949263 0.03872693]\n", + " [ 0.56926394 -0.27453983]]\n", + "[0. 0.]\n" + ] + } + ], + "source": [ + "weights = model.layers[0].get_weights()[0]\n", + "print(weights)\n", + "biases = model.layers[0].get_weights()[1]\n", + "print(biases)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tensor(\"dense_1_2/BiasAdd:0\", shape=(?, 2), dtype=float32)\n" + ] + } + ], + "source": [ + "model.pop()\n", + "print(model.output)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tensor(\"dense_1_2/BiasAdd:0\", shape=(?, 2), dtype=float32)\n", + "dense_1\n" + ] + } + ], + "source": [ + "print(model.layers[-1].output)\n", + "print(model.layers[-1].name)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.4/xception_weights_tf_dim_ordering_tf_kernels_notop.h5\n" + ] + }, + { + "ename": "Exception", + "evalue": "URL fetch failure on https://github.com/fchollet/deep-learning-models/releases/download/v0.4/xception_weights_tf_dim_ordering_tf_kernels_notop.h5: None -- [Errno -2] Name or service not known", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mgaierror\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/usr/lib/python3.5/urllib/request.py\u001b[0m in \u001b[0;36mdo_open\u001b[0;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[1;32m 1253\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1254\u001b[0;31m \u001b[0mh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mselector\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1255\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# timeout error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/lib/python3.5/http/client.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, body, headers)\u001b[0m\n\u001b[1;32m 1105\u001b[0m \u001b[0;34m\"\"\"Send a complete request to the server.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1106\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1107\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/lib/python3.5/http/client.py\u001b[0m in \u001b[0;36m_send_request\u001b[0;34m(self, method, url, body, headers)\u001b[0m\n\u001b[1;32m 1150\u001b[0m \u001b[0mbody\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_encode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'body'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1151\u001b[0;31m 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file_hash, cache_subdir, hash_algorithm, extract, archive_format, cache_dir)\u001b[0m\n\u001b[1;32m 221\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 222\u001b[0;31m \u001b[0murlretrieve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0morigin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdl_progress\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 223\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mHTTPError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/lib/python3.5/urllib/request.py\u001b[0m in \u001b[0;36murlretrieve\u001b[0;34m(url, filename, reporthook, data)\u001b[0m\n\u001b[1;32m 187\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 188\u001b[0;31m \u001b[0;32mwith\u001b[0m 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data)\u001b[0m\n\u001b[1;32m 483\u001b[0m result = self._call_chain(self.handle_open, protocol, protocol +\n\u001b[0;32m--> 484\u001b[0;31m '_open', req)\n\u001b[0m\u001b[1;32m 485\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/lib/python3.5/urllib/request.py\u001b[0m in \u001b[0;36m_call_chain\u001b[0;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[1;32m 443\u001b[0m \u001b[0mfunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandler\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmeth_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 444\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 445\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/lib/python3.5/urllib/request.py\u001b[0m in \u001b[0;36mhttps_open\u001b[0;34m(self, req)\u001b[0m\n\u001b[1;32m 1296\u001b[0m return self.do_open(http.client.HTTPSConnection, req,\n\u001b[0;32m-> 1297\u001b[0;31m context=self._context, check_hostname=self._check_hostname)\n\u001b[0m\u001b[1;32m 1298\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/lib/python3.5/urllib/request.py\u001b[0m in \u001b[0;36mdo_open\u001b[0;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[1;32m 1255\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# timeout error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1256\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mURLError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1257\u001b[0m \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mURLError\u001b[0m: ", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mException\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mxception\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mXception\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreprocess_input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m 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\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_weights\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweights_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/tensorflow_virtualenv/lib/python3.5/site-packages/keras/utils/data_utils.py\u001b[0m in \u001b[0;36mget_file\u001b[0;34m(fname, origin, untar, md5_hash, file_hash, cache_subdir, hash_algorithm, extract, archive_format, cache_dir)\u001b[0m\n\u001b[1;32m 224\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merror_msg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0morigin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 225\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mURLError\u001b[0m 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\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mException\u001b[0m: URL fetch failure on https://github.com/fchollet/deep-learning-models/releases/download/v0.4/xception_weights_tf_dim_ordering_tf_kernels_notop.h5: None -- [Errno -2] Name or service not known" + ] + } + ], + "source": [ + "from xception import Xception, preprocess_input\n", + "model = Xception(weights='imagenet')\n", + "\n", + "model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/knowledge_distillation_for_mobilenet.ipynb b/knowledge_distillation_for_mobilenet.ipynb new file mode 100644 index 0000000..c39f9c7 --- /dev/null +++ b/knowledge_distillation_for_mobilenet.ipynb @@ -0,0 +1,726 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import sys\n", + "sys.path.append('utils/')\n", + "\n", + "import keras\n", + "from keras import optimizers\n", + "from keras.callbacks import ReduceLROnPlateau, EarlyStopping\n", + "\n", + "# use non standard flow_from_directory\n", + "from image_preprocessing_ver2 import ImageDataGenerator\n", + "# it outputs y_batch that contains onehot targets and logits\n", + "# logits came from xception\n", + "\n", + "from keras.models import Model\n", + "from keras.layers import Lambda, concatenate, Activation\n", + "from keras.losses import categorical_crossentropy as logloss\n", + "from keras.metrics import categorical_accuracy, top_k_categorical_accuracy\n", + "from keras import backend as K\n", + "\n", + "from mobilenet import get_mobilenet\n", + "from keras.applications.mobilenet import preprocess_input\n", + "\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "data_dir = '/home/dongsung/study/machine_learning/distillation/data'" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: '/home/dongsung/study/machine_learning/distillation/datatrain_logits.npy'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain_logits\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_dir\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'train_logits.npy'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mval_logits\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_dir\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'val_logits.npy'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/tensorflow_virtualenv/lib/python3.5/site-packages/numpy/lib/npyio.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(file, mmap_mode, allow_pickle, fix_imports, encoding)\u001b[0m\n\u001b[1;32m 382\u001b[0m \u001b[0mown_fid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 383\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbasestring\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 384\u001b[0;31m \u001b[0mfid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"rb\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 385\u001b[0m \u001b[0mown_fid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 386\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mis_pathlib_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/home/dongsung/study/machine_learning/distillation/datatrain_logits.npy'" + ] + } + ], + "source": [ + "train_logits = np.load(data_dir + 'train_logits.npy')[()]\n", + "val_logits = np.load(data_dir + 'val_logits.npy')[()]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 25600 images belonging to 256 classes.\n", + "Found 5120 images belonging to 256 classes.\n" + ] + } + ], + "source": [ + "data_generator = ImageDataGenerator(\n", + " data_format='channels_last',\n", + " preprocessing_function=preprocess_input\n", + ")\n", + "\n", + "# note: i'm also passing dicts of logits\n", + "train_generator = data_generator.flow_from_directory(\n", + " data_dir + 'train', train_logits,\n", + " target_size=(224, 224),\n", + " batch_size=64\n", + ")\n", + "\n", + "val_generator = data_generator.flow_from_directory(\n", + " data_dir + 'val', val_logits,\n", + " target_size=(224, 224),\n", + " batch_size=64\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Create model" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "temperature = 5.0" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "model = get_mobilenet(224, alpha=0.25, weight_decay=1e-5, dropout=0.1)\n", + "\n", + "# remove softmax\n", + "model.layers.pop()\n", + "\n", + "# usual probabilities\n", + "logits = model.layers[-1].output\n", + "probabilities = Activation('softmax')(logits)\n", + "\n", + "# softed probabilities\n", + "logits_T = Lambda(lambda x: x/temperature)(logits)\n", + "probabilities_T = Activation('softmax')(logits_T)\n", + "\n", + "output = concatenate([probabilities, probabilities_T])\n", + "model = Model(model.input, output)\n", + "# now model outputs 512 dimensional vectors" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "__________________________________________________________________________________________________\n", + "Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + "input_1 (InputLayer) (None, 224, 224, 3) 0 \n", + "__________________________________________________________________________________________________\n", + "conv1_pad (ZeroPadding2D) (None, 225, 225, 3) 0 input_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1 (Conv2D) (None, 112, 112, 8) 216 conv1_pad[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1_bn (BatchNormalization) (None, 112, 112, 8) 32 conv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1_relu (ReLU) (None, 112, 112, 8) 0 conv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_dw_1 (DepthwiseConv2D) (None, 112, 112, 8) 72 conv1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_dw_1_bn (BatchNormalizatio (None, 112, 112, 8) 32 conv_dw_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_dw_1_relu (ReLU) (None, 112, 112, 8) 0 conv_dw_1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_pw_1 (Conv2D) (None, 112, 112, 16) 128 conv_dw_1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_pw_1_bn (BatchNormalizatio (None, 112, 112, 16) 64 conv_pw_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_pw_1_relu (ReLU) (None, 112, 112, 16) 0 conv_pw_1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_pad_2 (ZeroPadding2D) (None, 113, 113, 16) 0 conv_pw_1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_dw_2 (DepthwiseConv2D) (None, 56, 56, 16) 144 conv_pad_2[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_dw_2_bn (BatchNormalizatio (None, 56, 56, 16) 64 conv_dw_2[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_dw_2_relu (ReLU) (None, 56, 56, 16) 0 conv_dw_2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_pw_2 (Conv2D) (None, 56, 56, 32) 512 conv_dw_2_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_pw_2_bn (BatchNormalizatio (None, 56, 56, 32) 128 conv_pw_2[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_pw_2_relu (ReLU) (None, 56, 56, 32) 0 conv_pw_2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_dw_3 (DepthwiseConv2D) (None, 56, 56, 32) 288 conv_pw_2_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_dw_3_bn (BatchNormalizatio (None, 56, 56, 32) 128 conv_dw_3[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_dw_3_relu (ReLU) (None, 56, 56, 32) 0 conv_dw_3_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_pw_3 (Conv2D) (None, 56, 56, 32) 1024 conv_dw_3_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_pw_3_bn (BatchNormalizatio (None, 56, 56, 32) 128 conv_pw_3[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_pw_3_relu (ReLU) (None, 56, 56, 32) 0 conv_pw_3_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_pad_4 (ZeroPadding2D) (None, 57, 57, 32) 0 conv_pw_3_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_dw_4 (DepthwiseConv2D) (None, 28, 28, 32) 288 conv_pad_4[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_dw_4_bn (BatchNormalizatio (None, 28, 28, 32) 128 conv_dw_4[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_dw_4_relu (ReLU) (None, 28, 28, 32) 0 conv_dw_4_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_pw_4 (Conv2D) (None, 28, 28, 64) 2048 conv_dw_4_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_pw_4_bn (BatchNormalizatio (None, 28, 28, 64) 256 conv_pw_4[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_pw_4_relu (ReLU) (None, 28, 28, 64) 0 conv_pw_4_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_dw_5 (DepthwiseConv2D) (None, 28, 28, 64) 576 conv_pw_4_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_dw_5_bn (BatchNormalizatio (None, 28, 28, 64) 256 conv_dw_5[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_dw_5_relu (ReLU) (None, 28, 28, 64) 0 conv_dw_5_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_pw_5 (Conv2D) (None, 28, 28, 64) 4096 conv_dw_5_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_pw_5_bn (BatchNormalizatio (None, 28, 28, 64) 256 conv_pw_5[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_pw_5_relu (ReLU) (None, 28, 28, 64) 0 conv_pw_5_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_pad_6 (ZeroPadding2D) (None, 29, 29, 64) 0 conv_pw_5_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_dw_6 (DepthwiseConv2D) (None, 14, 14, 64) 576 conv_pad_6[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_dw_6_bn (BatchNormalizatio (None, 14, 14, 64) 256 conv_dw_6[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_dw_6_relu (ReLU) (None, 14, 14, 64) 0 conv_dw_6_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_pw_6 (Conv2D) (None, 14, 14, 128) 8192 conv_dw_6_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_pw_6_bn (BatchNormalizatio (None, 14, 14, 128) 512 conv_pw_6[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_pw_6_relu (ReLU) (None, 14, 14, 128) 0 conv_pw_6_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_dw_7 (DepthwiseConv2D) (None, 14, 14, 128) 1152 conv_pw_6_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_dw_7_bn (BatchNormalizatio (None, 14, 14, 128) 512 conv_dw_7[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_dw_7_relu (ReLU) (None, 14, 14, 128) 0 conv_dw_7_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_pw_7 (Conv2D) (None, 14, 14, 128) 16384 conv_dw_7_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_pw_7_bn (BatchNormalizatio (None, 14, 14, 128) 512 conv_pw_7[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_pw_7_relu (ReLU) (None, 14, 14, 128) 0 conv_pw_7_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_dw_8 (DepthwiseConv2D) (None, 14, 14, 128) 1152 conv_pw_7_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_dw_8_bn (BatchNormalizatio (None, 14, 14, 128) 512 conv_dw_8[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_dw_8_relu (ReLU) (None, 14, 14, 128) 0 conv_dw_8_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_pw_8 (Conv2D) (None, 14, 14, 128) 16384 conv_dw_8_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_pw_8_bn (BatchNormalizatio (None, 14, 14, 128) 512 conv_pw_8[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_pw_8_relu (ReLU) (None, 14, 14, 128) 0 conv_pw_8_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_dw_9 (DepthwiseConv2D) (None, 14, 14, 128) 1152 conv_pw_8_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_dw_9_bn (BatchNormalizatio (None, 14, 14, 128) 512 conv_dw_9[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_dw_9_relu (ReLU) (None, 14, 14, 128) 0 conv_dw_9_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_pw_9 (Conv2D) (None, 14, 14, 128) 16384 conv_dw_9_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_pw_9_bn (BatchNormalizatio (None, 14, 14, 128) 512 conv_pw_9[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_pw_9_relu (ReLU) (None, 14, 14, 128) 0 conv_pw_9_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_dw_10 (DepthwiseConv2D) (None, 14, 14, 128) 1152 conv_pw_9_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_dw_10_bn (BatchNormalizati (None, 14, 14, 128) 512 conv_dw_10[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_dw_10_relu (ReLU) (None, 14, 14, 128) 0 conv_dw_10_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_pw_10 (Conv2D) (None, 14, 14, 128) 16384 conv_dw_10_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_pw_10_bn (BatchNormalizati (None, 14, 14, 128) 512 conv_pw_10[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_pw_10_relu (ReLU) (None, 14, 14, 128) 0 conv_pw_10_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_dw_11 (DepthwiseConv2D) (None, 14, 14, 128) 1152 conv_pw_10_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_dw_11_bn (BatchNormalizati (None, 14, 14, 128) 512 conv_dw_11[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_dw_11_relu (ReLU) (None, 14, 14, 128) 0 conv_dw_11_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_pw_11 (Conv2D) (None, 14, 14, 128) 16384 conv_dw_11_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_pw_11_bn (BatchNormalizati (None, 14, 14, 128) 512 conv_pw_11[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_pw_11_relu (ReLU) (None, 14, 14, 128) 0 conv_pw_11_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_pad_12 (ZeroPadding2D) (None, 15, 15, 128) 0 conv_pw_11_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_dw_12 (DepthwiseConv2D) (None, 7, 7, 128) 1152 conv_pad_12[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_dw_12_bn (BatchNormalizati (None, 7, 7, 128) 512 conv_dw_12[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_dw_12_relu (ReLU) (None, 7, 7, 128) 0 conv_dw_12_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_pw_12 (Conv2D) (None, 7, 7, 256) 32768 conv_dw_12_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_pw_12_bn (BatchNormalizati (None, 7, 7, 256) 1024 conv_pw_12[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_pw_12_relu (ReLU) (None, 7, 7, 256) 0 conv_pw_12_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_dw_13 (DepthwiseConv2D) (None, 7, 7, 256) 2304 conv_pw_12_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_dw_13_bn (BatchNormalizati (None, 7, 7, 256) 1024 conv_dw_13[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_dw_13_relu (ReLU) (None, 7, 7, 256) 0 conv_dw_13_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_pw_13 (Conv2D) (None, 7, 7, 256) 65536 conv_dw_13_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_pw_13_bn (BatchNormalizati (None, 7, 7, 256) 1024 conv_pw_13[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv_pw_13_relu (ReLU) (None, 7, 7, 256) 0 conv_pw_13_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "global_average_pooling2d_1 (Glo (None, 256) 0 conv_pw_13_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_1 (Dropout) (None, 256) 0 global_average_pooling2d_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_1 (Dense) (None, 256) 65792 dropout_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "lambda_1 (Lambda) (None, 256) 0 dense_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "activation_2 (Activation) (None, 256) 0 dense_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "activation_3 (Activation) (None, 256) 0 lambda_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_1 (Concatenate) (None, 512) 0 activation_2[0][0] \n", + " activation_3[0][0] \n", + "==================================================================================================\n", + "Total params: 284,336\n", + "Trainable params: 65,792\n", + "Non-trainable params: 218,544\n", + "__________________________________________________________________________________________________\n" + ] + } + ], + "source": [ + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Create custom loss" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def knowledge_distillation_loss(y_true, y_pred, lambda_const): \n", + " \n", + " # split in \n", + " # onehot hard true targets\n", + " # logits from xception\n", + " y_true, logits = y_true[:, :256], y_true[:, 256:]\n", + " \n", + " # convert logits to soft targets\n", + " y_soft = K.softmax(logits/temperature)\n", + " \n", + " # split in \n", + " # usual output probabilities\n", + " # probabilities made softer with temperature\n", + " y_pred, y_pred_soft = y_pred[:, :256], y_pred[:, 256:] \n", + " \n", + " return lambda_const*logloss(y_true, y_pred) + logloss(y_soft, y_pred_soft)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# For testing use usual output probabilities (without temperature)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def accuracy(y_true, y_pred):\n", + " y_true = y_true[:, :256]\n", + " y_pred = y_pred[:, :256]\n", + " return categorical_accuracy(y_true, y_pred)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def top_5_accuracy(y_true, y_pred):\n", + " y_true = y_true[:, :256]\n", + " y_pred = y_pred[:, :256]\n", + " return top_k_categorical_accuracy(y_true, y_pred)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def categorical_crossentropy(y_true, y_pred):\n", + " y_true = y_true[:, :256]\n", + " y_pred = y_pred[:, :256]\n", + " return logloss(y_true, y_pred)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# logloss with only soft probabilities and targets\n", + "def soft_logloss(y_true, y_pred): \n", + " logits = y_true[:, 256:]\n", + " y_soft = K.softmax(logits/temperature)\n", + " y_pred_soft = y_pred[:, 256:] \n", + " return logloss(y_soft, y_pred_soft)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Train" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "lambda_const = 0.07\n", + "\n", + "model.compile(\n", + " optimizer=optimizers.SGD(lr=1e-1, momentum=0.9, nesterov=True), \n", + " loss=lambda y_true, y_pred: knowledge_distillation_loss(y_true, y_pred, lambda_const), \n", + " metrics=[accuracy, top_5_accuracy, categorical_crossentropy, soft_logloss]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/30\n", + "400/400 [==============================] - 24s - loss: 5.8086 - accuracy: 0.2173 - top_5_accuracy: 0.4171 - categorical_crossentropy: 4.0499 - soft_logloss: 5.5225 - val_loss: 5.6917 - val_accuracy: 0.4475 - val_top_5_accuracy: 0.7285 - val_categorical_crossentropy: 2.6774 - val_soft_logloss: 5.5013\n", + "Epoch 2/30\n", + "400/400 [==============================] - 23s - loss: 5.6726 - accuracy: 0.4953 - top_5_accuracy: 0.7514 - categorical_crossentropy: 2.4414 - soft_logloss: 5.4984 - val_loss: 5.6416 - val_accuracy: 0.5660 - val_top_5_accuracy: 0.8070 - val_categorical_crossentropy: 2.0523 - val_soft_logloss: 5.4944\n", + "Epoch 3/30\n", + "400/400 [==============================] - 24s - loss: 5.6378 - accuracy: 0.5785 - top_5_accuracy: 0.8072 - categorical_crossentropy: 2.0036 - soft_logloss: 5.4938 - val_loss: 5.6240 - val_accuracy: 0.5951 - val_top_5_accuracy: 0.8303 - val_categorical_crossentropy: 1.8349 - val_soft_logloss: 5.4916\n", + "Epoch 4/30\n", + "400/400 [==============================] - 23s - loss: 5.6212 - accuracy: 0.6181 - top_5_accuracy: 0.8376 - categorical_crossentropy: 1.7899 - soft_logloss: 5.4917 - val_loss: 5.6167 - val_accuracy: 0.6207 - val_top_5_accuracy: 0.8410 - val_categorical_crossentropy: 1.7269 - val_soft_logloss: 5.4914\n", + "Epoch 5/30\n", + "400/400 [==============================] - 23s - loss: 5.6116 - accuracy: 0.6418 - top_5_accuracy: 0.8532 - categorical_crossentropy: 1.6668 - soft_logloss: 5.4904 - val_loss: 5.6128 - val_accuracy: 0.6244 - val_top_5_accuracy: 0.8428 - val_categorical_crossentropy: 1.6673 - val_soft_logloss: 5.4915\n", + "Epoch 6/30\n", + "400/400 [==============================] - 23s - loss: 5.6048 - accuracy: 0.6636 - top_5_accuracy: 0.8650 - categorical_crossentropy: 1.5771 - soft_logloss: 5.4896 - val_loss: 5.6079 - val_accuracy: 0.6213 - val_top_5_accuracy: 0.8496 - val_categorical_crossentropy: 1.6315 - val_soft_logloss: 5.4888\n", + "Epoch 7/30\n", + "400/400 [==============================] - 23s - loss: 5.6001 - accuracy: 0.6760 - top_5_accuracy: 0.8740 - categorical_crossentropy: 1.5145 - soft_logloss: 5.4891 - val_loss: 5.6029 - val_accuracy: 0.6369 - val_top_5_accuracy: 0.8518 - val_categorical_crossentropy: 1.5752 - val_soft_logloss: 5.4875\n", + "Epoch 8/30\n", + "400/400 [==============================] - 24s - loss: 5.5966 - accuracy: 0.6841 - top_5_accuracy: 0.8798 - categorical_crossentropy: 1.4656 - soft_logloss: 5.4888 - val_loss: 5.6024 - val_accuracy: 0.6422 - val_top_5_accuracy: 0.8596 - val_categorical_crossentropy: 1.5532 - val_soft_logloss: 5.4884\n", + "Epoch 9/30\n", + "400/400 [==============================] - 23s - loss: 5.5935 - accuracy: 0.6929 - top_5_accuracy: 0.8843 - categorical_crossentropy: 1.4258 - soft_logloss: 5.4883 - val_loss: 5.6016 - val_accuracy: 0.6430 - val_top_5_accuracy: 0.8539 - val_categorical_crossentropy: 1.5479 - val_soft_logloss: 5.4878\n", + "Epoch 10/30\n", + "400/400 [==============================] - 23s - loss: 5.5913 - accuracy: 0.7013 - top_5_accuracy: 0.8882 - categorical_crossentropy: 1.3932 - soft_logloss: 5.4882 - val_loss: 5.6010 - val_accuracy: 0.6479 - val_top_5_accuracy: 0.8586 - val_categorical_crossentropy: 1.5299 - val_soft_logloss: 5.4882\n", + "Epoch 11/30\n", + "400/400 [==============================] - 23s - loss: 5.5893 - accuracy: 0.7086 - top_5_accuracy: 0.8922 - categorical_crossentropy: 1.3639 - soft_logloss: 5.4881 - val_loss: 5.6000 - val_accuracy: 0.6496 - val_top_5_accuracy: 0.8643 - val_categorical_crossentropy: 1.5198 - val_soft_logloss: 5.4878\n", + "Epoch 12/30\n", + "400/400 [==============================] - 23s - loss: 5.5876 - accuracy: 0.7136 - top_5_accuracy: 0.8962 - categorical_crossentropy: 1.3396 - soft_logloss: 5.4879 - val_loss: 5.6018 - val_accuracy: 0.6475 - val_top_5_accuracy: 0.8602 - val_categorical_crossentropy: 1.5318 - val_soft_logloss: 5.4886\n", + "Epoch 13/30\n", + "400/400 [==============================] - 23s - loss: 5.5863 - accuracy: 0.7170 - top_5_accuracy: 0.8991 - categorical_crossentropy: 1.3194 - soft_logloss: 5.4879 - val_loss: 5.5964 - val_accuracy: 0.6559 - val_top_5_accuracy: 0.8660 - val_categorical_crossentropy: 1.4852 - val_soft_logloss: 5.4863\n", + "Epoch 14/30\n", + "400/400 [==============================] - 24s - loss: 5.5855 - accuracy: 0.7220 - top_5_accuracy: 0.8981 - categorical_crossentropy: 1.3094 - soft_logloss: 5.4877 - val_loss: 5.5999 - val_accuracy: 0.6492 - val_top_5_accuracy: 0.8609 - val_categorical_crossentropy: 1.4970 - val_soft_logloss: 5.4889\n", + "Epoch 15/30\n", + "400/400 [==============================] - 24s - loss: 5.5845 - accuracy: 0.7254 - top_5_accuracy: 0.9034 - categorical_crossentropy: 1.2905 - soft_logloss: 5.4879 - val_loss: 5.5961 - val_accuracy: 0.6584 - val_top_5_accuracy: 0.8604 - val_categorical_crossentropy: 1.4825 - val_soft_logloss: 5.4860\n", + "Epoch 16/30\n", + "400/400 [==============================] - 24s - loss: 5.5835 - accuracy: 0.7258 - top_5_accuracy: 0.9059 - categorical_crossentropy: 1.2741 - soft_logloss: 5.4879 - val_loss: 5.5975 - val_accuracy: 0.6564 - val_top_5_accuracy: 0.8686 - val_categorical_crossentropy: 1.4793 - val_soft_logloss: 5.4875\n", + "Epoch 17/30\n", + "400/400 [==============================] - 23s - loss: 5.5794 - accuracy: 0.7401 - top_5_accuracy: 0.9128 - categorical_crossentropy: 1.2293 - soft_logloss: 5.4868 - val_loss: 5.5948 - val_accuracy: 0.6627 - val_top_5_accuracy: 0.8682 - val_categorical_crossentropy: 1.4618 - val_soft_logloss: 5.4860\n", + "Epoch 18/30\n", + "400/400 [==============================] - 24s - loss: 5.5788 - accuracy: 0.7466 - top_5_accuracy: 0.9130 - categorical_crossentropy: 1.2241 - soft_logloss: 5.4866 - val_loss: 5.5970 - val_accuracy: 0.6592 - val_top_5_accuracy: 0.8641 - val_categorical_crossentropy: 1.4787 - val_soft_logloss: 5.4870\n", + "Epoch 19/30\n", + "400/400 [==============================] - 23s - loss: 5.5783 - accuracy: 0.7441 - top_5_accuracy: 0.9142 - categorical_crossentropy: 1.2181 - soft_logloss: 5.4866 - val_loss: 5.5931 - val_accuracy: 0.6641 - val_top_5_accuracy: 0.8715 - val_categorical_crossentropy: 1.4433 - val_soft_logloss: 5.4856\n", + "Epoch 20/30\n", + "400/400 [==============================] - 23s - loss: 5.5787 - accuracy: 0.7448 - top_5_accuracy: 0.9116 - categorical_crossentropy: 1.2227 - soft_logloss: 5.4866 - val_loss: 5.5967 - val_accuracy: 0.6607 - val_top_5_accuracy: 0.8658 - val_categorical_crossentropy: 1.4738 - val_soft_logloss: 5.4871\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.fit_generator(\n", + " train_generator, \n", + " steps_per_epoch=400, epochs=30, verbose=1,\n", + " callbacks=[\n", + " EarlyStopping(monitor='val_accuracy', patience=4, min_delta=0.01), \n", + " ReduceLROnPlateau(monitor='val_accuracy', factor=0.1, patience=2, epsilon=0.007)\n", + " ],\n", + " validation_data=val_generator, validation_steps=80, workers=4\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Loss/epoch plots" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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1yLSS8FAws7uAS4CDzrklEZ4/D/gVsNNf9ZBz7jvjva5CYXLp6u3ntdomXtjW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\nvAbMM7Mq/7/JNcDDI9o8DAy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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(model.history.history['categorical_crossentropy'], label='train');\n", + "plt.plot(model.history.history['val_categorical_crossentropy'], label='val');\n", + "plt.legend();\n", + "plt.xlabel('epoch');\n", + "plt.ylabel('logloss');" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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rJ50hLyE2sxvMrMrMqkKhUIwhH4aBshajVES96/nNFOWk8/nT5459DCIi40Cs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\nhuH/ERP5/g2Y6pwbuFp/DzB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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(model.history.history['accuracy'], label='train');\n", + "plt.plot(model.history.history['val_accuracy'], label='val');\n", + "plt.legend();\n", + "plt.xlabel('epoch');\n", + "plt.ylabel('accuracy');" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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OCpJA5TzILRh5n+mjvFZ3O7TsDJPF9qG3dSuh65BBj49eVm5w9n/aew4mgdpF0dT7Jys7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\np/P7OyqTNTEcF8ysBPgR8Cl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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(model.history.history['top_5_accuracy'], label='train');\n", + "plt.plot(model.history.history['val_top_5_accuracy'], label='val');\n", + "plt.legend();\n", + "plt.xlabel('epoch');\n", + "plt.ylabel('top5_accuracy');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Results" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 5120 images belonging to 256 classes.\n" + ] + } + ], + "source": [ + "val_generator_no_shuffle = data_generator.flow_from_directory(\n", + " data_dir + 'val', val_logits,\n", + " target_size=(224, 224),\n", + " batch_size=64, shuffle=False\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[5.595249432325363, 0.66171875000000002, 0.86679687500000002, 1.4644993908703328, 5.4862456738948824]\n" + ] + } + ], + "source": [ + "print(model.evaluate_generator(val_generator_no_shuffle, 80))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/knowledge_distillation_for_squeezenet.ipynb b/knowledge_distillation_for_squeezenet.ipynb new file mode 100644 index 0000000..cf5326d --- /dev/null +++ b/knowledge_distillation_for_squeezenet.ipynb @@ -0,0 +1,587 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import sys\n", + "sys.path.append('utils/')\n", + "\n", + "import keras\n", + "from keras import optimizers\n", + "from keras.callbacks import ReduceLROnPlateau, EarlyStopping\n", + "\n", + "# use non standard flow_from_directory\n", + "from image_preprocessing_ver2 import ImageDataGenerator\n", + "# it outputs y_batch that contains onehot targets and logits\n", + "# logits came from xception\n", + "\n", + "from keras.models import Model\n", + "from keras.layers import Lambda, concatenate, Activation\n", + "from keras.losses import categorical_crossentropy as logloss\n", + "from keras.metrics import categorical_accuracy, top_k_categorical_accuracy\n", + "from keras import backend as K\n", + "\n", + "from squeezenet import SqueezeNet, preprocess_input\n", + "\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "data_dir = '/home/ubuntu/data/'" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "train_logits = np.load(data_dir + 'train_logits.npy')[()]\n", + "val_logits = np.load(data_dir + 'val_logits.npy')[()]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 25600 images belonging to 256 classes.\n", + "Found 5120 images belonging to 256 classes.\n" + ] + } + ], + "source": [ + "data_generator = ImageDataGenerator(\n", + " data_format='channels_last',\n", + " preprocessing_function=preprocess_input\n", + ")\n", + "\n", + "# note: i'm also passing dicts of logits\n", + "train_generator = data_generator.flow_from_directory(\n", + " data_dir + 'train', train_logits,\n", + " target_size=(299, 299),\n", + " batch_size=64\n", + ")\n", + "\n", + "val_generator = data_generator.flow_from_directory(\n", + " data_dir + 'val', val_logits,\n", + " target_size=(299, 299),\n", + " batch_size=64\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Show effect of the temperature" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def softmax(x):\n", + " return np.exp(x)/np.exp(x).sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# get a random batch\n", + "pictures, labels_and_logits = train_generator.next()\n", + "onehot_target, logits = labels_and_logits[:, :256], labels_and_logits[:, 256:]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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D97r7U2b2YTN7XVTto8BU4GtmtsPMtvRzOBEREZlEgsDZtm+czj+W64b//HP4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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# logits for a random image\n", + "v = logits[4]\n", + "plt.figure(figsize=(10, 5));\n", + "plt.plot(np.sort(softmax(v)), label='$T=1$', linewidth=2);\n", + "plt.plot(np.sort(softmax(v/2)), label='$T=2$', linewidth=2);\n", + "plt.plot(np.sort(softmax(v/3)), label='$T=3$', linewidth=2);\n", + "plt.plot(np.sort(softmax(v/7)), label='$T=7$', linewidth=2);\n", + "plt.legend();\n", + "plt.xlabel('classes sorted by probability, most probable ->');\n", + "plt.ylabel('probability');\n", + "plt.xlim([245, 255]);" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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KN6xdjTc57Qe7rD95YMsRERGRVNbvOsLH7q1k+8Emgn4f/3HxfN5/zix8Pst1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\nSUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# logits for a random image\n", + "v = logits[20]\n", + "plt.figure(figsize=(10, 5));\n", + "plt.plot(np.sort(softmax(v)), label='$T=1$', linewidth=2);\n", + "plt.plot(np.sort(softmax(v/2)), label='$T=2$', linewidth=2);\n", + "plt.plot(np.sort(softmax(v/3)), label='$T=3$', linewidth=2);\n", + "plt.plot(np.sort(softmax(v/7)), label='$T=7$', linewidth=2);\n", + "plt.legend();\n", + "plt.xlabel('classes sorted by probability, most probable ->');\n", + "plt.ylabel('probability');\n", + "plt.xlim([245, 255]);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Create model" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "temperature = 5.0" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "model = SqueezeNet(weight_decay=1e-4, image_size=299)\n", + "\n", + "# remove softmax\n", + "model.layers.pop()\n", + "\n", + "# usual probabilities\n", + "logits = model.layers[-1].output\n", + "probabilities = Activation('softmax')(logits)\n", + "\n", + "# softed probabilities\n", + "logits_T = Lambda(lambda x: x/temperature)(logits)\n", + "probabilities_T = Activation('softmax')(logits_T)\n", + "\n", + "output = concatenate([probabilities, probabilities_T])\n", + "model = Model(model.input, output)\n", + "# now model outputs 512 dimensional vectors" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Create custom loss" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def knowledge_distillation_loss(y_true, y_pred, lambda_const): \n", + " \n", + " # split in \n", + " # onehot hard true targets\n", + " # logits from xception\n", + " y_true, logits = y_true[:, :256], y_true[:, 256:]\n", + " \n", + " # convert logits to soft targets\n", + " y_soft = K.softmax(logits/temperature)\n", + " \n", + " # split in \n", + " # usual output probabilities\n", + " # probabilities made softer with temperature\n", + " y_pred, y_pred_soft = y_pred[:, :256], y_pred[:, 256:] \n", + " \n", + " return lambda_const*logloss(y_true, y_pred) + logloss(y_soft, y_pred_soft)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# For testing use usual output probabilities (without temperature)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def accuracy(y_true, y_pred):\n", + " y_true = y_true[:, :256]\n", + " y_pred = y_pred[:, :256]\n", + " return categorical_accuracy(y_true, y_pred)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def top_5_accuracy(y_true, y_pred):\n", + " y_true = y_true[:, :256]\n", + " y_pred = y_pred[:, :256]\n", + " return top_k_categorical_accuracy(y_true, y_pred)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def categorical_crossentropy(y_true, y_pred):\n", + " y_true = y_true[:, :256]\n", + " y_pred = y_pred[:, :256]\n", + " return logloss(y_true, y_pred)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# logloss with only soft probabilities and targets\n", + "def soft_logloss(y_true, y_pred): \n", + " logits = y_true[:, 256:]\n", + " y_soft = K.softmax(logits/temperature)\n", + " y_pred_soft = y_pred[:, 256:] \n", + " return logloss(y_soft, y_pred_soft)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Train" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "lambda_const = 0.2\n", + "\n", + "model.compile(\n", + " optimizer=optimizers.SGD(lr=1e-2, momentum=0.9, nesterov=True), \n", + " loss=lambda y_true, y_pred: knowledge_distillation_loss(y_true, y_pred, lambda_const), \n", + " metrics=[accuracy, top_5_accuracy, categorical_crossentropy, soft_logloss]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/30\n", + "400/400 [==============================] - 36s - loss: 6.1129 - accuracy: 0.3999 - top_5_accuracy: 0.6179 - categorical_crossentropy: 3.0321 - soft_logloss: 5.5041 - val_loss: 5.8628 - val_accuracy: 0.5820 - val_top_5_accuracy: 0.8186 - val_categorical_crossentropy: 1.8152 - val_soft_logloss: 5.4963\n", + "Epoch 2/30\n", + "400/400 [==============================] - 35s - loss: 5.8049 - accuracy: 0.6626 - top_5_accuracy: 0.8586 - categorical_crossentropy: 1.5629 - soft_logloss: 5.4881 - val_loss: 5.8121 - val_accuracy: 0.6291 - val_top_5_accuracy: 0.8523 - val_categorical_crossentropy: 1.5437 - val_soft_logloss: 5.4984\n", + "Epoch 3/30\n", + "400/400 [==============================] - 35s - loss: 5.7414 - accuracy: 0.7319 - top_5_accuracy: 0.9034 - categorical_crossentropy: 1.2440 - soft_logloss: 5.4870 - val_loss: 5.8001 - val_accuracy: 0.6484 - val_top_5_accuracy: 0.8562 - val_categorical_crossentropy: 1.4661 - val_soft_logloss: 5.5007\n", + "Epoch 4/30\n", + "400/400 [==============================] - 35s - loss: 5.7077 - accuracy: 0.7751 - top_5_accuracy: 0.9242 - categorical_crossentropy: 1.0728 - soft_logloss: 5.4864 - val_loss: 5.7889 - val_accuracy: 0.6586 - val_top_5_accuracy: 0.8652 - val_categorical_crossentropy: 1.4034 - val_soft_logloss: 5.5009\n", + "Epoch 5/30\n", + "400/400 [==============================] - 36s - loss: 5.6860 - accuracy: 0.8019 - top_5_accuracy: 0.9362 - categorical_crossentropy: 0.9609 - soft_logloss: 5.4860 - val_loss: 5.7869 - val_accuracy: 0.6654 - val_top_5_accuracy: 0.8643 - val_categorical_crossentropy: 1.3793 - val_soft_logloss: 5.5027\n", + "Epoch 6/30\n", + "400/400 [==============================] - 35s - loss: 5.6694 - accuracy: 0.8250 - top_5_accuracy: 0.9465 - categorical_crossentropy: 0.8742 - soft_logloss: 5.4858 - val_loss: 5.7876 - val_accuracy: 0.6666 - val_top_5_accuracy: 0.8648 - val_categorical_crossentropy: 1.3902 - val_soft_logloss: 5.5004\n", + "Epoch 7/30\n", + "400/400 [==============================] - 36s - loss: 5.6573 - accuracy: 0.8438 - top_5_accuracy: 0.9534 - categorical_crossentropy: 0.8111 - soft_logloss: 5.4855 - val_loss: 5.7820 - val_accuracy: 0.6805 - val_top_5_accuracy: 0.8646 - val_categorical_crossentropy: 1.3401 - val_soft_logloss: 5.5040\n", + "Epoch 8/30\n", + "400/400 [==============================] - 35s - loss: 5.6475 - accuracy: 0.8541 - top_5_accuracy: 0.9609 - categorical_crossentropy: 0.7586 - soft_logloss: 5.4853 - val_loss: 5.7853 - val_accuracy: 0.6695 - val_top_5_accuracy: 0.8656 - val_categorical_crossentropy: 1.3582 - val_soft_logloss: 5.5028\n", + "Epoch 9/30\n", + "400/400 [==============================] - 36s - loss: 5.6398 - accuracy: 0.8653 - top_5_accuracy: 0.9645 - categorical_crossentropy: 0.7170 - soft_logloss: 5.4852 - val_loss: 5.7838 - val_accuracy: 0.6777 - val_top_5_accuracy: 0.8689 - val_categorical_crossentropy: 1.3341 - val_soft_logloss: 5.5054\n", + "Epoch 10/30\n", + "400/400 [==============================] - 35s - loss: 5.6333 - accuracy: 0.8761 - top_5_accuracy: 0.9675 - categorical_crossentropy: 0.6805 - soft_logloss: 5.4852 - val_loss: 5.7866 - val_accuracy: 0.6773 - val_top_5_accuracy: 0.8674 - val_categorical_crossentropy: 1.3570 - val_soft_logloss: 5.5029\n", + "Epoch 11/30\n", + "400/400 [==============================] - 35s - loss: 5.6178 - accuracy: 0.9008 - top_5_accuracy: 0.9756 - categorical_crossentropy: 0.6073 - soft_logloss: 5.4839 - val_loss: 5.7768 - val_accuracy: 0.6875 - val_top_5_accuracy: 0.8754 - val_categorical_crossentropy: 1.3110 - val_soft_logloss: 5.5022\n", + "Epoch 12/30\n", + "400/400 [==============================] - 36s - loss: 5.6153 - accuracy: 0.9062 - top_5_accuracy: 0.9758 - categorical_crossentropy: 0.5971 - soft_logloss: 5.4835 - val_loss: 5.7697 - val_accuracy: 0.6922 - val_top_5_accuracy: 0.8762 - val_categorical_crossentropy: 1.2696 - val_soft_logloss: 5.5033\n", + "Epoch 13/30\n", + "400/400 [==============================] - 35s - loss: 5.6151 - accuracy: 0.9054 - top_5_accuracy: 0.9755 - categorical_crossentropy: 0.5958 - soft_logloss: 5.4834 - val_loss: 5.7802 - val_accuracy: 0.6838 - val_top_5_accuracy: 0.8695 - val_categorical_crossentropy: 1.3274 - val_soft_logloss: 5.5022\n", + "Epoch 14/30\n", + "400/400 [==============================] - 36s - loss: 5.6137 - accuracy: 0.9082 - top_5_accuracy: 0.9767 - categorical_crossentropy: 0.5895 - soft_logloss: 5.4833 - val_loss: 5.7724 - val_accuracy: 0.6900 - val_top_5_accuracy: 0.8754 - val_categorical_crossentropy: 1.2935 - val_soft_logloss: 5.5012\n", + "Epoch 15/30\n", + "400/400 [==============================] - 36s - loss: 5.6126 - accuracy: 0.9096 - top_5_accuracy: 0.9762 - categorical_crossentropy: 0.5829 - soft_logloss: 5.4834 - val_loss: 5.7723 - val_accuracy: 0.6926 - val_top_5_accuracy: 0.8773 - val_categorical_crossentropy: 1.2860 - val_soft_logloss: 5.5025\n", + "Epoch 16/30\n", + "400/400 [==============================] - 36s - loss: 5.6126 - accuracy: 0.9095 - top_5_accuracy: 0.9770 - categorical_crossentropy: 0.5840 - soft_logloss: 5.4832 - val_loss: 5.7769 - val_accuracy: 0.6871 - val_top_5_accuracy: 0.8754 - val_categorical_crossentropy: 1.3071 - val_soft_logloss: 5.5029\n", + "Epoch 17/30\n", + "400/400 [==============================] - 36s - loss: 5.6119 - accuracy: 0.9113 - top_5_accuracy: 0.9770 - categorical_crossentropy: 0.5808 - soft_logloss: 5.4832 - val_loss: 5.7728 - val_accuracy: 0.6900 - val_top_5_accuracy: 0.8742 - val_categorical_crossentropy: 1.2930 - val_soft_logloss: 5.5016\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.fit_generator(\n", + " train_generator, \n", + " steps_per_epoch=400, epochs=30, verbose=1,\n", + " callbacks=[\n", + " EarlyStopping(monitor='val_accuracy', patience=4, min_delta=0.01),\n", + " ReduceLROnPlateau(monitor='val_accuracy', factor=0.1, patience=2, epsilon=0.007)\n", + " ],\n", + " validation_data=val_generator, validation_steps=80, workers=4\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Loss/epoch plots" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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zaj6fXVLCopJ87FgvXC4iQ148XGTnbuC8I2z/IDDNX64Bfh7DWhJOXnYaXzjn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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(model.history.history['categorical_crossentropy'], label='train');\n", + "plt.plot(model.history.history['val_categorical_crossentropy'], label='val');\n", + "plt.legend();\n", + "plt.xlabel('epoch');\n", + "plt.ylabel('logloss');" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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KPw5n3QZTPq0hj0UkqcV7+EiTBBBctAbddDLv2hC0CFY8HAwjkTsaTrsZZl8d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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(model.history.history['accuracy'], label='train');\n", + "plt.plot(model.history.history['val_accuracy'], label='val');\n", + "plt.legend();\n", + "plt.xlabel('epoch');\n", + "plt.ylabel('accuracy');" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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Dcdu+HR63zsxS8lkQPYMhIukiaqN3cTj4IADuvgsY09dBZpYJ3Ekw4dJM4Goz\nm9ltt1uAhe4+F7gK+Fl47MxwfRYwH/hZeL6U0lXD0DMYIjLIRU0YHWY2qXPFzCYT7WG+04EN7r7R\n3VuBB4HLuu3jQOeMRCM40Jh+GfCgu7e4ewWwITxfSqmsb2Ls8Fzyc9SlVkQGt6hXue8AL5rZ8wQP\n770fuCHCcROALXHrVRzaHfc24Ekz+zIwFOh8IGIC8HK3YydEjHfAaNBBEUkXkWoY7v4ngq60vyOo\nJZzm7pHaMCK4GrjH3UuADwG/MbPIz4eY2Q1mtszMltXW1vZTSNHpGQwRSRdRG70/ArS5++Pu/jjB\nVK2XRzi0moOnci0Jy+J9FlgI4O5LCIYeGR3xWNz9bncvd/fy4uKBfRZib3Mbdfta1aVWRNJC1G/y\n33X3PZ0rYQP4dyMctxSYamZlZpZD0Ii9qNs+m4HzAMxsBkHCqA33u8rMcs2sDJgKvBox3gGxqT4Y\ndFATJ4lIOojahtFTYunzWHdvN7MbgSeATGCBu68ys9uBZe6+CPgG8Eszu4mgAfzT4Sx/q8xsIbCa\n4GHBL7l7R8R4B0RFnQYdFJH0ETVhLDOz/yDoIgvwJWB5lAPdfTFxU7yGZbfGLa8Gzurl2B8AP4gY\n44DTQ3sikk6i3pL6MtBK0Oj9O6CFIGmktYr6RsYNz2NITso9HiIi0u+iDg3SCBzylHa621TfpAf2\nRCRtRB2tthj4O4KnrvM6y9393ATFdUyorGvkgpljkx2GiMiAiHpL6j5gLcFMe98DKgl6QKWthuY2\n6hvVpVZE0kfUhFHk7v9N8CzG8+7+t0Da1y5ADd4ikj6i9pJqC9+3mdnFBOM9jUpMSMeGzi61Zaph\niEiaiJowvm9mIwiemfgpwWCBNyUsqmNA50N7k0ap0VtE0kPUXlKPh4t7gL/pvt3Mvu3uP+zPwFJd\nZV0jx41Ql1oRSR+RB/nrwyf66TzHjIr6RrVfiEha6a+EYf10nmNGZV2jekiJSFrpr4QRZTKlQWNP\nUxu7mto06KCIpBXVMN6Fznm8NeigiKST/koYv++n8xwTOhOGutSKSDo5bMIws9Hd1q81s5+Es9x1\n1Src/Z8SFWAqqqhrxExdakUkvfRVw3iyc8HMbgGuIxjW/ALgPxIYV0rbVN/E+BFDyMtWl1oRSR99\nPYcR3zbxUeD97t5oZvcDryUurNRWUdfI5CLVLkQkvfRVwxhiZnPN7DQgMxzmHHdvA1Jq9ruBVFmv\nLrUikn4LySKdAAAQNklEQVT6qmFs48Ctp51mdpy7bzOzIoJpU9PO7qZWdje1UaYeUiKSZg6bMNz9\nkGFAQruAD/R/OKmvMhxDSjUMEUk3UQcfxMw+CryP4CG9F939DxGPmw/8GMgEfuXud3Tb/iMOjE+V\nD4xx95Hhtg5gZbhts7tfGjXeRDkwrLnaMEQkvUSdce9nwAnAA2HR/zGz8939sPN6m1kmcCdBr6oq\nYKmZLXL31Z37uPtNcft/GZgbd4r97j4n0icZIJ1daieqS62IpJmoNYxzgRnu7gBmdi+wKsJxpwMb\n3H1jeNyDwGXA6l72vxr4bsSYkqKyvlFdakUkLUV90nsDMClufWJY1pcJwJa49aqw7BBmNplgCthn\n4orzzGyZmb1sZpf3ctwN4T7LamtrI4R0dCrrm/SEt4ikpagJowBYY2bPmdlzBDWE4Wa2yMwW9VMs\nVwEPuXt8d93J7l4OfBL4TzOb0v0gd7/b3cvdvby4uLifQuldpZ7BEJE0FfWW1K3v8vzVBLWRTiVh\nWU+uAg5qE3H36vB9Y5io5gLvvMtYjtquxlb27G9TDUNE0lLUGfeeN7OxwLyw6FV3r4lw6FJgqpmV\nESSKqwhqCwcxs+lAIbAkrqwQaHL3lnBMq7OAf4kSb6JU1Hf2kFLCEJH0E+mWlJldAbxKMLPeFcAr\nZvbxvo5z93bgRuAJYA2w0N1XmdntZhbfRfYq4MHORvXQDGCZma0AngXuiO9dlQybOhOGahgikoai\n3pL6DjCvs1ZhZsXAn4GH+jrQ3RcDi7uV3dpt/bYejnsJmB0xvgFRUddEhsHEUUOSHYqIyICL2uid\n0e0WVP0RHDtoVNY1Mn7kEHKz1KVWRNJP1BrGH83sCQ48uHcl3WoN6aCyvlEN3iKStqLWEhy4Czg5\nfN2dsIhSlLtTUdeoBm8RSVtRaxgXuPvfA490FpjZ94C/T0hUKWhXUxt7m9vV4C0iaeuwCcPMvgB8\nETjezN6M21QA/DWRgaWaCg06KCJprq8axv3AH4EfAjfHle91950JiyoFdY1SqxqGiKSpvubD2APs\nIRgUMK1V1jcGXWoLVcMQkfSUdl1j363K+iZKCvPJydKvTETSk65+EWnQQRFJd0oYEbg7lXV6BkNE\n0psSRgT1ja3sbWnXMxgiktaUMCLoHHRQNQwRSWdKGBFU1DUBqA1DRNKaEkYElXWNZGYYE0cpYYhI\n+lLCiKCivpGSwiFkZ+rXJSLpS1fACDbVa9BBEREljD4EXWqbNIaUiKQ9JYw+1O1rZV+LRqkVEUl4\nwjCz+Wa2zsw2mNnNPWz/kZm9Eb7eNrPdcduuN7P14ev6RMfak0rN4y0iAkSfD+NdMbNM4E7gAqAK\nWGpmi9x9dec+7n5T3P5fBuaGy6OA7wLlBBM4LQ+P3ZXImLvrHKW2TG0YIpLmEl3DOB3Y4O4b3b0V\neBC47DD7X82BaWA/CDzl7jvDJPEUMD+h0fagsr6RrAyjpHDIQP9oEZGUkuiEMQHYErdeFZYdwswm\nA2XAM0d6bCJV1jVRUjiELHWpFZE0l0pXwauAh9y940gOMrMbzGyZmS2rra3t96Aq6hrVfiEiQuIT\nRjUwMW69JCzryVUcuB0V+Vh3v9vdy929vLi4+CjDPeTcVOoZDBERIPEJYykw1czKzCyHICks6r6T\nmU0HCoElccVPABeaWaGZFQIXhmUDpnZfC02tHRp0UESEBPeScvd2M7uR4EKfCSxw91VmdjuwzN07\nk8dVwIPu7nHH7jSzfyRIOgC3D/Q84pUadFBEpEtCEwaAuy8GFncru7Xb+m29HLsAWJCw4PrQ1aVW\nNQwRkZRq9E45FWGX2gkj1aVWREQJ4zA21TcyaVS+utSKiKCEcVgVdU1qvxARCSlh9MLdg2HN1X4h\nIgIoYfSqZq+61IqIxFPC6EVnDyk9tCciElDC6EXXsOZKGCIigBJGryrqmsjONMaPzEt2KCIiKUEJ\noxeVdY1MVJdaEZEuuhr2orK+UZMmiYjEUcLoQdCltonJShgiIl2UMHqwo6GF/W0dlI3WQ3siIp2U\nMHpQ0dmlVs9giIh0UcLowSZ1qRUROYQSRg8q6hvJycxgvEapFRHpooTRg6BL7RAyMyzZoYiIpAwl\njB5U1jVpDCkRkW6UMLqJxZzK+ka1X4iIdKOE0c2Ovc20tMfUQ0pEpJuEJwwzm29m68xsg5nd3Ms+\nV5jZajNbZWb3x5V3mNkb4WtRomOFuC61qmGIiBwkK5EnN7NM4E7gAqAKWGpmi9x9ddw+U4FvA2e5\n+y4zGxN3iv3uPieRMXZXWdcEQKke2hMROUiiaxinAxvcfaO7twIPApd12+dzwJ3uvgvA3WsSHNNh\nVdY3kpOVwfgR6lIrIhIv0QljArAlbr0qLIt3InCimf3VzF42s/lx2/LMbFlYfnlPP8DMbgj3WVZb\nW3vUAVfWNTJ5VD4Z6lIrInKQhN6SiigLmAqcA5QAL5jZbHffDUx292ozOx54xsxWuvs78Qe7+93A\n3QDl5eV+tMFU1jdq0EERkR4kuoZRDUyMWy8Jy+JVAYvcvc3dK4C3CRII7l4dvm8EngPmJjLYWCwY\npVaDDoqIHCrRCWMpMNXMyswsB7gK6N7b6VGC2gVmNprgFtVGMys0s9y48rOA1STQtgZ1qRUR6U1C\nb0m5e7uZ3Qg8AWQCC9x9lZndDixz90XhtgvNbDXQAXzL3evN7L3AXWYWI0hsd8T3rkqETWGXWk2c\nJCJyqIS3Ybj7YmBxt7Jb45Yd+Hr4it/nJWB2ouOLVxGOUjtZNQwRkUPoSe84lXWN5GZlcNzwvGSH\nIiKScpQw4lTUNTG5SF1qRUR6ooQRZ5MGHRQR6ZUSRigWczbtbFIPKRGRXihhhLbu2U9re0w1DBGR\nXihhhDTooIjI4SlhhCrDLrWaaU9EpGdKGKHKukbysjMYW6AutSIiPVHCCFXWNzJ51FB1qRUR6YUS\nRqiirlHtFyIih6GEAXTEnC0796tLrYjIYShhAFt376e1I6ZBB0VEDkMJA4i5c/Hs45g5fniyQxER\nSVmpMONe0k0uGsqd15ya7DBERFKaahgiIhKJEoaIiESihCEiIpEoYYiISCQJTxhmNt/M1pnZBjO7\nuZd9rjCz1Wa2yszujyu/3szWh6/rEx2riIj0LqG9pMwsE7gTuACoApaa2SJ3Xx23z1Tg28BZ7r7L\nzMaE5aOA7wLlgAPLw2N3JTJmERHpWaJrGKcDG9x9o7u3Ag8Cl3Xb53PAnZ2JwN1rwvIPAk+5+85w\n21PA/ATHKyIivUh0wpgAbIlbrwrL4p0InGhmfzWzl81s/hEcKyIiAyQVHtzLAqYC5wAlwAtmNjvq\nwWZ2A3BDuLrPzNYdRSyjgbqjOD5RFNeRUVxHRnEdmcEY1+QoOyU6YVQDE+PWS8KyeFXAK+7eBlSY\n2dsECaSaIInEH/tc9x/g7ncDd/dHsGa2zN3L++Nc/UlxHRnFdWQU15FJ57gSfUtqKTDVzMrMLAe4\nCljUbZ9HCRODmY0muEW1EXgCuNDMCs2sELgwLBMRkSRIaA3D3dvN7EaCC30msMDdV5nZ7cAyd1/E\ngcSwGugAvuXu9QBm9o8ESQf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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(model.history.history['top_5_accuracy'], label='train');\n", + "plt.plot(model.history.history['val_top_5_accuracy'], label='val');\n", + "plt.legend();\n", + "plt.xlabel('epoch');\n", + "plt.ylabel('top5_accuracy');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Results" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 5120 images belonging to 256 classes.\n" + ] + } + ], + "source": [ + "val_generator_no_shuffle = data_generator.flow_from_directory(\n", + " data_dir + 'val', val_logits,\n", + " target_size=(299, 299),\n", + " batch_size=64, shuffle=False\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[5.7739551007747654, 0.689453125, 0.87441406249999998, 1.2974167175590992, 5.501888936758041]\n" + ] + } + ], + "source": [ + "print(model.evaluate_generator(val_generator_no_shuffle, 80))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/mnist_data_test.ipynb b/mnist_data_test.ipynb new file mode 100644 index 0000000..4088d72 --- /dev/null +++ b/mnist_data_test.ipynb @@ -0,0 +1,97 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n", + "11493376/11490434 [==============================] - 2s 0us/step\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "mnist = tf.keras.datasets.mnist\n", + "\n", + "(x_train, y_train),(x_test, y_test) = mnist.load_data()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "8\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAADmlJREFUeJzt3X+MVPW5x/HPc7FEsq0GZPkRi3ex2VSNsXSzIUbMDTe9NEJIkD9UiDaYmLtVIbGxJiXU5KL+Q25uW0m8klAlUK1LNUXhD1NRrD9ItLqgFwG1/mBJQYQFCwV/octz/9iD2eqe7wzz68zu834lk505zzlznox+ODPzPXO+5u4CEM+/FN0AgGIQfiAowg8ERfiBoAg/EBThB4Ii/EBQhB8IivADQZ3VyJ2NHz/e29raGrlLIJTe3l4dPnzYylm3qvCb2VWSVkoaJekBd1+RWr+trU09PT3V7BJAQmdnZ9nrVvy238xGSfpfSbMlXSJpoZldUunzAWisaj7zT5f0rru/7+4nJa2XNK82bQGot2rCf76kvw16vC9b9k/MrMvMesysp6+vr4rdAailun/b7+6r3b3T3TtbW1vrvTsAZaom/PslTRn0+LvZMgDDQDXhf1VSu5lNNbPRkhZI2lSbtgDUW8VDfe7+pZktkfSUBob61rj7rpp1BqCuqhrnd/cnJT1Zo14ANBCn9wJBEX4gKMIPBEX4gaAIPxAU4QeCIvxAUIQfCIrwA0ERfiAowg8ERfiBoAg/EBThB4Ii/EBQhB8IivADQRF+ICjCDwRF+IGgCD8QVEOn6AYGO3nyZLL+1FNPJevPPfdcxfvu7u5O1js6OpL1W2+9NVmfM2fOGffUaBz5gaAIPxAU4QeCIvxAUIQfCIrwA0ERfiCoqsb5zaxX0nFJ/ZK+dPfOWjSF4ePTTz9N1u+6667c2vr165Pb7t27N1mfMGFCsj537tzc2vz585PbbtiwIVl/6KGHkvXhMM5fi5N8/t3dD9fgeQA0EG/7gaCqDb9L2mxm28ysqxYNAWiMat/2X+nu+81sgqSnzewtd39h8ArZPwpdknTBBRdUuTsAtVLVkd/d92d/D0l6XNL0IdZZ7e6d7t7Z2tpaze4A1FDF4TezFjP7zun7kn4saWetGgNQX9W87Z8o6XEzO/08j7j7n2rSFYC6qzj87v6+pB/UsBc0oY0bNybrd955Z7K+c2f+m8GxY8cmt7399tuT9bvvvjtZb2lpSdZTFi9enKyXOk9gOGCoDwiK8ANBEX4gKMIPBEX4gaAIPxAUl+4ObseOHcn6Nddck6yfOnUqWV+5cmVu7eabb05uO3r06GS9lNRPgidNmpTc9uKLL07Wt27dWlFPzYQjPxAU4QeCIvxAUIQfCIrwA0ERfiAowg8ExTj/CHf8+PFkfcaMGcm6uyfr27dvT9Yvu+yyZD2lv78/Wb/hhhuS9cceeyy39sQTTyS3TV32W5JGwlWpOPIDQRF+ICjCDwRF+IGgCD8QFOEHgiL8QFCM849wK1asSNZPnDiRrHd1padgrGYcv5RSl+YuNcV3ynnnnVfxtiMFR34gKMIPBEX4gaAIPxAU4QeCIvxAUIQfCKrkOL+ZrZE0V9Ihd780WzZO0h8ktUnqlXStu/+9fm0i5ZNPPsmtdXd3V/Xc99xzT1XbHzt2LLd23XXXJbfdvHlzVft+8cUXc2uXX355Vc89EpRz5F8r6aqvLVsqaYu7t0vakj0GMIyUDL+7vyDpo68tnidpXXZ/naSra9wXgDqr9DP/RHc/kN3/UNLEGvUDoEGq/sLPBy7ylnuhNzPrMrMeM+vp6+urdncAaqTS8B80s8mSlP09lLeiu69290537xwJFz0ERopKw79J0qLs/iJJG2vTDoBGKRl+M+uW9JKk75vZPjO7SdIKSbPM7B1J/5E9BjCMlBznd/eFOaUf1bgXVOjUqVO5tc8//7yq5z5y5Eiy3tLSkqwvXrw4t/bMM88ktz377LOT9YcffjhZ7+joyK2ZWXLbCDjDDwiK8ANBEX4gKMIPBEX4gaAIPxAUl+4eAVLDeR9//HFVz/3oo48m6/fee2+yfvTo0dzauHHjktu+/PLLyXp7e3uyjjSO/EBQhB8IivADQRF+ICjCDwRF+IGgCD8QFOP8I0B/f39ubezYscltU5fWlqTly5dX0tJX5s2bl1t75JFHktuW+kkvqsORHwiK8ANBEX4gKMIPBEX4gaAIPxAU4QeCYpx/BHjrrbdya6lzAMoxZsyYZP3+++9P1hcsWJBbYxy/WBz5gaAIPxAU4QeCIvxAUIQfCIrwA0ERfiCokuP8ZrZG0lxJh9z90mzZckn/KakvW22Zuz9Zryaj27NnT7I+a9as3NrJkyer2vfs2bOT9dQ4vsRYfjMr58i/VtJVQyz/jbtPy24EHxhmSobf3V+Q9FEDegHQQNV85l9iZjvMbI2Zpa8VBaDpVBr+VZK+J2mapAOSfpW3opl1mVmPmfX09fXlrQagwSoKv7sfdPd+dz8l6beSpifWXe3une7e2draWmmfAGqsovCb2eRBD+dL2lmbdgA0SjlDfd2SZkoab2b7JP2XpJlmNk2SS+qV9NM69gigDkqG390XDrH4wTr0Etbzzz+frKfG8SVp0qRJubU77rgjue3atWuT9Q0bNiTr9913X7Jeav8oDmf4AUERfiAowg8ERfiBoAg/EBThB4Li0t0NsGvXrmS91M9izSxZ37x5c27toosuSm67bdu2ZP21115L1j/77LNkHc2LIz8QFOEHgiL8QFCEHwiK8ANBEX4gKMIPBMU4f5m++OKL3Nru3buT23Z0dCTrZ52V/s+wZcuWZL3UWH7KLbfckqx3d3cn62+//XbF+0axOPIDQRF+ICjCDwRF+IGgCD8QFOEHgiL8QFCM85fpyJEjubVp06Yltx0zZkyyXmqsfMqUKcl6yokTJ5L12267LVkfNWpUsl7qPAE0L478QFCEHwiK8ANBEX4gKMIPBEX4gaAIPxBUyXF+M5si6XeSJkpySavdfaWZjZP0B0ltknolXevuf69fq/VVajx8zpw5FT/3s88+m6yXGsd392T9lVdeya1df/31yW3fe++9ZH3mzJnJ+hVXXJGso3mVc+T/UtLP3f0SSZdLWmxml0haKmmLu7dL2pI9BjBMlAy/ux9w9+3Z/eOS3pR0vqR5ktZlq62TdHW9mgRQe2f0md/M2iT9UNJfJE109wNZ6UMNfCwAMEyUHX4z+7akP0r6mbv/Y3DNBz6UDvnB1My6zKzHzHr6+vqqahZA7ZQVfjP7lgaC/3t335AtPmhmk7P6ZEmHhtrW3Ve7e6e7d7a2ttaiZwA1UDL8NjBF7IOS3nT3Xw8qbZK0KLu/SNLG2rcHoF7K+UnvDEk/kfSGmb2eLVsmaYWkR83sJkl7JV1bnxYb44MPPkjWS01VnTJ9+vRk/ejRo8n6smXLkvVVq1adcU+n3Xjjjcn6Aw88UPFzo7mVDL+7b5WUN0H8j2rbDoBG4Qw/ICjCDwRF+IGgCD8QFOEHgiL8QFBcujszcWL6pwlTp07Nre3Zsye57YUXXpisHzt2LFkvdR7AhAkTcmtLl6Z/bLlkyZJkvdSluzF8ceQHgiL8QFCEHwiK8ANBEX4gKMIPBEX4gaAY58+ce+65yfpLL72UW+vq6kpuu2nTpop6Oq29vT1Z7+npya2dc845Ve0bIxdHfiAowg8ERfiBoAg/EBThB4Ii/EBQhB8IinH+MqV+779xI/OVYPjhyA8ERfiBoAg/EBThB4Ii/EBQhB8IivADQZUMv5lNMbM/m9luM9tlZrdly5eb2X4zez27zal/uwBqpZyTfL6U9HN3325m35G0zcyezmq/cff/qV97AOqlZPjd/YCkA9n942b2pqTz690YgPo6o8/8ZtYm6YeS/pItWmJmO8xsjZmNzdmmy8x6zKynr6+vqmYB1E7Z4Tezb0v6o6Sfufs/JK2S9D1J0zTwzuBXQ23n7qvdvdPdO1tbW2vQMoBaKCv8ZvYtDQT/9+6+QZLc/aC797v7KUm/lTS9fm0CqLVyvu03SQ9KetPdfz1o+eRBq82XtLP27QGol3K+7Z8h6SeS3jCz17NlyyQtNLNpklxSr6Sf1qVDAHVRzrf9WyXZEKUna98OgEbhDD8gKMIPBEX4gaAIPxAU4QeCIvxAUIQfCIrwA0ERfiAowg8ERfiBoAg/EBThB4Ii/EBQ5u6N25lZn6S9gxaNl3S4YQ2cmWbtrVn7kuitUrXs7V/dvazr5TU0/N/YuVmPu3cW1kBCs/bWrH1J9FaponrjbT8QFOEHgio6/KsL3n9Ks/bWrH1J9FapQnor9DM/gOIUfeQHUJBCwm9mV5nZ22b2rpktLaKHPGbWa2ZvZDMP9xTcyxozO2RmOwctG2dmT5vZO9nfIadJK6i3ppi5OTGzdKGvXbPNeN3wt/1mNkrSXyXNkrRP0quSFrr77oY2ksPMeiV1unvhY8Jm9m+STkj6nbtfmi37b0kfufuK7B/Ose7+iybpbbmkE0XP3JxNKDN58MzSkq6WdKMKfO0SfV2rAl63Io780yW96+7vu/tJSeslzSugj6bn7i9I+uhri+dJWpfdX6eB/3kaLqe3puDuB9x9e3b/uKTTM0sX+tol+ipEEeE/X9LfBj3ep+aa8tslbTazbWbWVXQzQ5iYTZsuSR9KmlhkM0MoOXNzI31tZummee0qmfG61vjC75uudPcOSbMlLc7e3jYlH/jM1kzDNWXN3NwoQ8ws/ZUiX7tKZ7yutSLCv1/SlEGPv5stawruvj/7e0jS42q+2YcPnp4kNft7qOB+vtJMMzcPNbO0muC1a6YZr4sI/6uS2s1sqpmNlrRA0qYC+vgGM2vJvoiRmbVI+rGab/bhTZIWZfcXSdpYYC//pFlmbs6bWVoFv3ZNN+O1uzf8JmmOBr7xf0/SL4voIaevCyX9X3bbVXRvkro18DbwCw18N3KTpPMkbZH0jqRnJI1rot4ekvSGpB0aCNrkgnq7UgNv6XdIej27zSn6tUv0Vcjrxhl+QFB84QcERfiBoAg/EBThB4Ii/EBQhB8IivADQRF+IKj/B+4Jb0bYriM/AAAAAElFTkSuQmCC\n", + "text/plain": [ + "

" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "%matplotlib inline \n", + "\n", + "image_index = 7777 # You may select anything up to 60,000\n", + "print(y_train[image_index]) # The label is 8\n", + "plt.imshow(x_train[image_index], cmap='Greys')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/mobilenet.py b/mobilenet.py new file mode 100644 index 0000000..086dc9f --- /dev/null +++ b/mobilenet.py @@ -0,0 +1,23 @@ +import keras +from keras.applications.mobilenet import MobileNet +from keras.models import Model +from keras.layers import Activation, GlobalAveragePooling2D, Dropout, Dense, Input + + +def get_mobilenet(input_size, alpha, weight_decay, dropout): + input_shape = (input_size, input_size, 3) + base_model = MobileNet( + include_top=False, weights='imagenet', + input_shape=input_shape, alpha=alpha + ) + x = base_model.output + x = GlobalAveragePooling2D()(x) + x = Dropout(dropout)(x) + logits = Dense(256, kernel_regularizer=keras.regularizers.l2(weight_decay))(x) + probabilities = Activation('softmax')(logits) + model = Model(base_model.input, probabilities) + + for layer in model.layers[:-2]: + layer.trainable = False + + return model diff --git a/numpy_append_test.ipynb b/numpy_append_test.ipynb new file mode 100644 index 0000000..9769487 --- /dev/null +++ b/numpy_append_test.ipynb @@ -0,0 +1,247 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.11916354 0.00935769]\n", + " [0.50139208 0.30204473]\n", + " [0.34190585 0.98032303]]\n", + "[[0.13578243 0.87503173]\n", + " [0.50204954 0.33548314]]\n" + ] + } + ], + "source": [ + "a = np.random.rand(3,2)\n", + "b = np.random.rand(2,2)\n", + "c = []\n", + "\n", + "print(a)\n", + "print(b)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "b2= np.random.rand(2,2)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "1\n", + "2\n", + "3\n", + "4\n" + ] + } + ], + "source": [ + "for i in range(0,5):\n", + " c.ap" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "ename": "AxisError", + "evalue": "axis 1 is out of bounds for array of dimension 1", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAxisError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mAxisError\u001b[0m: axis 1 is out of bounds for array of dimension 1" + ] + } + ], + "source": [ + "np.append()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.41399962 0.37813481]\n", + " [0.1733906 0.58156499]\n", + " [0.62257235 0.39419615]]\n" + ] + } + ], + "source": [ + "c= np.stack(a)\n", + "print(c)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.78685516 0.17593261]\n", + " [0.27882233 0.13330801]]\n" + ] + } + ], + "source": [ + "c = np.stack(b)\n", + "print(c)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "c=np.append(c,a)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.11916354 0.00935769 0.50139208 0.30204473 0.34190585 0.98032303]\n" + ] + } + ], + "source": [ + "print(c)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.11916354 0.00935769 0.50139208 0.30204473 0.34190585 0.98032303\n", + " 0.13578243 0.87503173 0.50204954 0.33548314]\n" + ] + } + ], + "source": [ + "c=np.append(c,b)\n", + "print(c)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.11916354 0.00935769]\n", + " [0.50139208 0.30204473]\n", + " [0.34190585 0.98032303]\n", + " [0.13578243 0.87503173]\n", + " [0.50204954 0.33548314]]\n" + ] + } + ], + "source": [ + "d=np.reshape(c,(5,2))\n", + "print(d)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.16674636 0.4438501 0.13578243 0.87503173]\n", + " [0.27808818 0.29201852 0.50204954 0.33548314]]\n", + "[[0.16674636 0.4438501 ]\n", + " [0.27808818 0.29201852]]\n" + ] + } + ], + "source": [ + "e= np.c_[b2,b]\n", + "print(e)\n", + "print(b2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/squeezenet.py b/squeezenet.py new file mode 100644 index 0000000..025bd01 --- /dev/null +++ b/squeezenet.py @@ -0,0 +1,96 @@ +import numpy as np +import keras +from keras.models import Model +from keras.layers import Input, Convolution2D, MaxPooling2D +from keras.layers import Activation, Dropout, GlobalAveragePooling2D, concatenate + + +mean = np.array([0.485, 0.456, 0.406], dtype='float32') +std = np.array([0.229, 0.224, 0.225], dtype='float32') + + +def preprocess_input(x): + x /= 255.0 + x -= mean + x /= std + return x + + +# a building block of the SqueezeNet architecture +def fire_module(number, x, squeeze, expand, weight_decay=None, trainable=False): + + module_name = 'fire' + number + + if trainable and weight_decay is not None: + kernel_regularizer = keras.regularizers.l2(weight_decay) + else: + kernel_regularizer = None + + x = Convolution2D( + squeeze, (1, 1), + name=module_name + '/' + 'squeeze', + trainable=trainable, + kernel_regularizer=kernel_regularizer + )(x) + x = Activation('relu')(x) + + a = Convolution2D( + expand, (1, 1), + name=module_name + '/' + 'expand1x1', + trainable=trainable, + kernel_regularizer=kernel_regularizer + )(x) + a = Activation('relu')(a) + + b = Convolution2D( + expand, (3, 3), padding='same', + name=module_name + '/' + 'expand3x3', + trainable=trainable, + kernel_regularizer=kernel_regularizer + )(x) + b = Activation('relu')(b) + + return concatenate([a, b]) + + +def SqueezeNet(weight_decay, image_size=224): + + image = Input(shape=(image_size, image_size, 3)) + + x = Convolution2D( + 64, (3, 3), strides=(2, 2), name='conv1', + trainable=False + )(image) # 111, 111, 64 + + x = Activation('relu')(x) + x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2))(x) # 55, 55, 64 + + x = fire_module('2', x, squeeze=16, expand=64) # 55, 55, 128 + x = fire_module('3', x, squeeze=16, expand=64) # 55, 55, 128 + x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2))(x) # 27, 27, 128 + + x = fire_module('4', x, squeeze=32, expand=128) # 27, 27, 256 + x = fire_module('5', x, squeeze=32, expand=128) # 27, 27, 256 + x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2))(x) # 13, 13, 256 + + x = fire_module('6', x, squeeze=48, expand=192) # 13, 13, 384 + x = fire_module('7', x, squeeze=48, expand=192) # 13, 13, 384 + x = fire_module('8', x, squeeze=64, expand=256) # 13, 13, 512 + x = fire_module('9', x, squeeze=64, expand=256) # 13, 13, 512 + + x = Dropout(0.5)(x) + x = Convolution2D( + 256, (1, 1), name='conv10', + kernel_initializer=keras.initializers.RandomNormal(stddev=0.01), + kernel_regularizer=keras.regularizers.l2(weight_decay) + )(x) # 13, 13, 256 + + x = Activation('relu')(x) + logits = GlobalAveragePooling2D()(x) # 256 + probabilities = Activation('softmax')(logits) + + model = Model(image, probabilities) + model.load_weights('squeezenet_weights.hdf5', by_name=True) + + return model + diff --git a/squeezenet_weights.hdf5 b/squeezenet_weights.hdf5 new file mode 100644 index 0000000000000000000000000000000000000000..5000e4e559b30e32f93947d7c9c7b7bcb649576f GIT binary patch literal 3034616 zcmeEv2|QI@_wYrAWS&VS6*49i?%C@SDh)^}jS?yiQW~VmSW1ba43R{GqIuw+y{;rh zgA%1Qr_!TAG>_jc_dfq8@B93p@BhBv_kQ31|91S`b=KbNthM(#Yp=7<-fQo(cBt!M zMR`4W&i6x3j?;;w^5aze^G$TTA8Gj`NsNp0r;1J_L@-_i_ei#%NN{90-}7`t=_(@r z22$+_rJdU07;iT>4o99N<}Ze#%Q!jhCFD#MCEO$J0`7@$uc6XE&g?}6IT$bZ1J4oR za<>+Pitf8@**@fVT(0{@pZ 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--git a/train_val_split/.ipynb_checkpoints/train_val_split-checkpoint.ipynb b/train_val_split/.ipynb_checkpoints/train_val_split-checkpoint.ipynb new file mode 100644 index 0000000..aeed675 --- /dev/null +++ b/train_val_split/.ipynb_checkpoints/train_val_split-checkpoint.ipynb @@ -0,0 +1,366 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from scipy import misc\n", + "\n", + "import os\n", + "import shutil\n", + "from tqdm import tqdm " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Delete rubbish" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# the folder from 256_ObjectCategories.tar file\n", + "dir_train = '/home/ubuntu/data/256_ObjectCategories/'" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# remove non-images\n", + "os.remove(os.path.join(dir_train, '198.spider/RENAME2'))\n", + "shutil.rmtree(os.path.join(dir_train, '056.dog/greg'))\n", + "# we don't need the class with noise\n", + "#shutil.rmtree(os.path.join(dir_train, '257.clutter'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Collect metadata" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 256/256 [01:52<00:00, 1.77it/s]\n" + ] + } + ], + "source": [ + "subdirs = list(os.walk(dir_train))[1:]\n", + "\n", + "# collect train metadata\n", + "train_metadata = []\n", + "\n", + "for dir_path, _, files in tqdm(subdirs):\n", + " \n", + " dir_name = dir_path.split('/')[-1]\n", + " \n", + " for file_name in files:\n", + " if not file_name.startswith('.'):\n", + " # read image\n", + " temp = misc.imread(os.path.join(dir_path, file_name)) \n", + " # collect image metadata\n", + " image_metadata = []\n", + " image_metadata.extend([dir_name, file_name])\n", + " image_metadata.extend( \n", + " list(temp.shape) if len(temp.shape) == 3 \n", + " else [temp.shape[0], temp.shape[1], 1]\n", + " )\n", + " image_metadata.extend([temp.nbytes, temp.dtype])\n", + " # append image metadata to list\n", + " train_metadata.append(image_metadata)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Explore metadata" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "M = pd.DataFrame(train_metadata)\n", + "M.columns = ['directory', 'img_name', 'height', 'width', 'channels', 'byte_size', 'bit_depth']\n", + "\n", + "M['category_name'] = M.directory.apply(lambda x: x.split('.')[-1].lower())\n", + "M['img_extension'] = M.img_name.apply(lambda x: x.split('.')[-1])\n", + "M['category_number'] = M.directory.apply(lambda x: int(x.split('.')[0]))\n", + "\n", + "# remove '101' from some category names\n", + "M.category_name = M.category_name.apply(lambda x: x[:-4] if '101' in x else x)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "422" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# number of grayscale images\n", + "(M.channels != 3).sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['jpg'], dtype=object)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M.img_extension.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([dtype('uint8')], dtype=object)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M.bit_depth.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "256" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# number of categories\n", + "M.category_name.nunique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Create decoder" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'M' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# class number -> class name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdecode\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mM\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'category_name'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcategory_number\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfirst\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miteritems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'M' is not defined" + ] + } + ], + "source": [ + "# class number -> class name\n", + "decode = {n: i for i, n in M.groupby('category_name').category_number.first().iteritems()}" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "np.save('decode.npy', decode)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Split data" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# 20 images per class\n", + "V = M.groupby('category_name', group_keys=False).apply(lambda x: x.sample(n=20, replace=False))\n", + "V.sort_index(inplace=True)\n", + "M.drop(V.index, axis=0, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "24660" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# train data\n", + "len(M)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5120" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# validation data\n", + "len(V)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Save split" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "M.to_csv('train_metadata.csv', index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "V.to_csv('val_metadata.csv', index=False)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + 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+232.t-shirt,232_0313.jpg,250,188,3,141000,uint8,t-shirt,jpg,232 +232.t-shirt,232_0239.jpg,214,259,3,166278,uint8,t-shirt,jpg,232 +232.t-shirt,232_0062.jpg,350,452,3,474600,uint8,t-shirt,jpg,232 +232.t-shirt,232_0141.jpg,200,200,3,120000,uint8,t-shirt,jpg,232 +232.t-shirt,232_0250.jpg,448,300,3,403200,uint8,t-shirt,jpg,232 +232.t-shirt,232_0236.jpg,200,170,3,102000,uint8,t-shirt,jpg,232 +232.t-shirt,232_0208.jpg,200,200,3,120000,uint8,t-shirt,jpg,232 +232.t-shirt,232_0015.jpg,271,200,3,162600,uint8,t-shirt,jpg,232 +232.t-shirt,232_0224.jpg,198,304,3,180576,uint8,t-shirt,jpg,232 +232.t-shirt,232_0181.jpg,480,640,3,921600,uint8,t-shirt,jpg,232 +232.t-shirt,232_0221.jpg,300,200,3,180000,uint8,t-shirt,jpg,232 +232.t-shirt,232_0270.jpg,240,240,3,172800,uint8,t-shirt,jpg,232 +232.t-shirt,232_0058.jpg,300,300,3,270000,uint8,t-shirt,jpg,232 +232.t-shirt,232_0314.jpg,248,282,3,209808,uint8,t-shirt,jpg,232 +232.t-shirt,232_0005.jpg,435,400,3,522000,uint8,t-shirt,jpg,232 +232.t-shirt,232_0349.jpg,255,218,3,166770,uint8,t-shirt,jpg,232 +232.t-shirt,232_0038.jpg,360,480,3,518400,uint8,t-shirt,jpg,232 +232.t-shirt,232_0253.jpg,220,300,3,198000,uint8,t-shirt,jpg,232 +232.t-shirt,232_0082.jpg,200,200,3,120000,uint8,t-shirt,jpg,232 +232.t-shirt,232_0276.jpg,348,216,3,225504,uint8,t-shirt,jpg,232 +232.t-shirt,232_0193.jpg,310,400,3,372000,uint8,t-shirt,jpg,232 +232.t-shirt,232_0033.jpg,198,225,3,133650,uint8,t-shirt,jpg,232 +232.t-shirt,232_0051.jpg,492,432,3,637632,uint8,t-shirt,jpg,232 +232.t-shirt,232_0348.jpg,640,479,3,919680,uint8,t-shirt,jpg,232 +232.t-shirt,232_0108.jpg,275,275,3,226875,uint8,t-shirt,jpg,232 +232.t-shirt,232_0273.jpg,324,347,3,337284,uint8,t-shirt,jpg,232 +232.t-shirt,232_0039.jpg,350,350,3,367500,uint8,t-shirt,jpg,232 +232.t-shirt,232_0124.jpg,250,190,3,142500,uint8,t-shirt,jpg,232 +232.t-shirt,232_0011.jpg,292,225,3,197100,uint8,t-shirt,jpg,232 +232.t-shirt,232_0333.jpg,200,173,3,103800,uint8,t-shirt,jpg,232 +232.t-shirt,232_0240.jpg,216,184,3,119232,uint8,t-shirt,jpg,232 +232.t-shirt,232_0355.jpg,205,220,3,135300,uint8,t-shirt,jpg,232 +232.t-shirt,232_0157.jpg,580,780,3,1357200,uint8,t-shirt,jpg,232 +232.t-shirt,232_0080.jpg,300,300,3,270000,uint8,t-shirt,jpg,232 +232.t-shirt,232_0158.jpg,288,258,3,222912,uint8,t-shirt,jpg,232 +232.t-shirt,232_0210.jpg,200,206,3,123600,uint8,t-shirt,jpg,232 +232.t-shirt,232_0302.jpg,280,300,3,252000,uint8,t-shirt,jpg,232 +232.t-shirt,232_0075.jpg,202,233,3,141198,uint8,t-shirt,jpg,232 +232.t-shirt,232_0316.jpg,345,531,3,549585,uint8,t-shirt,jpg,232 +232.t-shirt,232_0282.jpg,409,520,3,638040,uint8,t-shirt,jpg,232 +232.t-shirt,232_0170.jpg,552,736,3,1218816,uint8,t-shirt,jpg,232 +232.t-shirt,232_0324.jpg,250,200,3,150000,uint8,t-shirt,jpg,232 +232.t-shirt,232_0059.jpg,450,305,3,411750,uint8,t-shirt,jpg,232 +232.t-shirt,232_0265.jpg,587,387,3,681507,uint8,t-shirt,jpg,232 +232.t-shirt,232_0044.jpg,200,300,3,180000,uint8,t-shirt,jpg,232 +232.t-shirt,232_0114.jpg,307,300,3,276300,uint8,t-shirt,jpg,232 +232.t-shirt,232_0279.jpg,388,384,3,446976,uint8,t-shirt,jpg,232 +232.t-shirt,232_0142.jpg,640,480,3,921600,uint8,t-shirt,jpg,232 +232.t-shirt,232_0286.jpg,211,200,3,126600,uint8,t-shirt,jpg,232 +232.t-shirt,232_0173.jpg,361,357,3,386631,uint8,t-shirt,jpg,232 +232.t-shirt,232_0117.jpg,610,700,3,1281000,uint8,t-shirt,jpg,232 +232.t-shirt,232_0298.jpg,290,282,3,245340,uint8,t-shirt,jpg,232 +232.t-shirt,232_0001.jpg,1080,1025,3,3321000,uint8,t-shirt,jpg,232 +232.t-shirt,232_0323.jpg,266,200,3,159600,uint8,t-shirt,jpg,232 +232.t-shirt,232_0103.jpg,815,1015,3,2481675,uint8,t-shirt,jpg,232 +232.t-shirt,232_0057.jpg,183,225,3,123525,uint8,t-shirt,jpg,232 +232.t-shirt,232_0315.jpg,358,477,3,512298,uint8,t-shirt,jpg,232 +232.t-shirt,232_0014.jpg,268,325,3,261300,uint8,t-shirt,jpg,232 +232.t-shirt,232_0067.jpg,282,280,3,236880,uint8,t-shirt,jpg,232 +232.t-shirt,232_0081.jpg,250,380,3,285000,uint8,t-shirt,jpg,232 +232.t-shirt,232_0132.jpg,452,339,3,459684,uint8,t-shirt,jpg,232 +232.t-shirt,232_0088.jpg,195,250,3,146250,uint8,t-shirt,jpg,232 +232.t-shirt,232_0232.jpg,320,360,3,345600,uint8,t-shirt,jpg,232 +232.t-shirt,232_0036.jpg,200,200,3,120000,uint8,t-shirt,jpg,232 +232.t-shirt,232_0292.jpg,515,660,3,1019700,uint8,t-shirt,jpg,232 +232.t-shirt,232_0110.jpg,375,500,3,562500,uint8,t-shirt,jpg,232 +232.t-shirt,232_0046.jpg,1416,1734,3,7366032,uint8,t-shirt,jpg,232 +232.t-shirt,232_0297.jpg,307,613,3,564573,uint8,t-shirt,jpg,232 +232.t-shirt,232_0322.jpg,258,224,3,173376,uint8,t-shirt,jpg,232 +232.t-shirt,232_0032.jpg,350,467,3,490350,uint8,t-shirt,jpg,232 +232.t-shirt,232_0144.jpg,463,360,3,500040,uint8,t-shirt,jpg,232 +232.t-shirt,232_0053.jpg,275,258,3,212850,uint8,t-shirt,jpg,232 +232.t-shirt,232_0092.jpg,496,472,3,702336,uint8,t-shirt,jpg,232 +232.t-shirt,232_0219.jpg,305,240,3,219600,uint8,t-shirt,jpg,232 +232.t-shirt,232_0185.jpg,375,500,3,562500,uint8,t-shirt,jpg,232 +232.t-shirt,232_0304.jpg,262,350,3,275100,uint8,t-shirt,jpg,232 +232.t-shirt,232_0242.jpg,244,230,3,168360,uint8,t-shirt,jpg,232 +232.t-shirt,232_0277.jpg,238,216,3,154224,uint8,t-shirt,jpg,232 +232.t-shirt,232_0035.jpg,425,291,3,371025,uint8,t-shirt,jpg,232 +232.t-shirt,232_0130.jpg,200,300,3,180000,uint8,t-shirt,jpg,232 +232.t-shirt,232_0076.jpg,203,233,3,141897,uint8,t-shirt,jpg,232 +232.t-shirt,232_0341.jpg,396,580,3,689040,uint8,t-shirt,jpg,232 +232.t-shirt,232_0152.jpg,391,293,3,343689,uint8,t-shirt,jpg,232 +232.t-shirt,232_0191.jpg,416,539,3,672672,uint8,t-shirt,jpg,232 +232.t-shirt,232_0106.jpg,181,250,3,135750,uint8,t-shirt,jpg,232 +232.t-shirt,232_0176.jpg,582,376,3,656496,uint8,t-shirt,jpg,232 +232.t-shirt,232_0220.jpg,622,416,1,258752,uint8,t-shirt,jpg,232 +232.t-shirt,232_0093.jpg,423,505,3,640845,uint8,t-shirt,jpg,232 +232.t-shirt,232_0258.jpg,488,650,3,951600,uint8,t-shirt,jpg,232 +232.t-shirt,232_0233.jpg,300,221,3,198900,uint8,t-shirt,jpg,232 +232.t-shirt,232_0269.jpg,239,300,3,215100,uint8,t-shirt,jpg,232 +232.t-shirt,232_0329.jpg,360,534,3,576720,uint8,t-shirt,jpg,232 +232.t-shirt,232_0099.jpg,250,279,3,209250,uint8,t-shirt,jpg,232 +232.t-shirt,232_0112.jpg,200,200,3,120000,uint8,t-shirt,jpg,232 +232.t-shirt,232_0207.jpg,200,200,3,120000,uint8,t-shirt,jpg,232 +232.t-shirt,232_0161.jpg,386,405,3,468990,uint8,t-shirt,jpg,232 +232.t-shirt,232_0228.jpg,280,400,3,336000,uint8,t-shirt,jpg,232 +232.t-shirt,232_0095.jpg,426,568,3,725904,uint8,t-shirt,jpg,232 +232.t-shirt,232_0107.jpg,310,410,3,381300,uint8,t-shirt,jpg,232 +232.t-shirt,232_0002.jpg,411,360,3,443880,uint8,t-shirt,jpg,232 +232.t-shirt,232_0139.jpg,448,702,3,943488,uint8,t-shirt,jpg,232 +232.t-shirt,232_0237.jpg,500,550,3,825000,uint8,t-shirt,jpg,232 +232.t-shirt,232_0245.jpg,208,200,1,41600,uint8,t-shirt,jpg,232 +232.t-shirt,232_0073.jpg,480,640,3,921600,uint8,t-shirt,jpg,232 +232.t-shirt,232_0195.jpg,400,300,3,360000,uint8,t-shirt,jpg,232 +232.t-shirt,232_0274.jpg,208,300,3,187200,uint8,t-shirt,jpg,232 +232.t-shirt,232_0338.jpg,793,600,3,1427400,uint8,t-shirt,jpg,232 +232.t-shirt,232_0068.jpg,274,477,3,392094,uint8,t-shirt,jpg,232 +232.t-shirt,232_0280.jpg,192,256,3,147456,uint8,t-shirt,jpg,232 +232.t-shirt,232_0184.jpg,263,350,3,276150,uint8,t-shirt,jpg,232 +232.t-shirt,232_0085.jpg,474,585,3,831870,uint8,t-shirt,jpg,232 +232.t-shirt,232_0332.jpg,288,313,3,270432,uint8,t-shirt,jpg,232 +232.t-shirt,232_0064.jpg,363,468,3,509652,uint8,t-shirt,jpg,232 +232.t-shirt,232_0183.jpg,438,468,3,614952,uint8,t-shirt,jpg,232 +232.t-shirt,232_0133.jpg,300,300,3,270000,uint8,t-shirt,jpg,232 +232.t-shirt,232_0021.jpg,263,350,3,276150,uint8,t-shirt,jpg,232 +232.t-shirt,232_0320.jpg,227,181,3,123261,uint8,t-shirt,jpg,232 +232.t-shirt,232_0010.jpg,453,500,3,679500,uint8,t-shirt,jpg,232 +232.t-shirt,232_0287.jpg,371,600,3,667800,uint8,t-shirt,jpg,232 +232.t-shirt,232_0120.jpg,284,350,3,298200,uint8,t-shirt,jpg,232 +232.t-shirt,232_0134.jpg,348,350,3,365400,uint8,t-shirt,jpg,232 +232.t-shirt,232_0328.jpg,258,250,3,193500,uint8,t-shirt,jpg,232 +232.t-shirt,232_0168.jpg,310,410,3,381300,uint8,t-shirt,jpg,232 +232.t-shirt,232_0259.jpg,243,300,3,218700,uint8,t-shirt,jpg,232 +232.t-shirt,232_0217.jpg,304,545,3,497040,uint8,t-shirt,jpg,232 +232.t-shirt,232_0248.jpg,408,306,3,374544,uint8,t-shirt,jpg,232 +232.t-shirt,232_0301.jpg,268,230,3,184920,uint8,t-shirt,jpg,232 +232.t-shirt,232_0289.jpg,221,250,3,165750,uint8,t-shirt,jpg,232 +232.t-shirt,232_0214.jpg,541,448,3,727104,uint8,t-shirt,jpg,232 +232.t-shirt,232_0180.jpg,250,290,3,217500,uint8,t-shirt,jpg,232 +232.t-shirt,232_0022.jpg,338,450,3,456300,uint8,t-shirt,jpg,232 +232.t-shirt,232_0325.jpg,180,205,3,110700,uint8,t-shirt,jpg,232 +232.t-shirt,232_0061.jpg,450,600,3,810000,uint8,t-shirt,jpg,232 +232.t-shirt,232_0009.jpg,350,398,3,417900,uint8,t-shirt,jpg,232 +232.t-shirt,232_0223.jpg,562,766,3,1291476,uint8,t-shirt,jpg,232 +232.t-shirt,232_0127.jpg,443,400,3,531600,uint8,t-shirt,jpg,232 +232.t-shirt,232_0018.jpg,1514,1004,3,4560168,uint8,t-shirt,jpg,232 +232.t-shirt,232_0006.jpg,395,500,3,592500,uint8,t-shirt,jpg,232 +232.t-shirt,232_0335.jpg,266,200,3,159600,uint8,t-shirt,jpg,232 +232.t-shirt,232_0077.jpg,242,250,3,181500,uint8,t-shirt,jpg,232 +232.t-shirt,232_0215.jpg,194,176,3,102432,uint8,t-shirt,jpg,232 +232.t-shirt,232_0307.jpg,303,320,3,290880,uint8,t-shirt,jpg,232 +232.t-shirt,232_0351.jpg,300,300,3,270000,uint8,t-shirt,jpg,232 +232.t-shirt,232_0164.jpg,533,400,3,639600,uint8,t-shirt,jpg,232 +232.t-shirt,232_0089.jpg,600,471,3,847800,uint8,t-shirt,jpg,232 +232.t-shirt,232_0296.jpg,600,347,3,624600,uint8,t-shirt,jpg,232 +232.t-shirt,232_0299.jpg,195,200,3,117000,uint8,t-shirt,jpg,232 +232.t-shirt,232_0156.jpg,410,499,3,613770,uint8,t-shirt,jpg,232 +232.t-shirt,232_0074.jpg,640,960,3,1843200,uint8,t-shirt,jpg,232 +232.t-shirt,232_0116.jpg,290,290,3,252300,uint8,t-shirt,jpg,232 +232.t-shirt,232_0312.jpg,267,200,3,160200,uint8,t-shirt,jpg,232 +232.t-shirt,232_0172.jpg,400,556,3,667200,uint8,t-shirt,jpg,232 +232.t-shirt,232_0326.jpg,400,600,3,720000,uint8,t-shirt,jpg,232 +232.t-shirt,232_0063.jpg,350,343,3,360150,uint8,t-shirt,jpg,232 +232.t-shirt,232_0070.jpg,358,295,3,316830,uint8,t-shirt,jpg,232 +232.t-shirt,232_0196.jpg,752,600,3,1353600,uint8,t-shirt,jpg,232 +232.t-shirt,232_0029.jpg,463,360,3,500040,uint8,t-shirt,jpg,232 +232.t-shirt,232_0199.jpg,602,750,3,1354500,uint8,t-shirt,jpg,232 +232.t-shirt,232_0042.jpg,378,366,3,415044,uint8,t-shirt,jpg,232 +232.t-shirt,232_0244.jpg,199,173,3,103281,uint8,t-shirt,jpg,232 +232.t-shirt,232_0254.jpg,211,200,3,126600,uint8,t-shirt,jpg,232 +232.t-shirt,232_0342.jpg,295,394,3,348690,uint8,t-shirt,jpg,232 +232.t-shirt,232_0140.jpg,400,300,3,360000,uint8,t-shirt,jpg,232 +232.t-shirt,232_0321.jpg,391,422,3,495006,uint8,t-shirt,jpg,232 +232.t-shirt,232_0150.jpg,416,295,3,368160,uint8,t-shirt,jpg,232 +232.t-shirt,232_0174.jpg,436,576,3,753408,uint8,t-shirt,jpg,232 +232.t-shirt,232_0119.jpg,464,500,3,696000,uint8,t-shirt,jpg,232 +232.t-shirt,232_0167.jpg,265,184,3,146280,uint8,t-shirt,jpg,232 +232.t-shirt,232_0339.jpg,202,190,3,115140,uint8,t-shirt,jpg,232 +232.t-shirt,232_0340.jpg,296,292,3,259296,uint8,t-shirt,jpg,232 +232.t-shirt,232_0293.jpg,308,410,3,378840,uint8,t-shirt,jpg,232 +232.t-shirt,232_0179.jpg,449,400,3,538800,uint8,t-shirt,jpg,232 +232.t-shirt,232_0198.jpg,750,608,3,1368000,uint8,t-shirt,jpg,232 +232.t-shirt,232_0288.jpg,200,246,3,147600,uint8,t-shirt,jpg,232 +232.t-shirt,232_0045.jpg,191,300,3,171900,uint8,t-shirt,jpg,232 +232.t-shirt,232_0334.jpg,576,325,3,561600,uint8,t-shirt,jpg,232 +232.t-shirt,232_0222.jpg,520,799,3,1246440,uint8,t-shirt,jpg,232 +232.t-shirt,232_0350.jpg,259,200,3,155400,uint8,t-shirt,jpg,232 +232.t-shirt,232_0311.jpg,241,300,3,216900,uint8,t-shirt,jpg,232 +232.t-shirt,232_0109.jpg,275,275,3,226875,uint8,t-shirt,jpg,232 +232.t-shirt,232_0030.jpg,185,250,3,138750,uint8,t-shirt,jpg,232 +232.t-shirt,232_0069.jpg,669,700,3,1404900,uint8,t-shirt,jpg,232 +232.t-shirt,232_0294.jpg,266,300,3,239400,uint8,t-shirt,jpg,232 +232.t-shirt,232_0169.jpg,300,300,3,270000,uint8,t-shirt,jpg,232 +232.t-shirt,232_0246.jpg,310,386,3,358980,uint8,t-shirt,jpg,232 +232.t-shirt,232_0054.jpg,345,300,3,310500,uint8,t-shirt,jpg,232 +232.t-shirt,232_0101.jpg,255,286,3,218790,uint8,t-shirt,jpg,232 +232.t-shirt,232_0252.jpg,480,640,3,921600,uint8,t-shirt,jpg,232 +232.t-shirt,232_0317.jpg,353,216,3,228744,uint8,t-shirt,jpg,232 +232.t-shirt,232_0319.jpg,500,375,3,562500,uint8,t-shirt,jpg,232 +232.t-shirt,232_0278.jpg,576,768,3,1327104,uint8,t-shirt,jpg,232 +232.t-shirt,232_0079.jpg,1050,1400,3,4410000,uint8,t-shirt,jpg,232 +232.t-shirt,232_0344.jpg,368,347,3,383088,uint8,t-shirt,jpg,232 +232.t-shirt,232_0268.jpg,280,202,3,169680,uint8,t-shirt,jpg,232 +232.t-shirt,232_0105.jpg,376,413,3,465864,uint8,t-shirt,jpg,232 +232.t-shirt,232_0159.jpg,495,375,3,556875,uint8,t-shirt,jpg,232 +232.t-shirt,232_0136.jpg,610,700,3,1281000,uint8,t-shirt,jpg,232 +232.t-shirt,232_0040.jpg,429,500,3,643500,uint8,t-shirt,jpg,232 +232.t-shirt,232_0327.jpg,253,207,3,157113,uint8,t-shirt,jpg,232 +232.t-shirt,232_0331.jpg,350,350,3,367500,uint8,t-shirt,jpg,232 +232.t-shirt,232_0004.jpg,800,600,3,1440000,uint8,t-shirt,jpg,232 +232.t-shirt,232_0100.jpg,426,498,3,636444,uint8,t-shirt,jpg,232 +232.t-shirt,232_0065.jpg,454,413,3,562506,uint8,t-shirt,jpg,232 +232.t-shirt,232_0121.jpg,395,500,3,592500,uint8,t-shirt,jpg,232 +232.t-shirt,232_0104.jpg,368,413,3,455952,uint8,t-shirt,jpg,232 +232.t-shirt,232_0336.jpg,360,360,3,388800,uint8,t-shirt,jpg,232 +232.t-shirt,232_0346.jpg,291,238,3,207774,uint8,t-shirt,jpg,232 +232.t-shirt,232_0047.jpg,422,472,3,597552,uint8,t-shirt,jpg,232 +232.t-shirt,232_0216.jpg,237,237,3,168507,uint8,t-shirt,jpg,232 +232.t-shirt,232_0205.jpg,400,300,3,360000,uint8,t-shirt,jpg,232 +232.t-shirt,232_0262.jpg,311,243,3,226719,uint8,t-shirt,jpg,232 +232.t-shirt,232_0204.jpg,240,240,3,172800,uint8,t-shirt,jpg,232 +232.t-shirt,232_0149.jpg,700,700,3,1470000,uint8,t-shirt,jpg,232 +232.t-shirt,232_0263.jpg,288,216,3,186624,uint8,t-shirt,jpg,232 +232.t-shirt,232_0260.jpg,200,200,3,120000,uint8,t-shirt,jpg,232 +232.t-shirt,232_0275.jpg,403,329,3,397761,uint8,t-shirt,jpg,232 +232.t-shirt,232_0241.jpg,321,321,3,309123,uint8,t-shirt,jpg,232 +232.t-shirt,232_0291.jpg,266,256,3,204288,uint8,t-shirt,jpg,232 +232.t-shirt,232_0090.jpg,218,200,3,130800,uint8,t-shirt,jpg,232 +232.t-shirt,232_0284.jpg,486,628,3,915624,uint8,t-shirt,jpg,232 +232.t-shirt,232_0087.jpg,380,380,3,433200,uint8,t-shirt,jpg,232 +232.t-shirt,232_0347.jpg,220,220,3,145200,uint8,t-shirt,jpg,232 +232.t-shirt,232_0266.jpg,229,250,3,171750,uint8,t-shirt,jpg,232 +232.t-shirt,232_0227.jpg,211,211,3,133563,uint8,t-shirt,jpg,232 +232.t-shirt,232_0357.jpg,500,495,3,742500,uint8,t-shirt,jpg,232 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+010.beer-mug,010_0073.jpg,355,399,3,424935,uint8,beer-mug,jpg,10 +010.beer-mug,010_0086.jpg,360,360,3,388800,uint8,beer-mug,jpg,10 +010.beer-mug,010_0001.jpg,248,200,3,148800,uint8,beer-mug,jpg,10 +010.beer-mug,010_0051.jpg,361,290,3,314070,uint8,beer-mug,jpg,10 +010.beer-mug,010_0010.jpg,180,180,3,97200,uint8,beer-mug,jpg,10 +010.beer-mug,010_0019.jpg,354,299,3,317538,uint8,beer-mug,jpg,10 +010.beer-mug,010_0093.jpg,305,259,3,236985,uint8,beer-mug,jpg,10 +010.beer-mug,010_0044.jpg,450,450,3,607500,uint8,beer-mug,jpg,10 +010.beer-mug,010_0007.jpg,480,395,3,568800,uint8,beer-mug,jpg,10 +010.beer-mug,010_0039.jpg,375,203,3,228375,uint8,beer-mug,jpg,10 +010.beer-mug,010_0048.jpg,400,328,3,393600,uint8,beer-mug,jpg,10 +010.beer-mug,010_0034.jpg,200,200,3,120000,uint8,beer-mug,jpg,10 +063.electric-guitar-101,063_0062.jpg,182,194,3,105924,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0083.jpg,174,300,3,156600,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0036.jpg,300,216,3,194400,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0059.jpg,135,300,3,121500,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0041.jpg,481,269,3,388167,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0075.jpg,113,300,3,101700,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0071.jpg,165,300,3,148500,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0096.jpg,210,300,3,189000,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0109.jpg,192,300,3,172800,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0072.jpg,225,300,3,202500,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0112.jpg,131,300,3,117900,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0037.jpg,133,250,3,99750,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0044.jpg,300,400,3,360000,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0024.jpg,213,325,3,207675,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0007.jpg,227,150,3,102150,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0057.jpg,225,300,3,202500,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0115.jpg,147,300,3,132300,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0092.jpg,113,300,3,101700,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0055.jpg,104,120,3,37440,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0121.jpg,163,300,3,146700,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0048.jpg,251,460,3,346380,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0039.jpg,250,250,3,187500,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0009.jpg,120,120,3,43200,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0002.jpg,152,203,3,92568,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0084.jpg,218,300,3,196200,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0001.jpg,358,250,3,268500,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0038.jpg,236,170,3,120360,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0093.jpg,225,300,3,202500,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0106.jpg,194,300,3,174600,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0060.jpg,250,250,3,187500,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0015.jpg,162,470,3,228420,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0070.jpg,166,300,1,49800,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0013.jpg,400,218,3,261600,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0008.jpg,120,120,3,43200,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0105.jpg,174,300,3,156600,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0042.jpg,205,200,3,123000,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0063.jpg,104,120,3,37440,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0046.jpg,207,500,3,310500,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0061.jpg,500,239,3,358500,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0035.jpg,274,217,3,178374,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0018.jpg,425,567,3,722925,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0005.jpg,338,218,3,221052,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0102.jpg,168,300,3,151200,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0050.jpg,500,239,3,358500,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0025.jpg,402,250,3,301500,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0030.jpg,236,310,3,219480,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0076.jpg,127,300,3,114300,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0028.jpg,174,450,3,234900,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0086.jpg,121,300,3,108900,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0020.jpg,226,186,3,126108,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0069.jpg,199,300,3,179100,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0090.jpg,172,300,3,154800,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0006.jpg,240,320,3,230400,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0073.jpg,130,300,3,117000,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0010.jpg,180,150,3,81000,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0053.jpg,328,170,3,167280,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0023.jpg,459,278,3,382806,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0032.jpg,216,320,3,207360,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0098.jpg,151,300,3,135900,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0088.jpg,212,300,3,190800,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0089.jpg,114,300,3,102600,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0031.jpg,171,460,3,235980,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0101.jpg,214,300,3,192600,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0074.jpg,162,300,3,145800,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0012.jpg,168,460,3,231840,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0080.jpg,208,300,1,62400,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0017.jpg,400,300,3,360000,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0077.jpg,269,300,3,242100,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0116.jpg,130,300,3,117000,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0079.jpg,148,300,3,133200,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0118.jpg,124,300,3,111600,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0052.jpg,314,170,3,160140,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0117.jpg,122,300,3,109800,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0051.jpg,360,195,3,210600,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0021.jpg,250,250,3,187500,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0081.jpg,200,300,3,180000,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0082.jpg,159,300,3,143100,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0108.jpg,124,300,3,111600,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0097.jpg,210,300,3,189000,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0056.jpg,200,189,3,113400,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0003.jpg,248,278,3,206832,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0011.jpg,200,150,3,90000,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0095.jpg,151,300,3,135900,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0047.jpg,500,175,3,262500,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0078.jpg,204,300,3,183600,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0104.jpg,135,300,3,121500,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0068.jpg,225,300,3,202500,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0103.jpg,146,300,3,131400,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0120.jpg,159,300,3,143100,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0058.jpg,143,161,3,69069,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0113.jpg,220,300,3,198000,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0019.jpg,153,410,3,188190,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0043.jpg,115,120,3,41400,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0016.jpg,369,500,3,553500,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0045.jpg,265,460,3,365700,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0067.jpg,134,300,3,120600,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0100.jpg,97,300,3,87300,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0110.jpg,161,300,3,144900,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0091.jpg,225,300,3,202500,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0040.jpg,165,270,3,133650,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0099.jpg,225,300,3,202500,uint8,electric-guitar,jpg,63 +063.electric-guitar-101,063_0064.jpg,212,300,3,190800,uint8,electric-guitar,jpg,63 +235.umbrella-101,235_0092.jpg,300,300,3,270000,uint8,umbrella,jpg,235 +235.umbrella-101,235_0073.jpg,211,300,3,189900,uint8,umbrella,jpg,235 +235.umbrella-101,235_0071.jpg,300,281,3,252900,uint8,umbrella,jpg,235 +235.umbrella-101,235_0056.jpg,266,300,3,239400,uint8,umbrella,jpg,235 +235.umbrella-101,235_0037.jpg,150,200,3,90000,uint8,umbrella,jpg,235 +235.umbrella-101,235_0101.jpg,299,300,3,269100,uint8,umbrella,jpg,235 +235.umbrella-101,235_0034.jpg,144,214,3,92448,uint8,umbrella,jpg,235 +235.umbrella-101,235_0102.jpg,206,300,3,185400,uint8,umbrella,jpg,235 +235.umbrella-101,235_0030.jpg,300,300,3,270000,uint8,umbrella,jpg,235 +235.umbrella-101,235_0111.jpg,300,277,3,249300,uint8,umbrella,jpg,235 +235.umbrella-101,235_0036.jpg,263,350,3,276150,uint8,umbrella,jpg,235 +235.umbrella-101,235_0042.jpg,413,468,3,579852,uint8,umbrella,jpg,235 +235.umbrella-101,235_0084.jpg,203,300,3,182700,uint8,umbrella,jpg,235 +235.umbrella-101,235_0080.jpg,300,300,3,270000,uint8,umbrella,jpg,235 +235.umbrella-101,235_0096.jpg,300,300,3,270000,uint8,umbrella,jpg,235 +235.umbrella-101,235_0083.jpg,300,297,3,267300,uint8,umbrella,jpg,235 +235.umbrella-101,235_0046.jpg,137,200,3,82200,uint8,umbrella,jpg,235 +235.umbrella-101,235_0074.jpg,226,300,3,203400,uint8,umbrella,jpg,235 +235.umbrella-101,235_0003.jpg,210,300,3,189000,uint8,umbrella,jpg,235 +235.umbrella-101,235_0063.jpg,206,300,3,185400,uint8,umbrella,jpg,235 +235.umbrella-101,235_0085.jpg,300,241,3,216900,uint8,umbrella,jpg,235 +235.umbrella-101,235_0067.jpg,221,300,3,198900,uint8,umbrella,jpg,235 +235.umbrella-101,235_0097.jpg,300,300,3,270000,uint8,umbrella,jpg,235 +235.umbrella-101,235_0021.jpg,480,640,3,921600,uint8,umbrella,jpg,235 +235.umbrella-101,235_0053.jpg,253,300,3,227700,uint8,umbrella,jpg,235 +235.umbrella-101,235_0086.jpg,300,300,3,270000,uint8,umbrella,jpg,235 +235.umbrella-101,235_0050.jpg,265,400,3,318000,uint8,umbrella,jpg,235 +235.umbrella-101,235_0033.jpg,180,220,3,118800,uint8,umbrella,jpg,235 +235.umbrella-101,235_0049.jpg,200,200,3,120000,uint8,umbrella,jpg,235 +235.umbrella-101,235_0023.jpg,343,400,3,411600,uint8,umbrella,jpg,235 +235.umbrella-101,235_0045.jpg,152,203,3,92568,uint8,umbrella,jpg,235 +235.umbrella-101,235_0044.jpg,122,150,3,54900,uint8,umbrella,jpg,235 +235.umbrella-101,235_0032.jpg,275,275,3,226875,uint8,umbrella,jpg,235 +235.umbrella-101,235_0094.jpg,271,300,3,243900,uint8,umbrella,jpg,235 +235.umbrella-101,235_0077.jpg,282,300,3,253800,uint8,umbrella,jpg,235 +235.umbrella-101,235_0018.jpg,294,350,3,308700,uint8,umbrella,jpg,235 +235.umbrella-101,235_0058.jpg,300,300,3,270000,uint8,umbrella,jpg,235 +235.umbrella-101,235_0113.jpg,237,300,3,213300,uint8,umbrella,jpg,235 +235.umbrella-101,235_0099.jpg,258,300,3,232200,uint8,umbrella,jpg,235 +235.umbrella-101,235_0025.jpg,619,820,3,1522740,uint8,umbrella,jpg,235 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+083.gas-pump,083_0063.jpg,431,575,3,743475,uint8,gas-pump,jpg,83 +083.gas-pump,083_0087.jpg,116,200,3,69600,uint8,gas-pump,jpg,83 +083.gas-pump,083_0037.jpg,475,379,3,540075,uint8,gas-pump,jpg,83 +083.gas-pump,083_0094.jpg,435,656,3,856080,uint8,gas-pump,jpg,83 +083.gas-pump,083_0013.jpg,314,200,3,188400,uint8,gas-pump,jpg,83 +083.gas-pump,083_0074.jpg,300,162,3,145800,uint8,gas-pump,jpg,83 +083.gas-pump,083_0064.jpg,246,192,3,141696,uint8,gas-pump,jpg,83 +083.gas-pump,083_0081.jpg,329,410,3,404670,uint8,gas-pump,jpg,83 +083.gas-pump,083_0018.jpg,613,397,3,730083,uint8,gas-pump,jpg,83 diff --git a/train_val_split/train_val_split.ipynb b/train_val_split/train_val_split.ipynb new file mode 100644 index 0000000..c09d999 --- /dev/null +++ b/train_val_split/train_val_split.ipynb @@ -0,0 +1,349 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from scipy import misc\n", + "\n", + "import os\n", + "import shutil\n", + "from tqdm import tqdm " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Delete rubbish" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# the folder from 256_ObjectCategories.tar file\n", + "dir_train = '/home/dongsung/Downloads/caltech-256-image-dataset/256_ObjectCategories'" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# remove non-images\n", + "os.remove(os.path.join(dir_train, '198.spider/RENAME2'))\n", + "shutil.rmtree(os.path.join(dir_train, '056.dog/greg'))\n", + "# we don't need the class with noise\n", + "#shutil.rmtree(os.path.join(dir_train, '257.clutter'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Collect metadata" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/257 [00:00 class name\n", + "decode = {n: i for i, n in M.groupby('category_name').category_number.first().iteritems()}" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "np.save('decode.npy', decode)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Split data" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# 20 images per class\n", + "V = M.groupby('category_name', group_keys=False).apply(lambda x: x.sample(n=20, replace=False))\n", + "V.sort_index(inplace=True)\n", + "M.drop(V.index, axis=0, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "25467" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# train data\n", + "len(M)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5140" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# validation data\n", + "len(V)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Save split" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "M.to_csv('train_metadata.csv', index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "V.to_csv('val_metadata.csv', index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/train_val_split/val_metadata.csv b/train_val_split/val_metadata.csv new file mode 100644 index 0000000..84f6853 --- /dev/null +++ b/train_val_split/val_metadata.csv @@ -0,0 +1,5141 @@ +directory,img_name,height,width,channels,byte_size,bit_depth,category_name,img_extension,category_number +128.lathe,128_0052.jpg,362,400,3,434400,uint8,lathe,jpg,128 +128.lathe,128_0096.jpg,480,640,3,921600,uint8,lathe,jpg,128 +128.lathe,128_0028.jpg,330,430,3,425700,uint8,lathe,jpg,128 +128.lathe,128_0078.jpg,200,200,3,120000,uint8,lathe,jpg,128 +128.lathe,128_0098.jpg,640,480,3,921600,uint8,lathe,jpg,128 +128.lathe,128_0066.jpg,480,720,3,1036800,uint8,lathe,jpg,128 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--git a/train_xception.ipynb b/train_xception.ipynb new file mode 100644 index 0000000..74b812d --- /dev/null +++ b/train_xception.ipynb @@ -0,0 +1,361 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "import keras\n", + "from keras import optimizers\n", + "from keras.losses import categorical_crossentropy as logloss\n", + "from keras.callbacks import ReduceLROnPlateau, EarlyStopping\n", + "from keras.preprocessing.image import ImageDataGenerator\n", + "from keras import backend as K\n", + "\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "from xception import Xception, preprocess_input\n", + "#Dongsung's local change\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "data_dir = '/home/dongsung/Downloads/caltech-256-image-dataset/temp/'" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 0 images belonging to 0 classes.\n", + "Found 0 images belonging to 0 classes.\n" + ] + } + ], + "source": [ + "data_generator = ImageDataGenerator(\n", + " rotation_range=30, \n", + " zoom_range=0.3,\n", + " horizontal_flip=True, \n", + " width_shift_range=0.2,\n", + " height_shift_range=0.2,\n", + " shear_range=0.001,\n", + " channel_shift_range=0.1,\n", + " fill_mode='reflect',\n", + " data_format='channels_last',\n", + " preprocessing_function=preprocess_input\n", + ")\n", + "\n", + "data_generator_val = ImageDataGenerator(\n", + " data_format='channels_last',\n", + " preprocessing_function=preprocess_input\n", + ")\n", + "\n", + "train_generator = data_generator.flow_from_directory(\n", + " data_dir + 'train_no_resizing', \n", + " target_size=(299, 299),\n", + " batch_size=64\n", + ")\n", + "\n", + "val_generator = data_generator_val.flow_from_directory(\n", + " data_dir + 'val', shuffle=False,\n", + " target_size=(299, 299),\n", + " batch_size=64\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Model" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "21386024" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = Xception(weight_decay=1e-5)\n", + "model.count_params()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Training" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# add entropy to the usual logloss (it is for regularization),\n", + "# \"Regularizing Neural Networks by Penalizing Confident Output Distributions\",\n", + "# https://arxiv.org/abs/1701.06548\n", + "# it reduces overfitting a little bit\n", + "def loss(y_true, y_pred):\n", + " entropy = -K.mean(K.sum(y_pred*K.log(y_pred), 1))\n", + " beta = 0.1\n", + " return logloss(y_true, y_pred) - beta*entropy" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "model.compile(\n", + " optimizer=optimizers.SGD(lr=1e-2, momentum=0.9, nesterov=True), \n", + " loss=loss, metrics=['categorical_crossentropy', 'accuracy', 'top_k_categorical_accuracy']\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/dongsung/tensorflow_virtualenv/lib/python3.5/site-packages/keras/callbacks.py:1065: UserWarning: `epsilon` argument is deprecated and will be removed, use `min_delta` instead.\n", + " warnings.warn('`epsilon` argument is deprecated and '\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/30\n" + ] + }, + { + "ename": "ZeroDivisionError", + "evalue": "integer division or modulo by zero", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mZeroDivisionError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mEarlyStopping\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmonitor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'val_acc'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpatience\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_delta\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.01\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m ],\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_generator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m80\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mworkers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 9\u001b[0m )\n", + "\u001b[0;32m~/tensorflow_virtualenv/lib/python3.5/site-packages/keras/legacy/interfaces.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 89\u001b[0m warnings.warn('Update your `' + object_name + '` call to the ' +\n\u001b[1;32m 90\u001b[0m 'Keras 2 API: ' + signature, stacklevel=2)\n\u001b[0;32m---> 91\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 92\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_function\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 93\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/tensorflow_virtualenv/lib/python3.5/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m 1416\u001b[0m \u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1417\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1418\u001b[0;31m initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m 1419\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1420\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0minterfaces\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegacy_generator_methods_support\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/tensorflow_virtualenv/lib/python3.5/site-packages/keras/engine/training_generator.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m 179\u001b[0m \u001b[0mbatch_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 180\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0msteps_done\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 181\u001b[0;31m \u001b[0mgenerator_output\u001b[0m \u001b[0;34m=\u001b[0m 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"\u001b[0;32m~/tensorflow_virtualenv/lib/python3.5/site-packages/keras_preprocessing/image.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1524\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1525\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__next__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1526\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1527\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1528\u001b[0m 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lock\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1969\u001b[0m \u001b[0;31m# so it can be done in parallel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/tensorflow_virtualenv/lib/python3.5/site-packages/keras_preprocessing/image.py\u001b[0m in \u001b[0;36m_flow_index\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1509\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_index_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1510\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1511\u001b[0;31m \u001b[0mcurrent_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatch_index\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1512\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mcurrent_index\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1513\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatch_index\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mZeroDivisionError\u001b[0m: integer division or modulo by zero" + ] + } + ], + "source": [ + "model.fit_generator(\n", + " train_generator, \n", + " steps_per_epoch=266, epochs=30, verbose=1,\n", + " callbacks=[\n", + " ReduceLROnPlateau(monitor='val_acc', factor=0.1, patience=2, epsilon=0.007),\n", + " EarlyStopping(monitor='val_acc', patience=4, min_delta=0.01)\n", + " ],\n", + " validation_data=val_generator, validation_steps=80, workers=4\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Loss/epoch plots" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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burni16+z4YDNS2iMCY6FQjSLc8Pyv4Ob1sGci+D578NvPwyVb4/70XNKs3ns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\ndC7KWrIAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(model.history.history['categorical_crossentropy'], label='train');\n", + "plt.plot(model.history.history['val_categorical_crossentropy'], label='val');\n", + "plt.legend();\n", + "plt.xlabel('epoch');\n", + "plt.ylabel('logloss');" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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/AH0dQZuOuvoG2Fffof4EEYk5oTYf7TCzzcB/A51DJ51zT0zC5y8CLgJKgZfM\n7MzAdZb8n3MfcB/AmjVr3Gl+ZmiqNkF6HpS9f8ylnYfb8Tn1J4hI7Ak1KaQBjcDFAeccMFFSqAXm\nBrwu9Z8LVAP8j3OuH9hvZrvxksS2EOMKj4Feb1XU5RsgcezEtKo6b+TRSq15JCIxJtQZzaH2IwTa\nBiwys3K8ZHAt3j7PgZ4ErgN+bmaFeM1J+07hsybXu89Db1vQCWsAFbVt5GUkMztXK6OKSGwJdUbz\nz/HuDEZwzn1uvJ9xzg2Y2U3AFiAR+JlzrtLMvglsd85t9l/7oJlVAYPA3zrnGk/h95hcVZsgLRfK\nLwx6uaKulZUludiofZpFRKa7UJuPfhNwnAZcA9Sd6Iecc08DT486d2fAsQP+xv+IDgN9sOspWPIh\nSBq7OnjfgI/dR9u54YLYX+FDROJPqM1Hjwe+NrOHgD+GJaJI2/+St/VmkFFHALuPttM/6NTJLCIx\nKdQZzaMtAqJrotlkqXoSUrJh4V8Evzy0h4I6mUUkBoXap9DOyD6FI3h7LMSWwX545zew5HJICj5T\nuaKulazUJObnZ0xxcCIi4Rdq81F2uAOJCgdehu5mWBF81BF4eygsn51DQoI6mUUk9oTUfGRm15hZ\nbsDrGWY2fs05XVVtgpQsWHhx0MuDPkdVXZt2WhORmBVqn8JdzrnWoRf+Gcd3hSekCBkcgJ2/gcV/\nCcnpQYvsb+igu39Q/QkiErNCTQrByoU6nHV6OPQKdDWMO+oIvKYjgJXaWEdEYlSoSWG7mX3HzBb6\nH98BXgtnYFOu8klIzoAzLhu3SEVtKylJCSwsOuFWEiIi01KoSeErQB/wCPAw0AN8OVxBTTnfIOz8\nNSz6IKSMP6qosq6NZbOySU481ZG8IiLRLdTRR51A0J3TYsKhV6Hz2IRNR845Kmpb+dCqOVMYmIjI\n1Ap19NGzZjYj4HWemW0JX1hTrGoTJKV5dwrjqGnupq1nQP0JIhLTQm0HKQzc48C/U1pszGj2+WDn\nZjjjUkgdv6+g0r9ctjbWEZFYFmpS8JnZvKEXZlZGkFVTp6WabdB+GFZcM2Gxito2EhOMpbPiYx6f\niMSnUIeV/gPwRzN7ETDg/cCNYYtqKlU9CYmpEzYdgXensKg4i7TkxCkKTERk6oV0p+Cc+x2wBtgF\nPAR8FegOY1xTw+fz+hPOuATSJu4rqNBMZhGJA6EuiPd54Ba8LTXfANYBWxm5Pef0U/c6tNXCJXdO\nWOxYWw/gX3b2AAANxklEQVT17b2sVH+CiMS4UPsUbgHOAw465/4COBtomfhHpoGqJyEhGRavn7DY\n0Exm7aEgIrEu1KTQ45zrATCzVOfcO8CS8IU1BZzzmo4WXgzpMyYsOjTySM1HIhLrQk0KNf55Ck8C\nz5rZJuBg+MKaAnU7oOXQhBPWhlTUtlFWkEF2WvIUBCYiEjmhzmgeGq95t5k9D+QCvwtbVFOhahMk\nJHkb6pxARV0rZ82d+G5CRCQWnPQiPs65F51zm51zfeEIaEoMNR2VXwgZ+RMWbe3qp6a5W53MIhIX\n4nNltyNvQ/P+kJqOjs9kVn+CiMS++EwKVZvAEmHph09YVCOPRCSexF9ScM4bilr+fsgsOGHxirpW\nZuemUZCVOgXBiYhEVvwlhWNV0Lg3pKYj8O4UtAieiMSL+EsKVZvAEkJqOurqG+Dd+g41HYlI3IjP\npDD/fMg68crfOw+34RysLNGdgojEh/hKCsfegfp3TqrpCNDGOiISN8KaFMxsvZntMrO9ZjZmO08z\n+6yZ1ZvZG/7H58MZD1WbAINlV4ZUvKK2lfzMFGblpIU1LBGRaBHqfgonzcwSgXuBy4AaYJuZbXbO\nVY0q+ohz7qZwxTFC1SaY917InhVSca+TOQczC3NgIiLRIZx3CmuBvc65ff7Zzw8DobXbhEPDHjhW\nGXLTUd+Aj91H2zXySETiSjiTQglQHfC6xn9utI+a2Vtm9piZzQ1bNFWbvOflG0IqvvtoO/2DTv0J\nIhJXIt3R/GugzDm3CngWuD9YITO70cy2m9n2+vr6U/uks6+Hj/0ccuaEVPz48ha6UxCR+BHOpFAL\nBH7zL/WfG+aca3TO9fpf/hQ4N9gbOefuc86tcc6tKSoqOrVosmfByo+EXLyito2s1CTm52ec2ueJ\niExD4UwK24BFZlZuZinAtcDmwAJmNjvg5QZgZxjjOSmVda0sn5NDQoI6mUUkfoQtKTjnBoCbgC14\nlf2jzrlKM/ummQ017N9sZpVm9iZwM/DZcMVzMgZ9jp2H2zWTWUTiTtiGpAI4554Gnh517s6A4zuA\nO8IZw6nY39BBd/+g9lAQkbgT6Y7mqFRR618uWyOPRCTOKCkEUVnXSmpSAmcUZUU6FBGRKaWkEERF\nbRtLZ2WTlKg/HhGJL6r1RnHOUVnXygqtjCoicUhJYZSa5m7aegbUySwicUlJYZSK2qGZzOpkFpH4\no6QwSmVdG4kJxpJZ2ZEORURkyikpjFJR18qi4izSkhMjHYqIyJRTUhjF20NB/QkiEp+UFAIca+uh\nvr1X/QkiEreUFAJU+JfLXqnhqCISp5QUAlT6l7dYrjsFEYlTSgoBKupaKS/MJCs1rOsEiohELSWF\nAF4ns+4SRCR+KSn4tXT1UdPcrZFHIhLXlBT8quq8/oSVWi5bROKYkoLf0Mgj3SmISDxTUvCrqG1j\nTm4a+ZkpkQ5FRCRilBT8tFy2iIiSAgCdvQPsa+jUyCMRiXtKCsA7R9pwDu2hICJxT0kBrz8BYIVG\nHolInFNSwOtPKMhMYVZOWqRDERGJKCUFvDuFFSW5mFmkQxERiai4Twq9A4PsPtquTmYREZQU2HO0\ngwGfUyeziAhKClTUDs1k1p2CiEjcJ4XKujayU5OYl58R6VBERCIu7pNCRV0ry+bkkJCgTmYRkbAm\nBTNbb2a7zGyvmd0+QbmPmpkzszXhjGe0QZ9j5+E29SeIiPiFLSmYWSJwL3A5sBy4zsyWBymXDdwC\n/E+4YhnPvvoOevp9Wi5bRMQvnHcKa4G9zrl9zrk+4GHgqiDl/gn4V6AnjLEEpeWyRURGCmdSKAGq\nA17X+M8NM7NzgLnOuafCGMe4KmvbSE1KYGFRZiQ+XkQk6kSso9nMEoDvAF8NoeyNZrbdzLbX19dP\nWgwVda0snZ1DUmLc97eLiADhTQq1wNyA16X+c0OygZXAC2Z2AFgHbA7W2eycu885t8Y5t6aoqGhS\ngnPOUVnXxkrNTxARGRbOpLANWGRm5WaWAlwLbB666Jxrdc4VOufKnHNlwKvABufc9jDGNKy6qZv2\nngH1J4iIBAhbUnDODQA3AVuAncCjzrlKM/ummW0I1+eGaqiTWSOPRESOSwrnmzvnngaeHnXuznHK\nXhTOWEarrGslKcFYPDN7Kj9WRCSqxW0Pa0VtG2cUZ5GWnBjpUEREokZcJgWvk7mVlSXqTxARCRSX\nSeFYey8NHX1aGVVEZJS4TAqVw53MulMQEQkUl0mhorYNM1g2W3cKIiKB4jQptFJekElWalgHX4mI\nTDtxmRQq69pYoaYjEZEx4i4pNHf2UdvSrU5mEZEg4i4pVB1uA9DGOiIiQcRdUqioHdpDQXcKIiKj\nxV1SqKxro2RGOnmZKZEORUQk6sRdUqioa2W57hJERIKKq6TQ2TvA/oZO9SeIiIwjrpLCzsNtOKfl\nskVExhNXSeF4J7PuFEREgomrpFBZ10ZhVgozc1IjHYqISFSKq6RQUdfG8jm5mFmkQxERiUpxkxR6\nBwbZc7SdlRp5JCIyrrhJCruPdDDgc+pPEBGZQNwkhYrhPRR0pyAiMp64SQoFmSlctnwmc/MyIh2K\niEjUipsNBT64YhYfXDEr0mGIiES1uLlTEBGRE1NSEBGRYUoKIiIyTElBRESGKSmIiMgwJQURERmm\npCAiIsOUFEREZJg55yIdw0kxs3rg4Cn+eCHQMInhhNt0inc6xQrTK97pFCtMr3inU6xwevHOd84V\nnajQtEsKp8PMtjvn1kQ6jlBNp3inU6wwveKdTrHC9Ip3OsUKUxOvmo9ERGSYkoKIiAyLt6RwX6QD\nOEnTKd7pFCtMr3inU6wwveKdTrHCFMQbV30KIiIysXi7UxARkQnETVIws/VmtsvM9prZ7ZGOZzxm\nNtfMnjezKjOrNLNbIh1TKMws0cx2mNlvIh3LRMxshpk9ZmbvmNlOM3tvpGOaiJnd5v93UGFmD5lZ\nWqRjCmRmPzOzY2ZWEXAu38yeNbM9/ue8SMY4ZJxY/83/b+EtM/uVmc2IZIxDgsUacO2rZubMrDAc\nnx0XScHMEoF7gcuB5cB1ZrY8slGNawD4qnNuObAO+HIUxxroFmBnpIMIwfeA3znnlgJnEcUxm1kJ\ncDOwxjm3EkgEro1sVGP8Alg/6tztwO+dc4uA3/tfR4NfMDbWZ4GVzrlVwG7gjqkOahy/YGysmNlc\n4IPAoXB9cFwkBWAtsNc5t8851wc8DFwV4ZiCcs4dds697j9ux6u0SiIb1cTMrBT4EPDTSMcyETPL\nBT4A/AeAc67POdcS2ahOKAlIN7MkIAOoi3A8IzjnXgKaRp2+Crjff3w/cPWUBjWOYLE6555xzg34\nX74KlE55YEGM8+cK8F3g74CwdQbHS1IoAaoDXtcQ5RUtgJmVAWcD/xPZSE7o3/H+ofoiHcgJlAP1\nwM/9TV0/NbPMSAc1HudcLfBtvG+Fh4FW59wzkY0qJDOdc4f9x0eAmZEM5iR8DvhtpIMYj5ldBdQ6\n594M5+fES1KYdswsC3gcuNU51xbpeMZjZh8GjjnnXot0LCFIAs4BfuicOxvoJHqaNsbwt8VfhZfM\n5gCZZnZ9ZKM6Oc4b3hj1Qxz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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(model.history.history['acc'], label='train');\n", + "plt.plot(model.history.history['val_acc'], label='val');\n", + "plt.legend();\n", + "plt.xlabel('epoch');\n", + "plt.ylabel('accuracy');" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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dfV/Wosqh5t7ieeoxiEieGul+DAeBg8DVExNO7vXWSFpSrcQgIvlp2k45HStV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\nMrNPAfeaWQHQDdxEcGOfs8P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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(model.history.history['top_k_categorical_accuracy'], label='train');\n", + "plt.plot(model.history.history['val_top_k_categorical_accuracy'], label='val');\n", + "plt.legend();\n", + "plt.xlabel('epoch');\n", + "plt.ylabel('top5_accuracy');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Results" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0.65727720120921729, 0.70469321161508558, 0.8232421875, 0.94746093750000004]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.evaluate_generator(val_generator, 80)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "model.save_weights('xception_weights.hdf5')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/utils.py b/utils.py new file mode 100644 index 0000000..7458236 --- /dev/null +++ b/utils.py @@ -0,0 +1,14 @@ +from keras.datasets import cifar10 +import tensorflow as tf +import numpy as np + +def get_cifar10(): + (x_train, y_train), (x_test, y_test) = cifar10.load_data()# + + return (x_train, y_train), (x_test, y_test) + +# train_data, test_data = get_cifar10() +# x_train, y_train = train_data +# x_test, y_test = test_data +# +# print (np.shape(x_train)) diff --git a/utils/__pycache__/image_preprocessing_ver1.cpython-35.pyc 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zgkW~e%UArlVn!7k%nkpya!BuM?-cNcFaL*2G|RGZ+^7VR70RCq?mkAu@t(6Ig*y5+Gtn&smAZL&D3vgI}JNX{rdlxWuU%n5&w#_iqjA6R{+FqJrj1b&+ ztG7Fej2#^?vA@Ryd){v7uXv9+56D+;epM&G;}j=)FQgP>0K#5b_egc!5?Y*rxNiQ1 zv*Q+@(YuEr7ox#9624U#hgMFY)?mk>jlPo8-~c=JZ7>Wzl*B*V0dXTedG9D2z68a~{|RZ9&FFw;BD zKv6Tkla=BB@O$J`z$w current_index + batch_size: + current_batch_size = batch_size + self.batch_index += 1 + else: + current_batch_size = n - current_index + self.batch_index = 0 + self.total_batches_seen += 1 + yield (index_array[current_index: current_index + current_batch_size], + current_index, current_batch_size) + + def __iter__(self): + # Needed if we want to do something like: + # for x, y in data_gen.flow(...): + return self + + def __next__(self, *args, **kwargs): + return self.next(*args, **kwargs) + + +class NumpyArrayIterator(Iterator): + """Iterator yielding data from a Numpy array. + + # Arguments + x: Numpy array of input data. + y: Numpy array of targets data. + image_data_generator: Instance of `ImageDataGenerator` + to use for random transformations and normalization. + batch_size: Integer, size of a batch. + shuffle: Boolean, whether to shuffle the data between epochs. + seed: Random seed for data shuffling. + data_format: String, one of `channels_first`, `channels_last`. + save_to_dir: Optional directory where to save the pictures + being yielded, in a viewable format. This is useful + for visualizing the random transformations being + applied, for debugging purposes. + save_prefix: String prefix to use for saving sample + images (if `save_to_dir` is set). + save_format: Format to use for saving sample images + (if `save_to_dir` is set). + """ + + def __init__(self, x, y, image_data_generator, + batch_size=32, shuffle=False, seed=None, + data_format=None, + save_to_dir=None, save_prefix='', save_format='png'): + if y is not None and len(x) != len(y): + raise ValueError('X (images tensor) and y (labels) ' + 'should have the same length. ' + 'Found: X.shape = %s, y.shape = %s' % + (np.asarray(x).shape, np.asarray(y).shape)) + + if data_format is None: + data_format = K.image_data_format() + self.x = np.asarray(x, dtype=K.floatx()) + + if self.x.ndim != 4: + raise ValueError('Input data in `NumpyArrayIterator` ' + 'should have rank 4. You passed an array ' + 'with shape', self.x.shape) + channels_axis = 3 if data_format == 'channels_last' else 1 + if self.x.shape[channels_axis] not in {1, 3, 4}: + raise ValueError('NumpyArrayIterator is set to use the ' + 'data format convention "' + data_format + '" ' + '(channels on axis ' + str(channels_axis) + '), i.e. expected ' + 'either 1, 3 or 4 channels on axis ' + str(channels_axis) + '. ' + 'However, it was passed an array with shape ' + str(self.x.shape) + + ' (' + str(self.x.shape[channels_axis]) + ' channels).') + if y is not None: + self.y = np.asarray(y) + else: + self.y = None + self.image_data_generator = image_data_generator + self.data_format = data_format + self.save_to_dir = save_to_dir + self.save_prefix = save_prefix + self.save_format = save_format + super(NumpyArrayIterator, self).__init__(x.shape[0], batch_size, shuffle, seed) + + def next(self): + """For python 2.x. + + # Returns + The next batch. + """ + # Keeps under lock only the mechanism which advances + # the indexing of each batch. + with self.lock: + index_array, current_index, current_batch_size = next(self.index_generator) + # The transformation of images is not under thread lock + # so it can be done in parallel + batch_x = np.zeros(tuple([current_batch_size] + list(self.x.shape)[1:]), dtype=K.floatx()) + for i, j in enumerate(index_array): + x = self.x[j] + x = self.image_data_generator.random_transform(x.astype(K.floatx())) + x = self.image_data_generator.standardize(x) + batch_x[i] = x + if self.save_to_dir: + for i in range(current_batch_size): + img = array_to_img(batch_x[i], self.data_format, scale=True) + fname = '{prefix}_{index}_{hash}.{format}'.format(prefix=self.save_prefix, + index=current_index + i, + hash=np.random.randint(1e4), + format=self.save_format) + img.save(os.path.join(self.save_to_dir, fname)) + if self.y is None: + return batch_x + batch_y = self.y[index_array] + return batch_x, batch_y + + +def _count_valid_files_in_directory(directory, white_list_formats, follow_links): + """Count files with extension in `white_list_formats` contained in a directory. + + # Arguments + directory: absolute path to the directory containing files to be counted + white_list_formats: set of strings containing allowed extensions for + the files to be counted. + + # Returns + the count of files with extension in `white_list_formats` contained in + the directory. + """ + def _recursive_list(subpath): + return sorted(os.walk(subpath, followlinks=follow_links), key=lambda tpl: tpl[0]) + + samples = 0 + for root, _, files in _recursive_list(directory): + for fname in files: + is_valid = False + for extension in white_list_formats: + if fname.lower().endswith('.' + extension): + is_valid = True + break + if is_valid: + samples += 1 + return samples + + +def _list_valid_filenames_in_directory(directory, white_list_formats, + class_indices, follow_links): + """List paths of files in `subdir` relative from `directory` whose extensions are in `white_list_formats`. + + # Arguments + directory: absolute path to a directory containing the files to list. + The directory name is used as class label and must be a key of `class_indices`. + white_list_formats: set of strings containing allowed extensions for + the files to be counted. + class_indices: dictionary mapping a class name to its index. + + # Returns + classes: a list of class indices + filenames: the path of valid files in `directory`, relative from + `directory`'s parent (e.g., if `directory` is "dataset/class1", + the filenames will be ["class1/file1.jpg", "class1/file2.jpg", ...]). + """ + def _recursive_list(subpath): + return sorted(os.walk(subpath, followlinks=follow_links), key=lambda tpl: tpl[0]) + + classes = [] + filenames = [] + subdir = os.path.basename(directory) + basedir = os.path.dirname(directory) + for root, _, files in _recursive_list(directory): + for fname in files: + is_valid = False + for extension in white_list_formats: + if fname.lower().endswith('.' + extension): + is_valid = True + break + if is_valid: + classes.append(class_indices[subdir]) + # add filename relative to directory + absolute_path = os.path.join(root, fname) + filenames.append(os.path.relpath(absolute_path, basedir)) + return classes, filenames + + +class DirectoryIterator(Iterator): + """Iterator capable of reading images from a directory on disk. + + # Arguments + directory: Path to the directory to read images from. + Each subdirectory in this directory will be + considered to contain images from one class, + or alternatively you could specify class subdirectories + via the `classes` argument. + image_data_generator: Instance of `ImageDataGenerator` + to use for random transformations and normalization. + target_size: tuple of integers, dimensions to resize input images to. + color_mode: One of `"rgb"`, `"grayscale"`. Color mode to read images. + classes: Optional list of strings, names of sudirectories + containing images from each class (e.g. `["dogs", "cats"]`). + It will be computed automatically if not set. + class_mode: Mode for yielding the targets: + `"binary"`: binary targets (if there are only two classes), + `"categorical"`: categorical targets, + `"sparse"`: integer targets, + `"input"`: targets are images identical to input images (mainly + used to work with autoencoders), + `None`: no targets get yielded (only input images are yielded). + batch_size: Integer, size of a batch. + shuffle: Boolean, whether to shuffle the data between epochs. + seed: Random seed for data shuffling. + data_format: String, one of `channels_first`, `channels_last`. + save_to_dir: Optional directory where to save the pictures + being yielded, in a viewable format. This is useful + for visualizing the random transformations being + applied, for debugging purposes. + save_prefix: String prefix to use for saving sample + images (if `save_to_dir` is set). + save_format: Format to use for saving sample images + (if `save_to_dir` is set). + """ + + def __init__(self, directory, image_data_generator, + target_size=(256, 256), color_mode='rgb', + classes=None, class_mode='categorical', + batch_size=32, shuffle=True, seed=None, + data_format=None, + save_to_dir=None, save_prefix='', save_format='png', + follow_links=False): + if data_format is None: + data_format = K.image_data_format() + self.directory = directory + self.image_data_generator = image_data_generator + self.target_size = tuple(target_size) + if color_mode not in {'rgb', 'grayscale'}: + raise ValueError('Invalid color mode:', color_mode, + '; expected "rgb" or "grayscale".') + self.color_mode = color_mode + self.data_format = data_format + if self.color_mode == 'rgb': + if self.data_format == 'channels_last': + self.image_shape = self.target_size + (3,) + else: + self.image_shape = (3,) + self.target_size + else: + if self.data_format == 'channels_last': + self.image_shape = self.target_size + (1,) + else: + self.image_shape = (1,) + self.target_size + self.classes = classes + if class_mode not in {'categorical', 'binary', 'sparse', + 'input', None}: + raise ValueError('Invalid class_mode:', class_mode, + '; expected one of "categorical", ' + '"binary", "sparse", "input"' + ' or None.') + self.class_mode = class_mode + self.save_to_dir = save_to_dir + self.save_prefix = save_prefix + self.save_format = save_format + + white_list_formats = {'png', 'jpg', 'jpeg', 'bmp'} + + # first, count the number of samples and classes + self.samples = 0 + + if not classes: + classes = [] + for subdir in sorted(os.listdir(directory)): + if os.path.isdir(os.path.join(directory, subdir)): + classes.append(subdir) + self.num_class = len(classes) + self.class_indices = dict(zip(classes, range(len(classes)))) + + def _recursive_list(subpath): + return sorted(os.walk(subpath, followlinks=follow_links), key=lambda tpl: tpl[0]) + + pool = multiprocessing.pool.ThreadPool() + function_partial = partial(_count_valid_files_in_directory, + white_list_formats=white_list_formats, + follow_links=follow_links) + self.samples = sum(pool.map(function_partial, + (os.path.join(directory, subdir) + for subdir in classes))) + + print('Found %d images belonging to %d classes.' % (self.samples, self.num_class)) + + # second, build an index of the images in the different class subfolders + results = [] + + self.filenames = [] + self.classes = np.zeros((self.samples,), dtype='int32') + i = 0 + for dirpath in (os.path.join(directory, subdir) for subdir in classes): + results.append(pool.apply_async(_list_valid_filenames_in_directory, + (dirpath, white_list_formats, + self.class_indices, follow_links))) + for res in results: + classes, filenames = res.get() + self.classes[i:i + len(classes)] = classes + self.filenames += filenames + i += len(classes) + pool.close() + pool.join() + super(DirectoryIterator, self).__init__(self.samples, batch_size, shuffle, seed) + + def next(self): + """For python 2.x. + + # Returns + The next batch. + """ + with self.lock: + index_array, current_index, current_batch_size = next(self.index_generator) + # The transformation of images is not under thread lock + # so it can be done in parallel + batch_x = np.zeros((current_batch_size,) + self.image_shape, dtype=K.floatx()) + batch_fname = [] + grayscale = self.color_mode == 'grayscale' + # build batch of image data + for i, j in enumerate(index_array): + fname = self.filenames[j] + img = load_img(os.path.join(self.directory, fname), + grayscale=grayscale, + target_size=self.target_size) + x = img_to_array(img, data_format=self.data_format) + x = self.image_data_generator.random_transform(x) + x = self.image_data_generator.standardize(x) + batch_x[i] = x + batch_fname.append(fname) + # optionally save augmented images to disk for debugging purposes + if self.save_to_dir: + for i in range(current_batch_size): + img = array_to_img(batch_x[i], self.data_format, scale=True) + fname = '{prefix}_{index}_{hash}.{format}'.format(prefix=self.save_prefix, + index=current_index + i, + hash=np.random.randint(1e4), + format=self.save_format) + img.save(os.path.join(self.save_to_dir, fname)) + # build batch of labels + if self.class_mode == 'input': + batch_y = batch_x.copy() + elif self.class_mode == 'sparse': + batch_y = self.classes[index_array] + elif self.class_mode == 'binary': + batch_y = self.classes[index_array].astype(K.floatx()) + elif self.class_mode == 'categorical': + batch_y = np.zeros((len(batch_x), self.num_class), dtype=K.floatx()) + for i, label in enumerate(self.classes[index_array]): + batch_y[i, label] = 1. + else: + return batch_x + return batch_x, batch_y, batch_fname diff --git a/utils/image_preprocessing_ver2.py b/utils/image_preprocessing_ver2.py new file mode 100644 index 0000000..877e3c9 --- /dev/null +++ b/utils/image_preprocessing_ver2.py @@ -0,0 +1,1066 @@ +"""Fairly basic set of tools for real-time data augmentation on image data. +Can easily be extended to include new transformations, +new preprocessing methods, etc... +""" +from __future__ import absolute_import +from __future__ import print_function + +import numpy as np +import re +from scipy import linalg +import scipy.ndimage as ndi +from six.moves import range +import os +import threading +import warnings +import multiprocessing.pool +from functools import partial + +import keras.backend as K + +try: + from PIL import Image as pil_image +except ImportError: + pil_image = None + + +def random_rotation(x, rg, row_axis=1, col_axis=2, channel_axis=0, + fill_mode='nearest', cval=0.): + """Performs a random rotation of a Numpy image tensor. + + # Arguments + x: Input tensor. Must be 3D. + rg: Rotation range, in degrees. + row_axis: Index of axis for rows in the input tensor. + col_axis: Index of axis for columns in the input tensor. + channel_axis: Index of axis for channels in the input tensor. + fill_mode: Points outside the boundaries of the input + are filled according to the given mode + (one of `{'constant', 'nearest', 'reflect', 'wrap'}`). + cval: Value used for points outside the boundaries + of the input if `mode='constant'`. + + # Returns + Rotated Numpy image tensor. + """ + theta = np.pi / 180 * np.random.uniform(-rg, rg) + rotation_matrix = np.array([[np.cos(theta), -np.sin(theta), 0], + [np.sin(theta), np.cos(theta), 0], + [0, 0, 1]]) + + h, w = x.shape[row_axis], x.shape[col_axis] + transform_matrix = transform_matrix_offset_center(rotation_matrix, h, w) + x = apply_transform(x, transform_matrix, channel_axis, fill_mode, cval) + return x + + +def random_shift(x, wrg, hrg, row_axis=1, col_axis=2, channel_axis=0, + fill_mode='nearest', cval=0.): + """Performs a random spatial shift of a Numpy image tensor. + + # Arguments + x: Input tensor. Must be 3D. + wrg: Width shift range, as a float fraction of the width. + hrg: Height shift range, as a float fraction of the height. + row_axis: Index of axis for rows in the input tensor. + col_axis: Index of axis for columns in the input tensor. + channel_axis: Index of axis for channels in the input tensor. + fill_mode: Points outside the boundaries of the input + are filled according to the given mode + (one of `{'constant', 'nearest', 'reflect', 'wrap'}`). + cval: Value used for points outside the boundaries + of the input if `mode='constant'`. + + # Returns + Shifted Numpy image tensor. + """ + h, w = x.shape[row_axis], x.shape[col_axis] + tx = np.random.uniform(-hrg, hrg) * h + ty = np.random.uniform(-wrg, wrg) * w + translation_matrix = np.array([[1, 0, tx], + [0, 1, ty], + [0, 0, 1]]) + + transform_matrix = translation_matrix # no need to do offset + x = apply_transform(x, transform_matrix, channel_axis, fill_mode, cval) + return x + + +def random_shear(x, intensity, row_axis=1, col_axis=2, channel_axis=0, + fill_mode='nearest', cval=0.): + """Performs a random spatial shear of a Numpy image tensor. + + # Arguments + x: Input tensor. Must be 3D. + intensity: Transformation intensity. + row_axis: Index of axis for rows in the input tensor. + col_axis: Index of axis for columns in the input tensor. + channel_axis: Index of axis for channels in the input tensor. + fill_mode: Points outside the boundaries of the input + are filled according to the given mode + (one of `{'constant', 'nearest', 'reflect', 'wrap'}`). + cval: Value used for points outside the boundaries + of the input if `mode='constant'`. + + # Returns + Sheared Numpy image tensor. + """ + shear = np.random.uniform(-intensity, intensity) + shear_matrix = np.array([[1, -np.sin(shear), 0], + [0, np.cos(shear), 0], + [0, 0, 1]]) + + h, w = x.shape[row_axis], x.shape[col_axis] + transform_matrix = transform_matrix_offset_center(shear_matrix, h, w) + x = apply_transform(x, transform_matrix, channel_axis, fill_mode, cval) + return x + + +def random_zoom(x, zoom_range, row_axis=1, col_axis=2, channel_axis=0, + fill_mode='nearest', cval=0.): + """Performs a random spatial zoom of a Numpy image tensor. + + # Arguments + x: Input tensor. Must be 3D. + zoom_range: Tuple of floats; zoom range for width and height. + row_axis: Index of axis for rows in the input tensor. + col_axis: Index of axis for columns in the input tensor. + channel_axis: Index of axis for channels in the input tensor. + fill_mode: Points outside the boundaries of the input + are filled according to the given mode + (one of `{'constant', 'nearest', 'reflect', 'wrap'}`). + cval: Value used for points outside the boundaries + of the input if `mode='constant'`. + + # Returns + Zoomed Numpy image tensor. + + # Raises + ValueError: if `zoom_range` isn't a tuple. + """ + if len(zoom_range) != 2: + raise ValueError('`zoom_range` should be a tuple or list of two floats. ' + 'Received arg: ', zoom_range) + + if zoom_range[0] == 1 and zoom_range[1] == 1: + zx, zy = 1, 1 + else: + zx, zy = np.random.uniform(zoom_range[0], zoom_range[1], 2) + zoom_matrix = np.array([[zx, 0, 0], + [0, zy, 0], + [0, 0, 1]]) + + h, w = x.shape[row_axis], x.shape[col_axis] + transform_matrix = transform_matrix_offset_center(zoom_matrix, h, w) + x = apply_transform(x, transform_matrix, channel_axis, fill_mode, cval) + return x + + +def random_channel_shift(x, intensity, channel_axis=0): + x = np.rollaxis(x, channel_axis, 0) + min_x, max_x = np.min(x), np.max(x) + channel_images = [np.clip(x_channel + np.random.uniform(-intensity, intensity), min_x, max_x) + for x_channel in x] + x = np.stack(channel_images, axis=0) + x = np.rollaxis(x, 0, channel_axis + 1) + return x + + +def transform_matrix_offset_center(matrix, x, y): + o_x = float(x) / 2 + 0.5 + o_y = float(y) / 2 + 0.5 + offset_matrix = np.array([[1, 0, o_x], [0, 1, o_y], [0, 0, 1]]) + reset_matrix = np.array([[1, 0, -o_x], [0, 1, -o_y], [0, 0, 1]]) + transform_matrix = np.dot(np.dot(offset_matrix, matrix), reset_matrix) + return transform_matrix + + +def apply_transform(x, + transform_matrix, + channel_axis=0, + fill_mode='nearest', + cval=0.): + """Apply the image transformation specified by a matrix. + + # Arguments + x: 2D numpy array, single image. + transform_matrix: Numpy array specifying the geometric transformation. + channel_axis: Index of axis for channels in the input tensor. + fill_mode: Points outside the boundaries of the input + are filled according to the given mode + (one of `{'constant', 'nearest', 'reflect', 'wrap'}`). + cval: Value used for points outside the boundaries + of the input if `mode='constant'`. + + # Returns + The transformed version of the input. + """ + x = np.rollaxis(x, channel_axis, 0) + final_affine_matrix = transform_matrix[:2, :2] + final_offset = transform_matrix[:2, 2] + channel_images = [ndi.interpolation.affine_transform( + x_channel, + final_affine_matrix, + final_offset, + order=0, + mode=fill_mode, + cval=cval) for x_channel in x] + x = np.stack(channel_images, axis=0) + x = np.rollaxis(x, 0, channel_axis + 1) + return x + + +def flip_axis(x, axis): + x = np.asarray(x).swapaxes(axis, 0) + x = x[::-1, ...] + x = x.swapaxes(0, axis) + return x + + +def array_to_img(x, data_format=None, scale=True): + """Converts a 3D Numpy array to a PIL Image instance. + + # Arguments + x: Input Numpy array. + data_format: Image data format. + scale: Whether to rescale image values + to be within [0, 255]. + + # Returns + A PIL Image instance. + + # Raises + ImportError: if PIL is not available. + ValueError: if invalid `x` or `data_format` is passed. + """ + if pil_image is None: + raise ImportError('Could not import PIL.Image. ' + 'The use of `array_to_img` requires PIL.') + x = np.asarray(x, dtype=K.floatx()) + if x.ndim != 3: + raise ValueError('Expected image array to have rank 3 (single image). ' + 'Got array with shape:', x.shape) + + if data_format is None: + data_format = K.image_data_format() + if data_format not in {'channels_first', 'channels_last'}: + raise ValueError('Invalid data_format:', data_format) + + # Original Numpy array x has format (height, width, channel) + # or (channel, height, width) + # but target PIL image has format (width, height, channel) + if data_format == 'channels_first': + x = x.transpose(1, 2, 0) + if scale: + x = x + max(-np.min(x), 0) + x_max = np.max(x) + if x_max != 0: + x /= x_max + x *= 255 + if x.shape[2] == 3: + # RGB + return pil_image.fromarray(x.astype('uint8'), 'RGB') + elif x.shape[2] == 1: + # grayscale + return pil_image.fromarray(x[:, :, 0].astype('uint8'), 'L') + else: + raise ValueError('Unsupported channel number: ', x.shape[2]) + + +def img_to_array(img, data_format=None): + """Converts a PIL Image instance to a Numpy array. + + # Arguments + img: PIL Image instance. + data_format: Image data format. + + # Returns + A 3D Numpy array. + + # Raises + ValueError: if invalid `img` or `data_format` is passed. + """ + if data_format is None: + data_format = K.image_data_format() + if data_format not in {'channels_first', 'channels_last'}: + raise ValueError('Unknown data_format: ', data_format) + # Numpy array x has format (height, width, channel) + # or (channel, height, width) + # but original PIL image has format (width, height, channel) + x = np.asarray(img, dtype=K.floatx()) + if len(x.shape) == 3: + if data_format == 'channels_first': + x = x.transpose(2, 0, 1) + elif len(x.shape) == 2: + if data_format == 'channels_first': + x = x.reshape((1, x.shape[0], x.shape[1])) + else: + x = x.reshape((x.shape[0], x.shape[1], 1)) + else: + raise ValueError('Unsupported image shape: ', x.shape) + return x + + +def load_img(path, grayscale=False, target_size=None): + """Loads an image into PIL format. + + # Arguments + path: Path to image file + grayscale: Boolean, whether to load the image as grayscale. + target_size: Either `None` (default to original size) + or tuple of ints `(img_height, img_width)`. + + # Returns + A PIL Image instance. + + # Raises + ImportError: if PIL is not available. + """ + if pil_image is None: + raise ImportError('Could not import PIL.Image. ' + 'The use of `array_to_img` requires PIL.') + img = pil_image.open(path) + if grayscale: + if img.mode != 'L': + img = img.convert('L') + else: + if img.mode != 'RGB': + img = img.convert('RGB') + if target_size: + hw_tuple = (target_size[1], target_size[0]) + if img.size != hw_tuple: + img = img.resize(hw_tuple) + return img + + +def list_pictures(directory, ext='jpg|jpeg|bmp|png'): + return [os.path.join(root, f) + for root, _, files in os.walk(directory) for f in files + if re.match(r'([\w]+\.(?:' + ext + '))', f)] + + +class ImageDataGenerator(object): + """Generate minibatches of image data with real-time data augmentation. + + # Arguments + featurewise_center: set input mean to 0 over the dataset. + samplewise_center: set each sample mean to 0. + featurewise_std_normalization: divide inputs by std of the dataset. + samplewise_std_normalization: divide each input by its std. + zca_whitening: apply ZCA whitening. + zca_epsilon: epsilon for ZCA whitening. Default is 1e-6. + rotation_range: degrees (0 to 180). + width_shift_range: fraction of total width. + height_shift_range: fraction of total height. + shear_range: shear intensity (shear angle in radians). + zoom_range: amount of zoom. if scalar z, zoom will be randomly picked + in the range [1-z, 1+z]. A sequence of two can be passed instead + to select this range. + channel_shift_range: shift range for each channels. + fill_mode: points outside the boundaries are filled according to the + given mode ('constant', 'nearest', 'reflect' or 'wrap'). Default + is 'nearest'. + cval: value used for points outside the boundaries when fill_mode is + 'constant'. Default is 0. + horizontal_flip: whether to randomly flip images horizontally. + vertical_flip: whether to randomly flip images vertically. + rescale: rescaling factor. If None or 0, no rescaling is applied, + otherwise we multiply the data by the value provided. This is + applied after the `preprocessing_function` (if any provided) + but before any other transformation. + preprocessing_function: function that will be implied on each input. + The function will run before any other modification on it. + The function should take one argument: + one image (Numpy tensor with rank 3), + and should output a Numpy tensor with the same shape. + data_format: 'channels_first' or 'channels_last'. In 'channels_first' mode, the channels dimension + (the depth) is at index 1, in 'channels_last' mode it is at index 3. + It defaults to the `image_data_format` value found in your + Keras config file at `~/.keras/keras.json`. + If you never set it, then it will be "channels_last". + """ + + def __init__(self, + featurewise_center=False, + samplewise_center=False, + featurewise_std_normalization=False, + samplewise_std_normalization=False, + zca_whitening=False, + zca_epsilon=1e-6, + rotation_range=0., + width_shift_range=0., + height_shift_range=0., + shear_range=0., + zoom_range=0., + channel_shift_range=0., + fill_mode='nearest', + cval=0., + horizontal_flip=False, + vertical_flip=False, + rescale=None, + preprocessing_function=None, + data_format=None): + if data_format is None: + data_format = K.image_data_format() + self.featurewise_center = featurewise_center + self.samplewise_center = samplewise_center + self.featurewise_std_normalization = featurewise_std_normalization + self.samplewise_std_normalization = samplewise_std_normalization + self.zca_whitening = zca_whitening + self.zca_epsilon = zca_epsilon + self.rotation_range = rotation_range + self.width_shift_range = width_shift_range + self.height_shift_range = height_shift_range + self.shear_range = shear_range + self.zoom_range = zoom_range + self.channel_shift_range = channel_shift_range + self.fill_mode = fill_mode + self.cval = cval + self.horizontal_flip = horizontal_flip + self.vertical_flip = vertical_flip + self.rescale = rescale + self.preprocessing_function = preprocessing_function + + if data_format not in {'channels_last', 'channels_first'}: + raise ValueError('`data_format` should be `"channels_last"` (channel after row and ' + 'column) or `"channels_first"` (channel before row and column). ' + 'Received arg: ', data_format) + self.data_format = data_format + if data_format == 'channels_first': + self.channel_axis = 1 + self.row_axis = 2 + self.col_axis = 3 + if data_format == 'channels_last': + self.channel_axis = 3 + self.row_axis = 1 + self.col_axis = 2 + + self.mean = None + self.std = None + self.principal_components = None + + if np.isscalar(zoom_range): + self.zoom_range = [1 - zoom_range, 1 + zoom_range] + elif len(zoom_range) == 2: + self.zoom_range = [zoom_range[0], zoom_range[1]] + else: + raise ValueError('`zoom_range` should be a float or ' + 'a tuple or list of two floats. ' + 'Received arg: ', zoom_range) + + def flow(self, x, y=None, batch_size=32, shuffle=True, seed=None, + save_to_dir=None, save_prefix='', save_format='png'): + return NumpyArrayIterator( + x, y, self, + batch_size=batch_size, + shuffle=shuffle, + seed=seed, + data_format=self.data_format, + save_to_dir=save_to_dir, + save_prefix=save_prefix, + save_format=save_format) + + def flow_from_directory(self, directory, logits_dict, + target_size=(256, 256), color_mode='rgb', + classes=None, class_mode='categorical', + batch_size=32, shuffle=True, seed=None, + save_to_dir=None, + save_prefix='', + save_format='png', + follow_links=False): + return DirectoryIterator( + directory, self, logits_dict, + target_size=target_size, color_mode=color_mode, + classes=classes, class_mode=class_mode, + data_format=self.data_format, + batch_size=batch_size, shuffle=shuffle, seed=seed, + save_to_dir=save_to_dir, + save_prefix=save_prefix, + save_format=save_format, + follow_links=follow_links) + + def standardize(self, x): + """Apply the normalization configuration to a batch of inputs. + + # Arguments + x: batch of inputs to be normalized. + + # Returns + The inputs, normalized. + """ + if self.preprocessing_function: + x = self.preprocessing_function(x) + if self.rescale: + x *= self.rescale + # x is a single image, so it doesn't have image number at index 0 + img_channel_axis = self.channel_axis - 1 + if self.samplewise_center: + x -= np.mean(x, axis=img_channel_axis, keepdims=True) + if self.samplewise_std_normalization: + x /= (np.std(x, axis=img_channel_axis, keepdims=True) + 1e-7) + + if self.featurewise_center: + if self.mean is not None: + x -= self.mean + else: + warnings.warn('This ImageDataGenerator specifies ' + '`featurewise_center`, but it hasn\'t' + 'been fit on any training data. Fit it ' + 'first by calling `.fit(numpy_data)`.') + if self.featurewise_std_normalization: + if self.std is not None: + x /= (self.std + 1e-7) + else: + warnings.warn('This ImageDataGenerator specifies ' + '`featurewise_std_normalization`, but it hasn\'t' + 'been fit on any training data. Fit it ' + 'first by calling `.fit(numpy_data)`.') + if self.zca_whitening: + if self.principal_components is not None: + flatx = np.reshape(x, (-1, np.prod(x.shape[-3:]))) + whitex = np.dot(flatx, self.principal_components) + x = np.reshape(whitex, x.shape) + else: + warnings.warn('This ImageDataGenerator specifies ' + '`zca_whitening`, but it hasn\'t' + 'been fit on any training data. Fit it ' + 'first by calling `.fit(numpy_data)`.') + return x + + def random_transform(self, x, seed=None): + """Randomly augment a single image tensor. + + # Arguments + x: 3D tensor, single image. + seed: random seed. + + # Returns + A randomly transformed version of the input (same shape). + """ + # x is a single image, so it doesn't have image number at index 0 + img_row_axis = self.row_axis - 1 + img_col_axis = self.col_axis - 1 + img_channel_axis = self.channel_axis - 1 + + if seed is not None: + np.random.seed(seed) + + # use composition of homographies + # to generate final transform that needs to be applied + if self.rotation_range: + theta = np.pi / 180 * np.random.uniform(-self.rotation_range, self.rotation_range) + else: + theta = 0 + + if self.height_shift_range: + tx = np.random.uniform(-self.height_shift_range, self.height_shift_range) * x.shape[img_row_axis] + else: + tx = 0 + + if self.width_shift_range: + ty = np.random.uniform(-self.width_shift_range, self.width_shift_range) * x.shape[img_col_axis] + else: + ty = 0 + + if self.shear_range: + shear = np.random.uniform(-self.shear_range, self.shear_range) + else: + shear = 0 + + if self.zoom_range[0] == 1 and self.zoom_range[1] == 1: + zx, zy = 1, 1 + else: + zx, zy = np.random.uniform(self.zoom_range[0], self.zoom_range[1], 2) + + transform_matrix = None + if theta != 0: + rotation_matrix = np.array([[np.cos(theta), -np.sin(theta), 0], + [np.sin(theta), np.cos(theta), 0], + [0, 0, 1]]) + transform_matrix = rotation_matrix + + if tx != 0 or ty != 0: + shift_matrix = np.array([[1, 0, tx], + [0, 1, ty], + [0, 0, 1]]) + transform_matrix = shift_matrix if transform_matrix is None else np.dot(transform_matrix, shift_matrix) + + if shear != 0: + shear_matrix = np.array([[1, -np.sin(shear), 0], + [0, np.cos(shear), 0], + [0, 0, 1]]) + transform_matrix = shear_matrix if transform_matrix is None else np.dot(transform_matrix, shear_matrix) + + if zx != 1 or zy != 1: + zoom_matrix = np.array([[zx, 0, 0], + [0, zy, 0], + [0, 0, 1]]) + transform_matrix = zoom_matrix if transform_matrix is None else np.dot(transform_matrix, zoom_matrix) + + if transform_matrix is not None: + h, w = x.shape[img_row_axis], x.shape[img_col_axis] + transform_matrix = transform_matrix_offset_center(transform_matrix, h, w) + x = apply_transform(x, transform_matrix, img_channel_axis, + fill_mode=self.fill_mode, cval=self.cval) + + if self.channel_shift_range != 0: + x = random_channel_shift(x, + self.channel_shift_range, + img_channel_axis) + if self.horizontal_flip: + if np.random.random() < 0.5: + x = flip_axis(x, img_col_axis) + + if self.vertical_flip: + if np.random.random() < 0.5: + x = flip_axis(x, img_row_axis) + + return x + + def fit(self, x, + augment=False, + rounds=1, + seed=None): + """Fits internal statistics to some sample data. + + Required for featurewise_center, featurewise_std_normalization + and zca_whitening. + + # Arguments + x: Numpy array, the data to fit on. Should have rank 4. + In case of grayscale data, + the channels axis should have value 1, and in case + of RGB data, it should have value 3. + augment: Whether to fit on randomly augmented samples + rounds: If `augment`, + how many augmentation passes to do over the data + seed: random seed. + + # Raises + ValueError: in case of invalid input `x`. + """ + x = np.asarray(x, dtype=K.floatx()) + if x.ndim != 4: + raise ValueError('Input to `.fit()` should have rank 4. ' + 'Got array with shape: ' + str(x.shape)) + if x.shape[self.channel_axis] not in {1, 3, 4}: + warnings.warn( + 'Expected input to be images (as Numpy array) ' + 'following the data format convention "' + self.data_format + '" ' + '(channels on axis ' + str(self.channel_axis) + '), i.e. expected ' + 'either 1, 3 or 4 channels on axis ' + str(self.channel_axis) + '. ' + 'However, it was passed an array with shape ' + str(x.shape) + + ' (' + str(x.shape[self.channel_axis]) + ' channels).') + + if seed is not None: + np.random.seed(seed) + + x = np.copy(x) + if augment: + ax = np.zeros(tuple([rounds * x.shape[0]] + list(x.shape)[1:]), dtype=K.floatx()) + for r in range(rounds): + for i in range(x.shape[0]): + ax[i + r * x.shape[0]] = self.random_transform(x[i]) + x = ax + + if self.featurewise_center: + self.mean = np.mean(x, axis=(0, self.row_axis, self.col_axis)) + broadcast_shape = [1, 1, 1] + broadcast_shape[self.channel_axis - 1] = x.shape[self.channel_axis] + self.mean = np.reshape(self.mean, broadcast_shape) + x -= self.mean + + if self.featurewise_std_normalization: + self.std = np.std(x, axis=(0, self.row_axis, self.col_axis)) + broadcast_shape = [1, 1, 1] + broadcast_shape[self.channel_axis - 1] = x.shape[self.channel_axis] + self.std = np.reshape(self.std, broadcast_shape) + x /= (self.std + K.epsilon()) + + if self.zca_whitening: + flat_x = np.reshape(x, (x.shape[0], x.shape[1] * x.shape[2] * x.shape[3])) + sigma = np.dot(flat_x.T, flat_x) / flat_x.shape[0] + u, s, _ = linalg.svd(sigma) + self.principal_components = np.dot(np.dot(u, np.diag(1. / np.sqrt(s + self.zca_epsilon))), u.T) + + +class Iterator(object): + """Abstract base class for image data iterators. + + # Arguments + n: Integer, total number of samples in the dataset to loop over. + batch_size: Integer, size of a batch. + shuffle: Boolean, whether to shuffle the data between epochs. + seed: Random seeding for data shuffling. + """ + + def __init__(self, n, batch_size, shuffle, seed): + self.n = n + self.batch_size = batch_size + self.shuffle = shuffle + self.batch_index = 0 + self.total_batches_seen = 0 + self.lock = threading.Lock() + self.index_generator = self._flow_index(n, batch_size, shuffle, seed) + + def reset(self): + self.batch_index = 0 + + def _flow_index(self, n, batch_size=32, shuffle=False, seed=None): + # Ensure self.batch_index is 0. + self.reset() + while 1: + if seed is not None: + np.random.seed(seed + self.total_batches_seen) + if self.batch_index == 0: + index_array = np.arange(n) + if shuffle: + index_array = np.random.permutation(n) + + current_index = (self.batch_index * batch_size) % n + if n > current_index + batch_size: + current_batch_size = batch_size + self.batch_index += 1 + else: + current_batch_size = n - current_index + self.batch_index = 0 + self.total_batches_seen += 1 + yield (index_array[current_index: current_index + current_batch_size], + current_index, current_batch_size) + + def __iter__(self): + # Needed if we want to do something like: + # for x, y in data_gen.flow(...): + return self + + def __next__(self, *args, **kwargs): + return self.next(*args, **kwargs) + + +class NumpyArrayIterator(Iterator): + """Iterator yielding data from a Numpy array. + + # Arguments + x: Numpy array of input data. + y: Numpy array of targets data. + image_data_generator: Instance of `ImageDataGenerator` + to use for random transformations and normalization. + batch_size: Integer, size of a batch. + shuffle: Boolean, whether to shuffle the data between epochs. + seed: Random seed for data shuffling. + data_format: String, one of `channels_first`, `channels_last`. + save_to_dir: Optional directory where to save the pictures + being yielded, in a viewable format. This is useful + for visualizing the random transformations being + applied, for debugging purposes. + save_prefix: String prefix to use for saving sample + images (if `save_to_dir` is set). + save_format: Format to use for saving sample images + (if `save_to_dir` is set). + """ + + def __init__(self, x, y, image_data_generator, + batch_size=32, shuffle=False, seed=None, + data_format=None, + save_to_dir=None, save_prefix='', save_format='png'): + if y is not None and len(x) != len(y): + raise ValueError('X (images tensor) and y (labels) ' + 'should have the same length. ' + 'Found: X.shape = %s, y.shape = %s' % + (np.asarray(x).shape, np.asarray(y).shape)) + + if data_format is None: + data_format = K.image_data_format() + self.x = np.asarray(x, dtype=K.floatx()) + + if self.x.ndim != 4: + raise ValueError('Input data in `NumpyArrayIterator` ' + 'should have rank 4. You passed an array ' + 'with shape', self.x.shape) + channels_axis = 3 if data_format == 'channels_last' else 1 + if self.x.shape[channels_axis] not in {1, 3, 4}: + raise ValueError('NumpyArrayIterator is set to use the ' + 'data format convention "' + data_format + '" ' + '(channels on axis ' + str(channels_axis) + '), i.e. expected ' + 'either 1, 3 or 4 channels on axis ' + str(channels_axis) + '. ' + 'However, it was passed an array with shape ' + str(self.x.shape) + + ' (' + str(self.x.shape[channels_axis]) + ' channels).') + if y is not None: + self.y = np.asarray(y) + else: + self.y = None + self.image_data_generator = image_data_generator + self.data_format = data_format + self.save_to_dir = save_to_dir + self.save_prefix = save_prefix + self.save_format = save_format + super(NumpyArrayIterator, self).__init__(x.shape[0], batch_size, shuffle, seed) + + def next(self): + """For python 2.x. + + # Returns + The next batch. + """ + # Keeps under lock only the mechanism which advances + # the indexing of each batch. + with self.lock: + index_array, current_index, current_batch_size = next(self.index_generator) + # The transformation of images is not under thread lock + # so it can be done in parallel + batch_x = np.zeros(tuple([current_batch_size] + list(self.x.shape)[1:]), dtype=K.floatx()) + for i, j in enumerate(index_array): + x = self.x[j] + x = self.image_data_generator.random_transform(x.astype(K.floatx())) + x = self.image_data_generator.standardize(x) + batch_x[i] = x + if self.save_to_dir: + for i in range(current_batch_size): + img = array_to_img(batch_x[i], self.data_format, scale=True) + fname = '{prefix}_{index}_{hash}.{format}'.format(prefix=self.save_prefix, + index=current_index + i, + hash=np.random.randint(1e4), + format=self.save_format) + img.save(os.path.join(self.save_to_dir, fname)) + if self.y is None: + return batch_x + batch_y = self.y[index_array] + return batch_x, batch_y + + +def _count_valid_files_in_directory(directory, white_list_formats, follow_links): + """Count files with extension in `white_list_formats` contained in a directory. + + # Arguments + directory: absolute path to the directory containing files to be counted + white_list_formats: set of strings containing allowed extensions for + the files to be counted. + + # Returns + the count of files with extension in `white_list_formats` contained in + the directory. + """ + def _recursive_list(subpath): + return sorted(os.walk(subpath, followlinks=follow_links), key=lambda tpl: tpl[0]) + + samples = 0 + for root, _, files in _recursive_list(directory): + for fname in files: + is_valid = False + for extension in white_list_formats: + if fname.lower().endswith('.' + extension): + is_valid = True + break + if is_valid: + samples += 1 + return samples + + +def _list_valid_filenames_in_directory(directory, white_list_formats, + class_indices, follow_links): + """List paths of files in `subdir` relative from `directory` whose extensions are in `white_list_formats`. + + # Arguments + directory: absolute path to a directory containing the files to list. + The directory name is used as class label and must be a key of `class_indices`. + white_list_formats: set of strings containing allowed extensions for + the files to be counted. + class_indices: dictionary mapping a class name to its index. + + # Returns + classes: a list of class indices + filenames: the path of valid files in `directory`, relative from + `directory`'s parent (e.g., if `directory` is "dataset/class1", + the filenames will be ["class1/file1.jpg", "class1/file2.jpg", ...]). + """ + def _recursive_list(subpath): + return sorted(os.walk(subpath, followlinks=follow_links), key=lambda tpl: tpl[0]) + + classes = [] + filenames = [] + subdir = os.path.basename(directory) + basedir = os.path.dirname(directory) + for root, _, files in _recursive_list(directory): + for fname in files: + is_valid = False + for extension in white_list_formats: + if fname.lower().endswith('.' + extension): + is_valid = True + break + if is_valid: + classes.append(class_indices[subdir]) + # add filename relative to directory + absolute_path = os.path.join(root, fname) + filenames.append(os.path.relpath(absolute_path, basedir)) + return classes, filenames + + +class DirectoryIterator(Iterator): + """Iterator capable of reading images from a directory on disk. + + # Arguments + directory: Path to the directory to read images from. + Each subdirectory in this directory will be + considered to contain images from one class, + or alternatively you could specify class subdirectories + via the `classes` argument. + image_data_generator: Instance of `ImageDataGenerator` + to use for random transformations and normalization. + target_size: tuple of integers, dimensions to resize input images to. + color_mode: One of `"rgb"`, `"grayscale"`. Color mode to read images. + classes: Optional list of strings, names of sudirectories + containing images from each class (e.g. `["dogs", "cats"]`). + It will be computed automatically if not set. + class_mode: Mode for yielding the targets: + `"binary"`: binary targets (if there are only two classes), + `"categorical"`: categorical targets, + `"sparse"`: integer targets, + `"input"`: targets are images identical to input images (mainly + used to work with autoencoders), + `None`: no targets get yielded (only input images are yielded). + batch_size: Integer, size of a batch. + shuffle: Boolean, whether to shuffle the data between epochs. + seed: Random seed for data shuffling. + data_format: String, one of `channels_first`, `channels_last`. + save_to_dir: Optional directory where to save the pictures + being yielded, in a viewable format. This is useful + for visualizing the random transformations being + applied, for debugging purposes. + save_prefix: String prefix to use for saving sample + images (if `save_to_dir` is set). + save_format: Format to use for saving sample images + (if `save_to_dir` is set). + """ + + def __init__(self, directory, image_data_generator, logits_dict, + target_size=(256, 256), color_mode='rgb', + classes=None, class_mode='categorical', + batch_size=32, shuffle=True, seed=None, + data_format=None, + save_to_dir=None, save_prefix='', save_format='png', + follow_links=False): + if data_format is None: + data_format = K.image_data_format() + self.directory = directory + self.image_data_generator = image_data_generator + self.logits_dict = logits_dict + self.target_size = tuple(target_size) + if color_mode not in {'rgb', 'grayscale'}: + raise ValueError('Invalid color mode:', color_mode, + '; expected "rgb" or "grayscale".') + self.color_mode = color_mode + self.data_format = data_format + if self.color_mode == 'rgb': + if self.data_format == 'channels_last': + self.image_shape = self.target_size + (3,) + else: + self.image_shape = (3,) + self.target_size + else: + if self.data_format == 'channels_last': + self.image_shape = self.target_size + (1,) + else: + self.image_shape = (1,) + self.target_size + self.classes = classes + if class_mode not in {'categorical', 'binary', 'sparse', + 'input', None}: + raise ValueError('Invalid class_mode:', class_mode, + '; expected one of "categorical", ' + '"binary", "sparse", "input"' + ' or None.') + self.class_mode = class_mode + self.save_to_dir = save_to_dir + self.save_prefix = save_prefix + self.save_format = save_format + + white_list_formats = {'png', 'jpg', 'jpeg', 'bmp'} + + # first, count the number of samples and classes + self.samples = 0 + + if not classes: + classes = [] + for subdir in sorted(os.listdir(directory)): + if os.path.isdir(os.path.join(directory, subdir)): + classes.append(subdir) + self.num_class = len(classes) + self.class_indices = dict(zip(classes, range(len(classes)))) + + def _recursive_list(subpath): + return sorted(os.walk(subpath, followlinks=follow_links), key=lambda tpl: tpl[0]) + + pool = multiprocessing.pool.ThreadPool() + function_partial = partial(_count_valid_files_in_directory, + white_list_formats=white_list_formats, + follow_links=follow_links) + self.samples = sum(pool.map(function_partial, + (os.path.join(directory, subdir) + for subdir in classes))) + + print('Found %d images belonging to %d classes.' % (self.samples, self.num_class)) + + # second, build an index of the images in the different class subfolders + results = [] + + self.filenames = [] + self.classes = np.zeros((self.samples,), dtype='int32') + i = 0 + for dirpath in (os.path.join(directory, subdir) for subdir in classes): + results.append(pool.apply_async(_list_valid_filenames_in_directory, + (dirpath, white_list_formats, + self.class_indices, follow_links))) + for res in results: + classes, filenames = res.get() + self.classes[i:i + len(classes)] = classes + self.filenames += filenames + i += len(classes) + pool.close() + pool.join() + super(DirectoryIterator, self).__init__(self.samples, batch_size, shuffle, seed) + + def next(self): + """For python 2.x. + + # Returns + The next batch. + """ + with self.lock: + index_array, current_index, current_batch_size = next(self.index_generator) + # The transformation of images is not under thread lock + # so it can be done in parallel + batch_x = np.zeros((current_batch_size,) + self.image_shape, dtype=K.floatx()) + batch_logits = np.zeros((current_batch_size, self.num_class), dtype=K.floatx()) + grayscale = self.color_mode == 'grayscale' + # build batch of image data + for i, j in enumerate(index_array): + fname = self.filenames[j] + img = load_img(os.path.join(self.directory, fname), + grayscale=grayscale, + target_size=self.target_size) + x = img_to_array(img, data_format=self.data_format) + x = self.image_data_generator.random_transform(x) + x = self.image_data_generator.standardize(x) + batch_x[i] = x + batch_logits[i] = self.logits_dict[fname] + # optionally save augmented images to disk for debugging purposes + if self.save_to_dir: + for i in range(current_batch_size): + img = array_to_img(batch_x[i], self.data_format, scale=True) + fname = '{prefix}_{index}_{hash}.{format}'.format(prefix=self.save_prefix, + index=current_index + i, + hash=np.random.randint(1e4), + format=self.save_format) + img.save(os.path.join(self.save_to_dir, fname)) + # build batch of labels + if self.class_mode == 'input': + batch_y = batch_x.copy() + elif self.class_mode == 'sparse': + batch_y = self.classes[index_array] + elif self.class_mode == 'binary': + batch_y = self.classes[index_array].astype(K.floatx()) + elif self.class_mode == 'categorical': + batch_y = np.zeros((len(batch_x), self.num_class), dtype=K.floatx()) + for i, label in enumerate(self.classes[index_array]): + batch_y[i, label] = 1. + else: + return batch_x + return batch_x, np.concatenate([batch_y, batch_logits], axis=1) diff --git a/utils/split_and_resize_data.ipynb b/utils/split_and_resize_data.ipynb new file mode 100644 index 0000000..1d91d9c --- /dev/null +++ b/utils/split_and_resize_data.ipynb @@ -0,0 +1,393 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "from PIL import Image, ImageEnhance\n", + "import torchvision.transforms as transforms\n", + "\n", + "import os\n", + "from tqdm import tqdm " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# the folder from 256_ObjectCategories.tar file\n", + "train_dir = '/home/ubuntu/data/256_ObjectCategories/'\n", + "\n", + "# a folder where resized and split data will be stored\n", + "data_dir = '/home/ubuntu/data/'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load constant train-val split" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "T = pd.read_csv('../train_val_split/train_metadata.csv')\n", + "V = pd.read_csv('../train_val_split/val_metadata.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Create directories for different categories" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "os.mkdir(data_dir + 'train')\n", + "for i in range(1, 256 + 1):\n", + " os.mkdir(data_dir + 'train/' + str(i))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "os.mkdir(data_dir + 'val')\n", + "for i in range(1, 256 + 1):\n", + " os.mkdir(data_dir + 'val/' + str(i))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Resize val. images" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "val_transform = transforms.Compose([\n", + " transforms.Scale(299, Image.LANCZOS),\n", + " transforms.CenterCrop(299)\n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5120" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "val_size = len(V)\n", + "val_size" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "5033it [01:24, 59.70it/s]\n" + ] + } + ], + "source": [ + "# resize RGB images\n", + "for i, row in tqdm(V.loc[V.channels == 3].iterrows()):\n", + " # get image\n", + " file_path = os.path.join(train_dir, row.directory, row.img_name)\n", + " image = Image.open(file_path)\n", + " \n", + " # transform it\n", + " image = val_transform(image)\n", + " \n", + " # save\n", + " save_path = os.path.join(data_dir, 'val', str(row.category_number), row.img_name)\n", + " image.save(save_path, 'jpeg')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "87it [00:01, 87.24it/s]\n" + ] + } + ], + "source": [ + "# resize grayscale images\n", + "for i, row in tqdm(V.loc[V.channels == 1].iterrows()):\n", + " # get image\n", + " file_path = os.path.join(train_dir, row.directory, row.img_name)\n", + " image = Image.open(file_path)\n", + " \n", + " # transform it\n", + " image = val_transform(image)\n", + " \n", + " # convert to RGB\n", + " array = np.asarray(image, dtype='uint8')\n", + " array = np.stack([array, array, array], axis=2)\n", + " image = Image.fromarray(array)\n", + " \n", + " # save\n", + " save_path = os.path.join(data_dir, 'val', str(row.category_number), row.img_name)\n", + " image.save(save_path, 'jpeg')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Resize train images" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "enhancers = {\n", + " 0: lambda image, f: ImageEnhance.Color(image).enhance(f),\n", + " 1: lambda image, f: ImageEnhance.Contrast(image).enhance(f),\n", + " 2: lambda image, f: ImageEnhance.Brightness(image).enhance(f),\n", + " 3: lambda image, f: ImageEnhance.Sharpness(image).enhance(f)\n", + "}\n", + "\n", + "factors = {\n", + " 0: lambda: np.random.uniform(0.4, 1.6),\n", + " 1: lambda: np.random.uniform(0.8, 1.2),\n", + " 2: lambda: np.random.uniform(0.8, 1.2),\n", + " 3: lambda: np.random.uniform(0.4, 1.6)\n", + "}\n", + "\n", + "# randomly enhance images in random order\n", + "def enhance(image):\n", + " order = [0, 1, 2, 3]\n", + " np.random.shuffle(order)\n", + " for i in order:\n", + " f = factors[i]()\n", + " image = enhancers[i](image, f)\n", + " return image" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "train_transform_rare = transforms.Compose([\n", + " transforms.Scale(384, Image.LANCZOS),\n", + " transforms.RandomCrop(299),\n", + " transforms.RandomHorizontalFlip(),\n", + " transforms.Lambda(enhance)\n", + "])\n", + "\n", + "train_transform = transforms.Compose([\n", + " transforms.Scale(384, Image.LANCZOS),\n", + " transforms.RandomCrop(299),\n", + " transforms.RandomHorizontalFlip(),\n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# number of images in each category\n", + "class_counts = dict(T.category_name.value_counts())\n", + "np.save('class_counts.npy', class_counts)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": true, + "scrolled": true + }, + "outputs": [], + "source": [ + "# sample with replacement 100 images from each category\n", + "T = T.groupby('category_name', group_keys=False).apply(lambda x: x.sample(n=100, replace=True))\n", + "T.reset_index(drop=True, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "25600" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_size = len(T)\n", + "train_size" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "25261it [11:57, 35.22it/s]\n" + ] + } + ], + "source": [ + "# resize RGB images\n", + "for i, row in tqdm(T.loc[T.channels == 3].iterrows()):\n", + " # get image\n", + " file_path = os.path.join(train_dir, row.directory, row.img_name)\n", + " image = Image.open(file_path)\n", + " \n", + " # transform it\n", + " if class_counts[row.category_name] < 100:\n", + " image = train_transform_rare(image)\n", + " else:\n", + " image = train_transform(image)\n", + " \n", + " # save\n", + " new_image_name = str(i) + '_' + row.img_name\n", + " save_path = os.path.join(data_dir, 'train', str(row.category_number), new_image_name)\n", + " image.save(save_path, 'jpeg')" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "339it [00:03, 85.92it/s]\n" + ] + } + ], + "source": [ + "# resize grayscale images\n", + "for i, row in tqdm(T.loc[T.channels == 1].iterrows()):\n", + " # get image\n", + " file_path = os.path.join(train_dir, row.directory, row.img_name)\n", + " image = Image.open(file_path)\n", + " \n", + " # transform it\n", + " if class_counts[row.category_name] < 100:\n", + " image = train_transform_rare(image)\n", + " else:\n", + " image = train_transform(image)\n", + " \n", + " # convert to RGB\n", + " array = np.asarray(image, dtype='uint8')\n", + " array = np.stack([array, array, array], axis=2)\n", + " image = Image.fromarray(array)\n", + " \n", + " # save\n", + " new_image_name = str(i) + '_' + row.img_name\n", + " save_path = os.path.join(data_dir, 'train', str(row.category_number), new_image_name)\n", + " image.save(save_path, 'jpeg')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/utils/split_but_no_resizing.ipynb b/utils/split_but_no_resizing.ipynb new file mode 100644 index 0000000..f717192 --- /dev/null +++ b/utils/split_but_no_resizing.ipynb @@ -0,0 +1,277 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "from PIL import Image\n", + "\n", + "import os\n", + "from tqdm import tqdm " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# the folder from 256_ObjectCategories.tar file\n", + "train_dir = '/home/ubuntu/data/256_ObjectCategories/'\n", + "\n", + "# a folder where resized and split data will be stored\n", + "data_dir = '/home/ubuntu/data/'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load constant train-val split" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "T = pd.read_csv('../train_val_split/train_metadata.csv')\n", + "V = pd.read_csv('../train_val_split/val_metadata.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Create directories for different categories" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "os.mkdir(data_dir + 'train_no_resizing')\n", + "for i in range(1, 256 + 1):\n", + " os.mkdir(data_dir + 'train_no_resizing/' + str(i))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "os.mkdir(data_dir + 'val_no_resizing')\n", + "for i in range(1, 256 + 1):\n", + " os.mkdir(data_dir + 'val_no_resizing/' + str(i))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# val. images" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5120" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "val_size = len(V)\n", + "val_size" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "5033it [01:00, 83.73it/s] \n" + ] + } + ], + "source": [ + "# RGB images\n", + "for i, row in tqdm(V.loc[V.channels == 3].iterrows()):\n", + " # get image\n", + " file_path = os.path.join(train_dir, row.directory, row.img_name)\n", + " image = Image.open(file_path)\n", + " \n", + " # save\n", + " save_path = os.path.join(data_dir, 'val_no_resizing', str(row.category_number), row.img_name)\n", + " image.save(save_path, 'jpeg')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "87it [00:00, 126.98it/s]\n" + ] + } + ], + "source": [ + "# grayscale images\n", + "for i, row in tqdm(V.loc[V.channels == 1].iterrows()):\n", + " # get image\n", + " file_path = os.path.join(train_dir, row.directory, row.img_name)\n", + " image = Image.open(file_path)\n", + " \n", + " # convert to RGB\n", + " array = np.asarray(image, dtype='uint8')\n", + " array = np.stack([array, array, array], axis=2)\n", + " image = Image.fromarray(array)\n", + " \n", + " # save\n", + " save_path = os.path.join(data_dir, 'val_no_resizing', str(row.category_number), row.img_name)\n", + " image.save(save_path, 'jpeg')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# train images" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "16980" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_size = len(T)\n", + "train_size" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "16769it [02:51, 56.83it/s] \n" + ] + } + ], + "source": [ + "# RGB images\n", + "for i, row in tqdm(T.loc[T.channels == 3].iterrows()):\n", + " # get image\n", + " file_path = os.path.join(train_dir, row.directory, row.img_name)\n", + " image = Image.open(file_path)\n", + " \n", + " # save\n", + " save_path = os.path.join(data_dir, 'train_no_resizing', str(row.category_number), row.img_name)\n", + " image.save(save_path, 'jpeg')" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "211it [00:04, 48.05it/s]\n" + ] + } + ], + "source": [ + "# grayscale images\n", + "for i, row in tqdm(T.loc[T.channels == 1].iterrows()):\n", + " # get image\n", + " file_path = os.path.join(train_dir, row.directory, row.img_name)\n", + " image = Image.open(file_path)\n", + " \n", + " # convert to RGB\n", + " array = np.asarray(image, dtype='uint8')\n", + " array = np.stack([array, array, array], axis=2)\n", + " image = Image.fromarray(array)\n", + " \n", + " # save\n", + " save_path = os.path.join(data_dir, 'train_no_resizing', str(row.category_number), row.img_name)\n", + " image.save(save_path, 'jpeg')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/vanilla_mobilenet.ipynb b/vanilla_mobilenet.ipynb new file mode 100644 index 0000000..759e34c --- /dev/null +++ b/vanilla_mobilenet.ipynb @@ -0,0 +1,722 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "import keras\n", + "from keras import optimizers\n", + "from keras.preprocessing.image import ImageDataGenerator\n", + "from keras.callbacks import ReduceLROnPlateau, EarlyStopping\n", + "from keras.applications.mobilenet import preprocess_input\n", + "\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "from mobilenet import get_mobilenet" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "data_dir = '/home/dongsung/study/machine_learning/distillation/data'" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 0 images belonging to 0 classes.\n", + "Found 0 images belonging to 0 classes.\n" + ] + } + ], + "source": [ + "data_generator = ImageDataGenerator(\n", + " data_format='channels_last',\n", + " preprocessing_function=preprocess_input\n", + ")\n", + "\n", + "train_generator = data_generator.flow_from_directory(\n", + " data_dir + 'train', \n", + " target_size=(224, 224),\n", + " batch_size=64\n", + ")\n", + "\n", + "val_generator = data_generator.flow_from_directory(\n", + " data_dir + 'val', shuffle=False,\n", + " target_size=(224, 224),\n", + " batch_size=64\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Model" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "input_1 (InputLayer) (None, 224, 224, 3) 0 \n", + "_________________________________________________________________\n", + "conv1_pad (ZeroPadding2D) (None, 225, 225, 3) 0 \n", + "_________________________________________________________________\n", + "conv1 (Conv2D) (None, 112, 112, 8) 216 \n", + "_________________________________________________________________\n", + "conv1_bn (BatchNormalization (None, 112, 112, 8) 32 \n", + "_________________________________________________________________\n", + "conv1_relu (ReLU) (None, 112, 112, 8) 0 \n", + "_________________________________________________________________\n", + "conv_dw_1 (DepthwiseConv2D) (None, 112, 112, 8) 72 \n", + "_________________________________________________________________\n", + "conv_dw_1_bn (BatchNormaliza (None, 112, 112, 8) 32 \n", + "_________________________________________________________________\n", + "conv_dw_1_relu (ReLU) (None, 112, 112, 8) 0 \n", + "_________________________________________________________________\n", + "conv_pw_1 (Conv2D) (None, 112, 112, 16) 128 \n", + "_________________________________________________________________\n", + "conv_pw_1_bn (BatchNormaliza (None, 112, 112, 16) 64 \n", + "_________________________________________________________________\n", + "conv_pw_1_relu (ReLU) (None, 112, 112, 16) 0 \n", + "_________________________________________________________________\n", + "conv_pad_2 (ZeroPadding2D) (None, 113, 113, 16) 0 \n", + "_________________________________________________________________\n", + "conv_dw_2 (DepthwiseConv2D) (None, 56, 56, 16) 144 \n", + "_________________________________________________________________\n", + "conv_dw_2_bn (BatchNormaliza (None, 56, 56, 16) 64 \n", + "_________________________________________________________________\n", + "conv_dw_2_relu (ReLU) (None, 56, 56, 16) 0 \n", + "_________________________________________________________________\n", + "conv_pw_2 (Conv2D) (None, 56, 56, 32) 512 \n", + "_________________________________________________________________\n", + "conv_pw_2_bn (BatchNormaliza (None, 56, 56, 32) 128 \n", + "_________________________________________________________________\n", + "conv_pw_2_relu (ReLU) (None, 56, 56, 32) 0 \n", + "_________________________________________________________________\n", + "conv_dw_3 (DepthwiseConv2D) (None, 56, 56, 32) 288 \n", + "_________________________________________________________________\n", + "conv_dw_3_bn (BatchNormaliza (None, 56, 56, 32) 128 \n", + "_________________________________________________________________\n", + "conv_dw_3_relu (ReLU) (None, 56, 56, 32) 0 \n", + "_________________________________________________________________\n", + "conv_pw_3 (Conv2D) (None, 56, 56, 32) 1024 \n", + "_________________________________________________________________\n", + "conv_pw_3_bn (BatchNormaliza (None, 56, 56, 32) 128 \n", + "_________________________________________________________________\n", + "conv_pw_3_relu (ReLU) (None, 56, 56, 32) 0 \n", + "_________________________________________________________________\n", + "conv_pad_4 (ZeroPadding2D) (None, 57, 57, 32) 0 \n", + "_________________________________________________________________\n", + "conv_dw_4 (DepthwiseConv2D) (None, 28, 28, 32) 288 \n", + "_________________________________________________________________\n", + "conv_dw_4_bn (BatchNormaliza (None, 28, 28, 32) 128 \n", + "_________________________________________________________________\n", + "conv_dw_4_relu (ReLU) (None, 28, 28, 32) 0 \n", + "_________________________________________________________________\n", + "conv_pw_4 (Conv2D) (None, 28, 28, 64) 2048 \n", + "_________________________________________________________________\n", + "conv_pw_4_bn (BatchNormaliza (None, 28, 28, 64) 256 \n", + "_________________________________________________________________\n", + "conv_pw_4_relu (ReLU) (None, 28, 28, 64) 0 \n", + "_________________________________________________________________\n", + "conv_dw_5 (DepthwiseConv2D) (None, 28, 28, 64) 576 \n", + "_________________________________________________________________\n", + "conv_dw_5_bn (BatchNormaliza (None, 28, 28, 64) 256 \n", + "_________________________________________________________________\n", + "conv_dw_5_relu (ReLU) (None, 28, 28, 64) 0 \n", + "_________________________________________________________________\n", + "conv_pw_5 (Conv2D) (None, 28, 28, 64) 4096 \n", + "_________________________________________________________________\n", + "conv_pw_5_bn (BatchNormaliza (None, 28, 28, 64) 256 \n", + "_________________________________________________________________\n", + "conv_pw_5_relu (ReLU) (None, 28, 28, 64) 0 \n", + "_________________________________________________________________\n", + "conv_pad_6 (ZeroPadding2D) (None, 29, 29, 64) 0 \n", + "_________________________________________________________________\n", + "conv_dw_6 (DepthwiseConv2D) (None, 14, 14, 64) 576 \n", + "_________________________________________________________________\n", + "conv_dw_6_bn (BatchNormaliza (None, 14, 14, 64) 256 \n", + "_________________________________________________________________\n", + "conv_dw_6_relu (ReLU) (None, 14, 14, 64) 0 \n", + "_________________________________________________________________\n", + "conv_pw_6 (Conv2D) (None, 14, 14, 128) 8192 \n", + "_________________________________________________________________\n", + "conv_pw_6_bn (BatchNormaliza (None, 14, 14, 128) 512 \n", + "_________________________________________________________________\n", + "conv_pw_6_relu (ReLU) (None, 14, 14, 128) 0 \n", + "_________________________________________________________________\n", + "conv_dw_7 (DepthwiseConv2D) (None, 14, 14, 128) 1152 \n", + "_________________________________________________________________\n", + "conv_dw_7_bn (BatchNormaliza (None, 14, 14, 128) 512 \n", + "_________________________________________________________________\n", + "conv_dw_7_relu (ReLU) (None, 14, 14, 128) 0 \n", + "_________________________________________________________________\n", + "conv_pw_7 (Conv2D) (None, 14, 14, 128) 16384 \n", + "_________________________________________________________________\n", + "conv_pw_7_bn (BatchNormaliza (None, 14, 14, 128) 512 \n", + "_________________________________________________________________\n", + "conv_pw_7_relu (ReLU) (None, 14, 14, 128) 0 \n", + "_________________________________________________________________\n", + "conv_dw_8 (DepthwiseConv2D) (None, 14, 14, 128) 1152 \n", + "_________________________________________________________________\n", + "conv_dw_8_bn (BatchNormaliza (None, 14, 14, 128) 512 \n", + "_________________________________________________________________\n", + "conv_dw_8_relu (ReLU) (None, 14, 14, 128) 0 \n", + "_________________________________________________________________\n", + "conv_pw_8 (Conv2D) (None, 14, 14, 128) 16384 \n", + "_________________________________________________________________\n", + "conv_pw_8_bn (BatchNormaliza (None, 14, 14, 128) 512 \n", + "_________________________________________________________________\n", + "conv_pw_8_relu (ReLU) (None, 14, 14, 128) 0 \n", + "_________________________________________________________________\n", + "conv_dw_9 (DepthwiseConv2D) (None, 14, 14, 128) 1152 \n", + "_________________________________________________________________\n", + "conv_dw_9_bn (BatchNormaliza (None, 14, 14, 128) 512 \n", + "_________________________________________________________________\n", + "conv_dw_9_relu (ReLU) (None, 14, 14, 128) 0 \n", + "_________________________________________________________________\n", + "conv_pw_9 (Conv2D) (None, 14, 14, 128) 16384 \n", + "_________________________________________________________________\n", + "conv_pw_9_bn (BatchNormaliza (None, 14, 14, 128) 512 \n", + "_________________________________________________________________\n", + "conv_pw_9_relu (ReLU) (None, 14, 14, 128) 0 \n", + "_________________________________________________________________\n", + "conv_dw_10 (DepthwiseConv2D) (None, 14, 14, 128) 1152 \n", + "_________________________________________________________________\n", + "conv_dw_10_bn (BatchNormaliz (None, 14, 14, 128) 512 \n", + "_________________________________________________________________\n", + "conv_dw_10_relu (ReLU) (None, 14, 14, 128) 0 \n", + "_________________________________________________________________\n", + "conv_pw_10 (Conv2D) (None, 14, 14, 128) 16384 \n", + "_________________________________________________________________\n", + "conv_pw_10_bn (BatchNormaliz (None, 14, 14, 128) 512 \n", + "_________________________________________________________________\n", + "conv_pw_10_relu (ReLU) (None, 14, 14, 128) 0 \n", + "_________________________________________________________________\n", + "conv_dw_11 (DepthwiseConv2D) (None, 14, 14, 128) 1152 \n", + "_________________________________________________________________\n", + "conv_dw_11_bn (BatchNormaliz (None, 14, 14, 128) 512 \n", + "_________________________________________________________________\n", + "conv_dw_11_relu (ReLU) (None, 14, 14, 128) 0 \n", + "_________________________________________________________________\n", + "conv_pw_11 (Conv2D) (None, 14, 14, 128) 16384 \n", + "_________________________________________________________________\n", + "conv_pw_11_bn (BatchNormaliz (None, 14, 14, 128) 512 \n", + "_________________________________________________________________\n", + "conv_pw_11_relu (ReLU) (None, 14, 14, 128) 0 \n", + "_________________________________________________________________\n", + "conv_pad_12 (ZeroPadding2D) (None, 15, 15, 128) 0 \n", + "_________________________________________________________________\n", + "conv_dw_12 (DepthwiseConv2D) (None, 7, 7, 128) 1152 \n", + "_________________________________________________________________\n", + "conv_dw_12_bn (BatchNormaliz (None, 7, 7, 128) 512 \n", + "_________________________________________________________________\n", + "conv_dw_12_relu (ReLU) (None, 7, 7, 128) 0 \n", + "_________________________________________________________________\n", + "conv_pw_12 (Conv2D) (None, 7, 7, 256) 32768 \n", + "_________________________________________________________________\n", + "conv_pw_12_bn (BatchNormaliz (None, 7, 7, 256) 1024 \n", + "_________________________________________________________________\n", + "conv_pw_12_relu (ReLU) (None, 7, 7, 256) 0 \n", + "_________________________________________________________________\n", + "conv_dw_13 (DepthwiseConv2D) (None, 7, 7, 256) 2304 \n", + "_________________________________________________________________\n", + "conv_dw_13_bn (BatchNormaliz (None, 7, 7, 256) 1024 \n", + "_________________________________________________________________\n", + "conv_dw_13_relu (ReLU) (None, 7, 7, 256) 0 \n", + "_________________________________________________________________\n", + "conv_pw_13 (Conv2D) (None, 7, 7, 256) 65536 \n", + "_________________________________________________________________\n", + "conv_pw_13_bn (BatchNormaliz (None, 7, 7, 256) 1024 \n", + "_________________________________________________________________\n", + "conv_pw_13_relu (ReLU) (None, 7, 7, 256) 0 \n", + "_________________________________________________________________\n", + "global_average_pooling2d_1 ( (None, 256) 0 \n", + "_________________________________________________________________\n", + "dropout_1 (Dropout) (None, 256) 0 \n", + "_________________________________________________________________\n", + "dense_1 (Dense) (None, 256) 65792 \n", + "_________________________________________________________________\n", + "activation_1 (Activation) (None, 256) 0 \n", + "=================================================================\n", + "Total params: 284,336\n", + "Trainable params: 65,792\n", + "Non-trainable params: 218,544\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "model = get_mobilenet(224, alpha=0.25, weight_decay=1e-5, dropout=0.1)\n", + "model.count_params()\n", + "\n", + "model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.6/mobilenet_2_5_224_tf.h5\n", + "2113536/2108140 [==============================] - 1s 0us/step\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "input_2 (InputLayer) (None, 224, 224, 3) 0 \n", + "_________________________________________________________________\n", + "conv1_pad (ZeroPadding2D) (None, 225, 225, 3) 0 \n", + "_________________________________________________________________\n", + "conv1 (Conv2D) (None, 112, 112, 8) 216 \n", + "_________________________________________________________________\n", + "conv1_bn (BatchNormalization (None, 112, 112, 8) 32 \n", + "_________________________________________________________________\n", + "conv1_relu (ReLU) (None, 112, 112, 8) 0 \n", + "_________________________________________________________________\n", + "conv_dw_1 (DepthwiseConv2D) (None, 112, 112, 8) 72 \n", + "_________________________________________________________________\n", + "conv_dw_1_bn (BatchNormaliza (None, 112, 112, 8) 32 \n", + "_________________________________________________________________\n", + "conv_dw_1_relu (ReLU) (None, 112, 112, 8) 0 \n", + "_________________________________________________________________\n", + "conv_pw_1 (Conv2D) (None, 112, 112, 16) 128 \n", + "_________________________________________________________________\n", + "conv_pw_1_bn (BatchNormaliza (None, 112, 112, 16) 64 \n", + "_________________________________________________________________\n", + "conv_pw_1_relu (ReLU) (None, 112, 112, 16) 0 \n", + "_________________________________________________________________\n", + "conv_pad_2 (ZeroPadding2D) (None, 113, 113, 16) 0 \n", + "_________________________________________________________________\n", + "conv_dw_2 (DepthwiseConv2D) (None, 56, 56, 16) 144 \n", + "_________________________________________________________________\n", + "conv_dw_2_bn (BatchNormaliza (None, 56, 56, 16) 64 \n", + "_________________________________________________________________\n", + "conv_dw_2_relu (ReLU) (None, 56, 56, 16) 0 \n", + "_________________________________________________________________\n", + "conv_pw_2 (Conv2D) (None, 56, 56, 32) 512 \n", + "_________________________________________________________________\n", + "conv_pw_2_bn (BatchNormaliza (None, 56, 56, 32) 128 \n", + "_________________________________________________________________\n", + "conv_pw_2_relu (ReLU) (None, 56, 56, 32) 0 \n", + "_________________________________________________________________\n", + "conv_dw_3 (DepthwiseConv2D) (None, 56, 56, 32) 288 \n", + "_________________________________________________________________\n", + "conv_dw_3_bn (BatchNormaliza (None, 56, 56, 32) 128 \n", + "_________________________________________________________________\n", + "conv_dw_3_relu (ReLU) (None, 56, 56, 32) 0 \n", + "_________________________________________________________________\n", + "conv_pw_3 (Conv2D) (None, 56, 56, 32) 1024 \n", + "_________________________________________________________________\n", + "conv_pw_3_bn (BatchNormaliza (None, 56, 56, 32) 128 \n", + "_________________________________________________________________\n", + "conv_pw_3_relu (ReLU) (None, 56, 56, 32) 0 \n", + "_________________________________________________________________\n", + "conv_pad_4 (ZeroPadding2D) (None, 57, 57, 32) 0 \n", + "_________________________________________________________________\n", + "conv_dw_4 (DepthwiseConv2D) (None, 28, 28, 32) 288 \n", + "_________________________________________________________________\n", + "conv_dw_4_bn (BatchNormaliza (None, 28, 28, 32) 128 \n", + "_________________________________________________________________\n", + "conv_dw_4_relu (ReLU) (None, 28, 28, 32) 0 \n", + "_________________________________________________________________\n", + "conv_pw_4 (Conv2D) (None, 28, 28, 64) 2048 \n", + "_________________________________________________________________\n", + "conv_pw_4_bn (BatchNormaliza (None, 28, 28, 64) 256 \n", + "_________________________________________________________________\n", + "conv_pw_4_relu (ReLU) (None, 28, 28, 64) 0 \n", + "_________________________________________________________________\n", + "conv_dw_5 (DepthwiseConv2D) (None, 28, 28, 64) 576 \n", + "_________________________________________________________________\n", + "conv_dw_5_bn (BatchNormaliza (None, 28, 28, 64) 256 \n", + "_________________________________________________________________\n", + "conv_dw_5_relu (ReLU) (None, 28, 28, 64) 0 \n", + "_________________________________________________________________\n", + "conv_pw_5 (Conv2D) (None, 28, 28, 64) 4096 \n", + "_________________________________________________________________\n", + "conv_pw_5_bn (BatchNormaliza (None, 28, 28, 64) 256 \n", + "_________________________________________________________________\n", + "conv_pw_5_relu (ReLU) (None, 28, 28, 64) 0 \n", + "_________________________________________________________________\n", + "conv_pad_6 (ZeroPadding2D) (None, 29, 29, 64) 0 \n", + "_________________________________________________________________\n", + "conv_dw_6 (DepthwiseConv2D) (None, 14, 14, 64) 576 \n", + "_________________________________________________________________\n", + "conv_dw_6_bn (BatchNormaliza (None, 14, 14, 64) 256 \n", + "_________________________________________________________________\n", + "conv_dw_6_relu (ReLU) (None, 14, 14, 64) 0 \n", + "_________________________________________________________________\n", + "conv_pw_6 (Conv2D) (None, 14, 14, 128) 8192 \n", + "_________________________________________________________________\n", + "conv_pw_6_bn (BatchNormaliza (None, 14, 14, 128) 512 \n", + "_________________________________________________________________\n", + "conv_pw_6_relu (ReLU) (None, 14, 14, 128) 0 \n", + "_________________________________________________________________\n", + "conv_dw_7 (DepthwiseConv2D) (None, 14, 14, 128) 1152 \n", + "_________________________________________________________________\n", + "conv_dw_7_bn (BatchNormaliza (None, 14, 14, 128) 512 \n", + "_________________________________________________________________\n", + "conv_dw_7_relu (ReLU) (None, 14, 14, 128) 0 \n", + "_________________________________________________________________\n", + "conv_pw_7 (Conv2D) (None, 14, 14, 128) 16384 \n", + "_________________________________________________________________\n", + "conv_pw_7_bn (BatchNormaliza (None, 14, 14, 128) 512 \n", + "_________________________________________________________________\n", + "conv_pw_7_relu (ReLU) (None, 14, 14, 128) 0 \n", + "_________________________________________________________________\n", + "conv_dw_8 (DepthwiseConv2D) (None, 14, 14, 128) 1152 \n", + "_________________________________________________________________\n", + "conv_dw_8_bn (BatchNormaliza (None, 14, 14, 128) 512 \n", + "_________________________________________________________________\n", + "conv_dw_8_relu (ReLU) (None, 14, 14, 128) 0 \n", + "_________________________________________________________________\n", + "conv_pw_8 (Conv2D) (None, 14, 14, 128) 16384 \n", + "_________________________________________________________________\n", + "conv_pw_8_bn (BatchNormaliza (None, 14, 14, 128) 512 \n", + "_________________________________________________________________\n", + "conv_pw_8_relu (ReLU) (None, 14, 14, 128) 0 \n", + "_________________________________________________________________\n", + "conv_dw_9 (DepthwiseConv2D) (None, 14, 14, 128) 1152 \n", + "_________________________________________________________________\n", + "conv_dw_9_bn (BatchNormaliza (None, 14, 14, 128) 512 \n", + "_________________________________________________________________\n", + "conv_dw_9_relu (ReLU) (None, 14, 14, 128) 0 \n", + "_________________________________________________________________\n", + "conv_pw_9 (Conv2D) (None, 14, 14, 128) 16384 \n", + "_________________________________________________________________\n", + "conv_pw_9_bn (BatchNormaliza (None, 14, 14, 128) 512 \n", + "_________________________________________________________________\n", + "conv_pw_9_relu (ReLU) (None, 14, 14, 128) 0 \n", + "_________________________________________________________________\n", + "conv_dw_10 (DepthwiseConv2D) (None, 14, 14, 128) 1152 \n", + "_________________________________________________________________\n", + "conv_dw_10_bn (BatchNormaliz (None, 14, 14, 128) 512 \n", + "_________________________________________________________________\n", + "conv_dw_10_relu (ReLU) (None, 14, 14, 128) 0 \n", + "_________________________________________________________________\n", + "conv_pw_10 (Conv2D) (None, 14, 14, 128) 16384 \n", + "_________________________________________________________________\n", + "conv_pw_10_bn (BatchNormaliz (None, 14, 14, 128) 512 \n", + "_________________________________________________________________\n", + "conv_pw_10_relu (ReLU) (None, 14, 14, 128) 0 \n", + "_________________________________________________________________\n", + "conv_dw_11 (DepthwiseConv2D) (None, 14, 14, 128) 1152 \n", + "_________________________________________________________________\n", + "conv_dw_11_bn (BatchNormaliz (None, 14, 14, 128) 512 \n", + "_________________________________________________________________\n", + "conv_dw_11_relu (ReLU) (None, 14, 14, 128) 0 \n", + "_________________________________________________________________\n", + "conv_pw_11 (Conv2D) (None, 14, 14, 128) 16384 \n", + "_________________________________________________________________\n", + "conv_pw_11_bn (BatchNormaliz (None, 14, 14, 128) 512 \n", + "_________________________________________________________________\n", + "conv_pw_11_relu (ReLU) (None, 14, 14, 128) 0 \n", + "_________________________________________________________________\n", + "conv_pad_12 (ZeroPadding2D) (None, 15, 15, 128) 0 \n", + "_________________________________________________________________\n", + "conv_dw_12 (DepthwiseConv2D) (None, 7, 7, 128) 1152 \n", + "_________________________________________________________________\n", + "conv_dw_12_bn (BatchNormaliz (None, 7, 7, 128) 512 \n", + "_________________________________________________________________\n", + "conv_dw_12_relu (ReLU) (None, 7, 7, 128) 0 \n", + "_________________________________________________________________\n", + "conv_pw_12 (Conv2D) (None, 7, 7, 256) 32768 \n", + "_________________________________________________________________\n", + "conv_pw_12_bn (BatchNormaliz (None, 7, 7, 256) 1024 \n", + "_________________________________________________________________\n", + "conv_pw_12_relu (ReLU) (None, 7, 7, 256) 0 \n", + "_________________________________________________________________\n", + "conv_dw_13 (DepthwiseConv2D) (None, 7, 7, 256) 2304 \n", + "_________________________________________________________________\n", + "conv_dw_13_bn (BatchNormaliz (None, 7, 7, 256) 1024 \n", + "_________________________________________________________________\n", + "conv_dw_13_relu (ReLU) (None, 7, 7, 256) 0 \n", + "_________________________________________________________________\n", + "conv_pw_13 (Conv2D) (None, 7, 7, 256) 65536 \n", + "_________________________________________________________________\n", + "conv_pw_13_bn (BatchNormaliz (None, 7, 7, 256) 1024 \n", + "_________________________________________________________________\n", + "conv_pw_13_relu (ReLU) (None, 7, 7, 256) 0 \n", + "_________________________________________________________________\n", + "global_average_pooling2d_2 ( (None, 256) 0 \n", + "_________________________________________________________________\n", + "reshape_1 (Reshape) (None, 1, 1, 256) 0 \n", + "_________________________________________________________________\n", + "dropout (Dropout) (None, 1, 1, 256) 0 \n", + "_________________________________________________________________\n", + "conv_preds (Conv2D) (None, 1, 1, 1000) 257000 \n", + "_________________________________________________________________\n", + "act_softmax (Activation) (None, 1, 1, 1000) 0 \n", + "_________________________________________________________________\n", + "reshape_2 (Reshape) (None, 1000) 0 \n", + "=================================================================\n", + "Total params: 475,544\n", + "Trainable params: 470,072\n", + "Non-trainable params: 5,472\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "from keras.applications.mobilenet import MobileNet\n", + "\n", + "input_shape = (224, 224, 3)\n", + "model2 = MobileNet(\n", + " include_top=True, weights='imagenet', \n", + " input_shape=input_shape, alpha=0.25)\n", + "model2.count_params()\n", + "\n", + "model2.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Training" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "model.compile(\n", + " optimizer=optimizers.SGD(lr=1e-2, momentum=0.9, nesterov=True), \n", + " loss='categorical_crossentropy', metrics=['accuracy', 'top_k_categorical_accuracy']\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/dongsung/tensorflow_virtualenv/lib/python3.5/site-packages/keras/callbacks.py:1065: UserWarning: `epsilon` argument is deprecated and will be removed, use `min_delta` instead.\n", + " warnings.warn('`epsilon` argument is deprecated and '\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/30\n" + ] + }, + { + "ename": "ZeroDivisionError", + "evalue": "integer division or modulo by zero", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mZeroDivisionError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mEarlyStopping\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmonitor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'val_acc'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpatience\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_delta\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.01\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m ],\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_generator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m80\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mworkers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 9\u001b[0m )\n", + "\u001b[0;32m~/tensorflow_virtualenv/lib/python3.5/site-packages/keras/legacy/interfaces.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 89\u001b[0m warnings.warn('Update your `' + object_name + '` call to the ' +\n\u001b[1;32m 90\u001b[0m 'Keras 2 API: ' + signature, stacklevel=2)\n\u001b[0;32m---> 91\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 92\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_function\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 93\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/tensorflow_virtualenv/lib/python3.5/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m 1416\u001b[0m \u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1417\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1418\u001b[0;31m initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m 1419\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1420\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0minterfaces\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegacy_generator_methods_support\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/tensorflow_virtualenv/lib/python3.5/site-packages/keras/engine/training_generator.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m 179\u001b[0m \u001b[0mbatch_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 180\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0msteps_done\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 181\u001b[0;31m \u001b[0mgenerator_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput_generator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 182\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 183\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgenerator_output\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'__len__'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + 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\u001b[0mindex_array\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex_generator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1968\u001b[0m \u001b[0;31m# The transformation of images is not under thread lock\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1969\u001b[0m \u001b[0;31m# so it can be done in parallel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/tensorflow_virtualenv/lib/python3.5/site-packages/keras_preprocessing/image.py\u001b[0m in \u001b[0;36m_flow_index\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1509\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_index_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1510\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1511\u001b[0;31m \u001b[0mcurrent_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatch_index\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1512\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mcurrent_index\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1513\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatch_index\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mZeroDivisionError\u001b[0m: integer division or modulo by zero" + ] + } + ], + "source": [ + "model.fit_generator(\n", + " train_generator, \n", + " steps_per_epoch=400, epochs=30, verbose=1,\n", + " callbacks=[\n", + " ReduceLROnPlateau(monitor='val_acc', factor=0.1, patience=2, epsilon=0.007),\n", + " EarlyStopping(monitor='val_acc', patience=4, min_delta=0.01)\n", + " ],\n", + " validation_data=val_generator, validation_steps=80, workers=4\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Loss/epoch plots" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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WSyN37zCzKHCtmQH80d1/c5LD2oDPufvLYc9ilZn9zt039Gj3J3e/tM+V99WGx+HxT8HH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\niEgXhYKIiHRRKIiISBeFgoiIdPn/ft2jjoeXnkQAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(model.history.history['loss'], label='train');\n", + "plt.plot(model.history.history['val_loss'], label='val');\n", + "plt.legend();\n", + "plt.xlabel('epoch');\n", + "plt.ylabel('logloss');" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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LoZ9Ny4HMQhg3AZJSYNx4SBwHicmQNA4SU4JXUvhYYvhYUkq/c333e7bDn0lIDO4WQt3B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\nQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(model.history.history['acc'], label='train');\n", + "plt.plot(model.history.history['val_acc'], label='val');\n", + "plt.legend();\n", + "plt.xlabel('epoch');\n", + "plt.ylabel('accuracy');" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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XYFMiA0tFg0MRtrV2cbISg4iksXgTw98A/cAPo68+4GOJCipV7d5/iP7BCEvV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\nZAQlBhERGUGJQURERlBiEBGREf4/LQh7dywQRgkAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(model.history.history['top_k_categorical_accuracy'], label='train');\n", + "plt.plot(model.history.history['val_top_k_categorical_accuracy'], label='val');\n", + "plt.legend();\n", + "plt.xlabel('epoch');\n", + "plt.ylabel('top5_accuracy');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Results" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1.4546428885310889, 0.646484375, 0.859375]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.evaluate_generator(val_generator, 80)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/vanilla_squeezenet.ipynb b/vanilla_squeezenet.ipynb new file mode 100644 index 0000000..e54ccd6 --- /dev/null +++ b/vanilla_squeezenet.ipynb @@ -0,0 +1,323 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "import keras\n", + "from keras import optimizers\n", + "from keras.preprocessing.image import ImageDataGenerator\n", + "from keras.callbacks import EarlyStopping, ReduceLROnPlateau\n", + "\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "from squeezenet import SqueezeNet, preprocess_input" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "data_dir = '/home/ubuntu/data/'" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 25600 images belonging to 256 classes.\n", + "Found 5120 images belonging to 256 classes.\n" + ] + } + ], + "source": [ + "data_generator = ImageDataGenerator(\n", + " data_format='channels_last',\n", + " preprocessing_function=preprocess_input\n", + ")\n", + "\n", + "train_generator = data_generator.flow_from_directory(\n", + " data_dir + 'train', \n", + " target_size=(299, 299),\n", + " batch_size=64\n", + ")\n", + "\n", + "val_generator = data_generator.flow_from_directory(\n", + " data_dir + 'val', shuffle=False,\n", + " target_size=(299, 299),\n", + " batch_size=64\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Model" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "853824" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = SqueezeNet(weight_decay=1e-4, image_size=299)\n", + "model.count_params()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Training" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "model.compile(\n", + " optimizer=optimizers.SGD(lr=1e-2, momentum=0.9, nesterov=True), \n", + " loss='categorical_crossentropy', metrics=['accuracy', 'top_k_categorical_accuracy']\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/30\n", + "400/400 [==============================] - 36s - loss: 2.3837 - acc: 0.4822 - top_k_categorical_accuracy: 0.7059 - val_loss: 1.8188 - val_acc: 0.5773 - val_top_k_categorical_accuracy: 0.8105\n", + "Epoch 2/30\n", + "400/400 [==============================] - 35s - loss: 1.1869 - acc: 0.7113 - top_k_categorical_accuracy: 0.8940 - val_loss: 1.7067 - val_acc: 0.6201 - val_top_k_categorical_accuracy: 0.8400\n", + "Epoch 3/30\n", + "400/400 [==============================] - 35s - loss: 0.8814 - acc: 0.7823 - top_k_categorical_accuracy: 0.9320 - val_loss: 1.6830 - val_acc: 0.6330 - val_top_k_categorical_accuracy: 0.8463\n", + "Epoch 4/30\n", + "400/400 [==============================] - 35s - loss: 0.7074 - acc: 0.8240 - top_k_categorical_accuracy: 0.9532 - val_loss: 1.6935 - val_acc: 0.6385 - val_top_k_categorical_accuracy: 0.8453\n", + "Epoch 5/30\n", + "400/400 [==============================] - 35s - loss: 0.5857 - acc: 0.8561 - top_k_categorical_accuracy: 0.9655 - val_loss: 1.7206 - val_acc: 0.6309 - val_top_k_categorical_accuracy: 0.8533\n", + "Epoch 6/30\n", + "400/400 [==============================] - 35s - loss: 0.5124 - acc: 0.8749 - top_k_categorical_accuracy: 0.9740 - val_loss: 1.7514 - val_acc: 0.6336 - val_top_k_categorical_accuracy: 0.8459\n", + "Epoch 7/30\n", + "400/400 [==============================] - 35s - loss: 0.3407 - acc: 0.9335 - top_k_categorical_accuracy: 0.9868 - val_loss: 1.5808 - val_acc: 0.6660 - val_top_k_categorical_accuracy: 0.8619\n", + "Epoch 8/30\n", + "400/400 [==============================] - 36s - loss: 0.2986 - acc: 0.9481 - top_k_categorical_accuracy: 0.9888 - val_loss: 1.5681 - val_acc: 0.6648 - val_top_k_categorical_accuracy: 0.8637\n", + "Epoch 9/30\n", + "400/400 [==============================] - 36s - loss: 0.2862 - acc: 0.9540 - top_k_categorical_accuracy: 0.9891 - val_loss: 1.5635 - val_acc: 0.6674 - val_top_k_categorical_accuracy: 0.8670\n", + "Epoch 10/30\n", + "400/400 [==============================] - 36s - loss: 0.2789 - acc: 0.9559 - top_k_categorical_accuracy: 0.9896 - val_loss: 1.5620 - val_acc: 0.6689 - val_top_k_categorical_accuracy: 0.8646\n", + "Epoch 11/30\n", + "400/400 [==============================] - 36s - loss: 0.2660 - acc: 0.9611 - top_k_categorical_accuracy: 0.9907 - val_loss: 1.5574 - val_acc: 0.6709 - val_top_k_categorical_accuracy: 0.8654\n", + "Epoch 12/30\n", + "400/400 [==============================] - 36s - loss: 0.2645 - acc: 0.9608 - top_k_categorical_accuracy: 0.9913 - val_loss: 1.5712 - val_acc: 0.6693 - val_top_k_categorical_accuracy: 0.8629\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.fit_generator(\n", + " train_generator, \n", + " steps_per_epoch=400, epochs=30, verbose=1,\n", + " callbacks=[\n", + " EarlyStopping(monitor='val_acc', patience=4, min_delta=0.01),\n", + " ReduceLROnPlateau(monitor='val_acc', factor=0.1, patience=2, epsilon=0.007)\n", + " ],\n", + " validation_data=val_generator, validation_steps=80, workers=4\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Loss/epoch plots" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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VtUwp/4wXPlxFfWMLowf15tqyIXxpzECy0nTgKhIvCgXp0bY2NPHXOauYUl7F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\nUBARkTb/HyCUaifa1LDQAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(model.history.history['loss'], label='train');\n", + "plt.plot(model.history.history['val_loss'], label='val');\n", + "plt.legend();\n", + "plt.xlabel('epoch');\n", + "plt.ylabel('logloss');" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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w7RPoMwTO/SVMvtIb2kEkRikUjlBbwLFwVSWzxvYjIaEHNXINO6FuR4cGvvrg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\nIiLSTqEgIiLtFAoiItJOoSAiIu3+PwqtAKBxkUVNAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(model.history.history['acc'], label='train');\n", + "plt.plot(model.history.history['val_acc'], label='val');\n", + "plt.legend();\n", + "plt.xlabel('epoch');\n", + "plt.ylabel('accuracy');" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(model.history.history['top_k_categorical_accuracy'], label='train');\n", + "plt.plot(model.history.history['val_top_k_categorical_accuracy'], label='val');\n", + "plt.legend();\n", + "plt.xlabel('epoch');\n", + "plt.ylabel('top5_accuracy');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Results" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1.5555390566587448, 0.67167968749999996, 0.865234375]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.evaluate_generator(val_generator, 80)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/xception.py b/xception.py new file mode 100644 index 0000000..9621146 --- /dev/null +++ b/xception.py @@ -0,0 +1,130 @@ +import keras +from keras.models import Model +from keras.layers import Input, BatchNormalization, Conv2D, SeparableConv2D, MaxPooling2D +from keras.layers import GlobalAveragePooling2D, Dense, Activation, Dropout +from keras import layers +from keras.utils.data_utils import get_file + + +TF_WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.4/xception_weights_tf_dim_ordering_tf_kernels_notop.h5' + + +def preprocess_input(x): + x /= 255.0 + x -= 0.5 + x *= 2.0 + return x + + +def Xception(weight_decay=1e-4, classes=256, input_shape=(299, 299, 3)): + + img_input = Input(shape=input_shape) + + x = Conv2D(32, (3, 3), strides=(2, 2), use_bias=False, name='block1_conv1')(img_input) + x = BatchNormalization(name='block1_conv1_bn')(x) + x = Activation('relu', name='block1_conv1_act')(x) + x = Conv2D(64, (3, 3), use_bias=False, name='block1_conv2')(x) + x = BatchNormalization(name='block1_conv2_bn')(x) + x = Activation('relu', name='block1_conv2_act')(x) + + residual = Conv2D(128, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x) + residual = BatchNormalization()(residual) + + x = SeparableConv2D(128, (3, 3), padding='same', use_bias=False, name='block2_sepconv1')(x) + x = BatchNormalization(name='block2_sepconv1_bn')(x) + x = Activation('relu', name='block2_sepconv2_act')(x) + x = SeparableConv2D(128, (3, 3), padding='same', use_bias=False, name='block2_sepconv2')(x) + x = BatchNormalization(name='block2_sepconv2_bn')(x) + + x = MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='block2_pool')(x) + x = layers.add([x, residual]) + + residual = Conv2D(256, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x) + residual = BatchNormalization()(residual) + + x = Activation('relu', name='block3_sepconv1_act')(x) + x = SeparableConv2D(256, (3, 3), padding='same', use_bias=False, name='block3_sepconv1')(x) + x = BatchNormalization(name='block3_sepconv1_bn')(x) + x = Activation('relu', name='block3_sepconv2_act')(x) + x = SeparableConv2D(256, (3, 3), padding='same', use_bias=False, name='block3_sepconv2')(x) + x = BatchNormalization(name='block3_sepconv2_bn')(x) + + x = MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='block3_pool')(x) + x = layers.add([x, residual]) + + residual = Conv2D(728, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x) + residual = BatchNormalization()(residual) + + x = Activation('relu', name='block4_sepconv1_act')(x) + x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, name='block4_sepconv1')(x) + x = BatchNormalization(name='block4_sepconv1_bn')(x) + x = Activation('relu', name='block4_sepconv2_act')(x) + x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, name='block4_sepconv2')(x) + x = BatchNormalization(name='block4_sepconv2_bn')(x) + + x = MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='block4_pool')(x) + x = layers.add([x, residual]) + + for i in range(8): + residual = x + prefix = 'block' + str(i + 5) + + x = Activation('relu', name=prefix + '_sepconv1_act')(x) + x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, name=prefix + '_sepconv1')(x) + x = BatchNormalization(name=prefix + '_sepconv1_bn')(x) + x = Activation('relu', name=prefix + '_sepconv2_act')(x) + x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, name=prefix + '_sepconv2')(x) + x = BatchNormalization(name=prefix + '_sepconv2_bn')(x) + x = Activation('relu', name=prefix + '_sepconv3_act')(x) + x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, name=prefix + '_sepconv3')(x) + x = BatchNormalization(name=prefix + '_sepconv3_bn')(x) + + x = layers.add([x, residual]) + + residual = Conv2D(1024, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x) + residual = BatchNormalization()(residual) + + x = Activation('relu', name='block13_sepconv1_act')(x) + x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, name='block13_sepconv1')(x) + x = BatchNormalization(name='block13_sepconv1_bn')(x) + x = Activation('relu', name='block13_sepconv2_act')(x) + x = SeparableConv2D(1024, (3, 3), padding='same', use_bias=False, name='block13_sepconv2')(x) + x = BatchNormalization(name='block13_sepconv2_bn')(x) + + x = MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='block13_pool')(x) + x = layers.add([x, residual]) + + x = SeparableConv2D(1536, (3, 3), padding='same', use_bias=False, name='block14_sepconv1')(x) + x = BatchNormalization(name='block14_sepconv1_bn')(x) + x = Activation('relu', name='block14_sepconv1_act')(x) + + x = SeparableConv2D( + 2048, (3, 3), padding='same', + use_bias=False, name='block14_sepconv2', + depthwise_regularizer=keras.regularizers.l2(weight_decay), + pointwise_regularizer=keras.regularizers.l2(weight_decay) + )(x) + x = BatchNormalization(name='block14_sepconv2_bn')(x) + x = Activation('relu', name='block14_sepconv2_act')(x) + + x = GlobalAveragePooling2D(name='avg_pool')(x) + model = Model(img_input, x, name='xception') + + # load weights + weights_path = get_file( + 'xception_weights_tf_dim_ordering_tf_kernels_notop.h5', + TF_WEIGHTS_PATH_NO_TOP, cache_subdir='models' + ) + model.load_weights(weights_path) + + x = model.output + x = Dropout(0.5)(x) + logits = Dense(classes, kernel_regularizer=keras.regularizers.l2(weight_decay))(x) + probabilities = Activation('softmax')(logits) + + model = Model(model.input, probabilities, name='xception') + + for layer in model.layers[:-7]: + layer.trainable = False + + return model diff --git a/xception_fine_tuning.ipynb b/xception_fine_tuning.ipynb new file mode 100644 index 0000000..df45e8a --- /dev/null +++ b/xception_fine_tuning.ipynb @@ -0,0 +1,1191 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "import keras\n", + "from keras.applications.xception import Xception\n", + "# from keras.preprocessing import image\n", + "from keras.models import Model\n", + "from keras.layers import Dropout, Dense, Activation" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "num_class = 256 # caltech 256\n", + "# imagenet input sizse (299,299,3)\n", + "weight_decay=1e-4" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "__________________________________________________________________________________________________\n", + "Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + "input_2 (InputLayer) (None, None, None, 3 0 \n", + "__________________________________________________________________________________________________\n", + "block1_conv1 (Conv2D) (None, None, None, 3 864 input_2[0][0] \n", + "__________________________________________________________________________________________________\n", + "block1_conv1_bn (BatchNormaliza (None, None, None, 3 128 block1_conv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block1_conv1_act (Activation) (None, None, None, 3 0 block1_conv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block1_conv2 (Conv2D) (None, None, None, 6 18432 block1_conv1_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block1_conv2_bn (BatchNormaliza (None, None, None, 6 256 block1_conv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "block1_conv2_act (Activation) (None, None, None, 6 0 block1_conv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block2_sepconv1 (SeparableConv2 (None, None, None, 1 8768 block1_conv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block2_sepconv1_bn (BatchNormal (None, None, None, 1 512 block2_sepconv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block2_sepconv2_act (Activation (None, None, None, 1 0 block2_sepconv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block2_sepconv2 (SeparableConv2 (None, None, None, 1 17536 block2_sepconv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block2_sepconv2_bn (BatchNormal (None, None, None, 1 512 block2_sepconv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_5 (Conv2D) (None, None, None, 1 8192 block1_conv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block2_pool (MaxPooling2D) (None, None, None, 1 0 block2_sepconv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_5 (BatchNor (None, None, None, 1 512 conv2d_5[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_13 (Add) (None, None, None, 1 0 block2_pool[0][0] \n", + " batch_normalization_5[0][0] \n", + "__________________________________________________________________________________________________\n", + "block3_sepconv1_act (Activation (None, None, None, 1 0 add_13[0][0] \n", + "__________________________________________________________________________________________________\n", + "block3_sepconv1 (SeparableConv2 (None, None, None, 2 33920 block3_sepconv1_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block3_sepconv1_bn (BatchNormal (None, None, None, 2 1024 block3_sepconv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block3_sepconv2_act (Activation (None, None, None, 2 0 block3_sepconv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block3_sepconv2 (SeparableConv2 (None, None, None, 2 67840 block3_sepconv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block3_sepconv2_bn (BatchNormal (None, None, None, 2 1024 block3_sepconv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_6 (Conv2D) (None, None, None, 2 32768 add_13[0][0] \n", + "__________________________________________________________________________________________________\n", + "block3_pool (MaxPooling2D) (None, None, None, 2 0 block3_sepconv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_6 (BatchNor (None, None, None, 2 1024 conv2d_6[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_14 (Add) (None, None, None, 2 0 block3_pool[0][0] \n", + " batch_normalization_6[0][0] \n", + "__________________________________________________________________________________________________\n", + "block4_sepconv1_act (Activation (None, None, None, 2 0 add_14[0][0] \n", + "__________________________________________________________________________________________________\n", + "block4_sepconv1 (SeparableConv2 (None, None, None, 7 188672 block4_sepconv1_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block4_sepconv1_bn (BatchNormal (None, None, None, 7 2912 block4_sepconv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block4_sepconv2_act (Activation (None, None, None, 7 0 block4_sepconv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block4_sepconv2 (SeparableConv2 (None, None, None, 7 536536 block4_sepconv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block4_sepconv2_bn (BatchNormal (None, None, None, 7 2912 block4_sepconv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_7 (Conv2D) (None, None, None, 7 186368 add_14[0][0] \n", + "__________________________________________________________________________________________________\n", + "block4_pool (MaxPooling2D) (None, None, None, 7 0 block4_sepconv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_7 (BatchNor (None, None, None, 7 2912 conv2d_7[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_15 (Add) (None, None, None, 7 0 block4_pool[0][0] \n", + " batch_normalization_7[0][0] \n", + "__________________________________________________________________________________________________\n", + "block5_sepconv1_act (Activation (None, None, None, 7 0 add_15[0][0] \n", + "__________________________________________________________________________________________________\n", + "block5_sepconv1 (SeparableConv2 (None, None, None, 7 536536 block5_sepconv1_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block5_sepconv1_bn (BatchNormal (None, None, None, 7 2912 block5_sepconv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block5_sepconv2_act (Activation (None, None, None, 7 0 block5_sepconv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block5_sepconv2 (SeparableConv2 (None, None, None, 7 536536 block5_sepconv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block5_sepconv2_bn (BatchNormal (None, None, None, 7 2912 block5_sepconv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "block5_sepconv3_act (Activation (None, None, None, 7 0 block5_sepconv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block5_sepconv3 (SeparableConv2 (None, None, None, 7 536536 block5_sepconv3_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block5_sepconv3_bn (BatchNormal (None, None, None, 7 2912 block5_sepconv3[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_16 (Add) (None, None, None, 7 0 block5_sepconv3_bn[0][0] \n", + " add_15[0][0] \n", + "__________________________________________________________________________________________________\n", + "block6_sepconv1_act (Activation (None, None, None, 7 0 add_16[0][0] \n", + "__________________________________________________________________________________________________\n", + "block6_sepconv1 (SeparableConv2 (None, None, None, 7 536536 block6_sepconv1_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block6_sepconv1_bn (BatchNormal (None, None, None, 7 2912 block6_sepconv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block6_sepconv2_act (Activation (None, None, None, 7 0 block6_sepconv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block6_sepconv2 (SeparableConv2 (None, None, None, 7 536536 block6_sepconv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block6_sepconv2_bn (BatchNormal (None, None, None, 7 2912 block6_sepconv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "block6_sepconv3_act (Activation (None, None, None, 7 0 block6_sepconv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block6_sepconv3 (SeparableConv2 (None, None, None, 7 536536 block6_sepconv3_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block6_sepconv3_bn (BatchNormal (None, None, None, 7 2912 block6_sepconv3[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_17 (Add) (None, None, None, 7 0 block6_sepconv3_bn[0][0] \n", + " add_16[0][0] \n", + "__________________________________________________________________________________________________\n", + "block7_sepconv1_act (Activation (None, None, None, 7 0 add_17[0][0] \n", + "__________________________________________________________________________________________________\n", + "block7_sepconv1 (SeparableConv2 (None, None, None, 7 536536 block7_sepconv1_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block7_sepconv1_bn (BatchNormal (None, None, None, 7 2912 block7_sepconv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block7_sepconv2_act (Activation (None, None, None, 7 0 block7_sepconv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block7_sepconv2 (SeparableConv2 (None, None, None, 7 536536 block7_sepconv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block7_sepconv2_bn (BatchNormal (None, None, None, 7 2912 block7_sepconv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "block7_sepconv3_act (Activation (None, None, None, 7 0 block7_sepconv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block7_sepconv3 (SeparableConv2 (None, None, None, 7 536536 block7_sepconv3_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block7_sepconv3_bn (BatchNormal (None, None, None, 7 2912 block7_sepconv3[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_18 (Add) (None, None, None, 7 0 block7_sepconv3_bn[0][0] \n", + " add_17[0][0] \n", + "__________________________________________________________________________________________________\n", + "block8_sepconv1_act (Activation (None, None, None, 7 0 add_18[0][0] \n", + "__________________________________________________________________________________________________\n", + "block8_sepconv1 (SeparableConv2 (None, None, None, 7 536536 block8_sepconv1_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block8_sepconv1_bn (BatchNormal (None, None, None, 7 2912 block8_sepconv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block8_sepconv2_act (Activation (None, None, None, 7 0 block8_sepconv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block8_sepconv2 (SeparableConv2 (None, None, None, 7 536536 block8_sepconv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block8_sepconv2_bn (BatchNormal (None, None, None, 7 2912 block8_sepconv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "block8_sepconv3_act (Activation (None, None, None, 7 0 block8_sepconv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block8_sepconv3 (SeparableConv2 (None, None, None, 7 536536 block8_sepconv3_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block8_sepconv3_bn (BatchNormal (None, None, None, 7 2912 block8_sepconv3[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_19 (Add) (None, None, None, 7 0 block8_sepconv3_bn[0][0] \n", + " add_18[0][0] \n", + "__________________________________________________________________________________________________\n", + "block9_sepconv1_act (Activation (None, None, None, 7 0 add_19[0][0] \n", + "__________________________________________________________________________________________________\n", + "block9_sepconv1 (SeparableConv2 (None, None, None, 7 536536 block9_sepconv1_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block9_sepconv1_bn (BatchNormal (None, None, None, 7 2912 block9_sepconv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block9_sepconv2_act (Activation (None, None, None, 7 0 block9_sepconv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block9_sepconv2 (SeparableConv2 (None, None, None, 7 536536 block9_sepconv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block9_sepconv2_bn (BatchNormal (None, None, None, 7 2912 block9_sepconv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "block9_sepconv3_act (Activation (None, None, None, 7 0 block9_sepconv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block9_sepconv3 (SeparableConv2 (None, None, None, 7 536536 block9_sepconv3_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block9_sepconv3_bn (BatchNormal (None, None, None, 7 2912 block9_sepconv3[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_20 (Add) (None, None, None, 7 0 block9_sepconv3_bn[0][0] \n", + " add_19[0][0] \n", + "__________________________________________________________________________________________________\n", + "block10_sepconv1_act (Activatio (None, None, None, 7 0 add_20[0][0] \n", + "__________________________________________________________________________________________________\n", + "block10_sepconv1 (SeparableConv (None, None, None, 7 536536 block10_sepconv1_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block10_sepconv1_bn (BatchNorma (None, None, None, 7 2912 block10_sepconv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block10_sepconv2_act (Activatio (None, None, None, 7 0 block10_sepconv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block10_sepconv2 (SeparableConv (None, None, None, 7 536536 block10_sepconv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block10_sepconv2_bn (BatchNorma (None, None, None, 7 2912 block10_sepconv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "block10_sepconv3_act (Activatio (None, None, None, 7 0 block10_sepconv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block10_sepconv3 (SeparableConv (None, None, None, 7 536536 block10_sepconv3_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block10_sepconv3_bn (BatchNorma (None, None, None, 7 2912 block10_sepconv3[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_21 (Add) (None, None, None, 7 0 block10_sepconv3_bn[0][0] \n", + " add_20[0][0] \n", + "__________________________________________________________________________________________________\n", + "block11_sepconv1_act (Activatio (None, None, None, 7 0 add_21[0][0] \n", + "__________________________________________________________________________________________________\n", + "block11_sepconv1 (SeparableConv (None, None, None, 7 536536 block11_sepconv1_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block11_sepconv1_bn (BatchNorma (None, None, None, 7 2912 block11_sepconv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block11_sepconv2_act (Activatio (None, None, None, 7 0 block11_sepconv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block11_sepconv2 (SeparableConv (None, None, None, 7 536536 block11_sepconv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block11_sepconv2_bn (BatchNorma (None, None, None, 7 2912 block11_sepconv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "block11_sepconv3_act (Activatio (None, None, None, 7 0 block11_sepconv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block11_sepconv3 (SeparableConv (None, None, None, 7 536536 block11_sepconv3_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block11_sepconv3_bn (BatchNorma (None, None, None, 7 2912 block11_sepconv3[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_22 (Add) (None, None, None, 7 0 block11_sepconv3_bn[0][0] \n", + " add_21[0][0] \n", + "__________________________________________________________________________________________________\n", + "block12_sepconv1_act (Activatio (None, None, None, 7 0 add_22[0][0] \n", + "__________________________________________________________________________________________________\n", + "block12_sepconv1 (SeparableConv (None, None, None, 7 536536 block12_sepconv1_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block12_sepconv1_bn (BatchNorma (None, None, None, 7 2912 block12_sepconv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block12_sepconv2_act (Activatio (None, None, None, 7 0 block12_sepconv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block12_sepconv2 (SeparableConv (None, None, None, 7 536536 block12_sepconv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block12_sepconv2_bn (BatchNorma (None, None, None, 7 2912 block12_sepconv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "block12_sepconv3_act (Activatio (None, None, None, 7 0 block12_sepconv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block12_sepconv3 (SeparableConv (None, None, None, 7 536536 block12_sepconv3_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block12_sepconv3_bn (BatchNorma (None, None, None, 7 2912 block12_sepconv3[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_23 (Add) (None, None, None, 7 0 block12_sepconv3_bn[0][0] \n", + " add_22[0][0] \n", + "__________________________________________________________________________________________________\n", + "block13_sepconv1_act (Activatio (None, None, None, 7 0 add_23[0][0] \n", + "__________________________________________________________________________________________________\n", + "block13_sepconv1 (SeparableConv (None, None, None, 7 536536 block13_sepconv1_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block13_sepconv1_bn (BatchNorma (None, None, None, 7 2912 block13_sepconv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block13_sepconv2_act (Activatio (None, None, None, 7 0 block13_sepconv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block13_sepconv2 (SeparableConv (None, None, None, 1 752024 block13_sepconv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block13_sepconv2_bn (BatchNorma (None, None, None, 1 4096 block13_sepconv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_8 (Conv2D) (None, None, None, 1 745472 add_23[0][0] \n", + "__________________________________________________________________________________________________\n", + "block13_pool (MaxPooling2D) (None, None, None, 1 0 block13_sepconv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_8 (BatchNor (None, None, None, 1 4096 conv2d_8[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_24 (Add) (None, None, None, 1 0 block13_pool[0][0] \n", + " batch_normalization_8[0][0] \n", + "__________________________________________________________________________________________________\n", + "block14_sepconv1 (SeparableConv (None, None, None, 1 1582080 add_24[0][0] \n", + "__________________________________________________________________________________________________\n", + "block14_sepconv1_bn (BatchNorma (None, None, None, 1 6144 block14_sepconv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block14_sepconv1_act (Activatio (None, None, None, 1 0 block14_sepconv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block14_sepconv2 (SeparableConv (None, None, None, 2 3159552 block14_sepconv1_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block14_sepconv2_bn (BatchNorma (None, None, None, 2 8192 block14_sepconv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "block14_sepconv2_act (Activatio (None, None, None, 2 0 block14_sepconv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "avg_pool (GlobalAveragePooling2 (None, 2048) 0 block14_sepconv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "predictions (Dense) (None, 1000) 2049000 avg_pool[0][0] \n", + "==================================================================================================\n", + "Total params: 22,910,480\n", + "Trainable params: 22,855,952\n", + "Non-trainable params: 54,528\n", + "__________________________________________________________________________________________________\n" + ] + } + ], + "source": [ + "base_model = Xception(weights='imagenet')\n", + "base_model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "__________________________________________________________________________________________________\n", + "Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + "input_2 (InputLayer) (None, None, None, 3 0 \n", + "__________________________________________________________________________________________________\n", + "block1_conv1 (Conv2D) (None, None, None, 3 864 input_2[0][0] \n", + "__________________________________________________________________________________________________\n", + "block1_conv1_bn (BatchNormaliza (None, None, None, 3 128 block1_conv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block1_conv1_act (Activation) (None, None, None, 3 0 block1_conv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block1_conv2 (Conv2D) (None, None, None, 6 18432 block1_conv1_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block1_conv2_bn (BatchNormaliza (None, None, None, 6 256 block1_conv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "block1_conv2_act (Activation) (None, None, None, 6 0 block1_conv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block2_sepconv1 (SeparableConv2 (None, None, None, 1 8768 block1_conv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block2_sepconv1_bn (BatchNormal (None, None, None, 1 512 block2_sepconv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block2_sepconv2_act (Activation (None, None, None, 1 0 block2_sepconv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block2_sepconv2 (SeparableConv2 (None, None, None, 1 17536 block2_sepconv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block2_sepconv2_bn (BatchNormal (None, None, None, 1 512 block2_sepconv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_5 (Conv2D) (None, None, None, 1 8192 block1_conv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block2_pool (MaxPooling2D) (None, None, None, 1 0 block2_sepconv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_5 (BatchNor (None, None, None, 1 512 conv2d_5[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_13 (Add) (None, None, None, 1 0 block2_pool[0][0] \n", + " batch_normalization_5[0][0] \n", + "__________________________________________________________________________________________________\n", + "block3_sepconv1_act (Activation (None, None, None, 1 0 add_13[0][0] \n", + "__________________________________________________________________________________________________\n", + "block3_sepconv1 (SeparableConv2 (None, None, None, 2 33920 block3_sepconv1_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block3_sepconv1_bn (BatchNormal (None, None, None, 2 1024 block3_sepconv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block3_sepconv2_act (Activation (None, None, None, 2 0 block3_sepconv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block3_sepconv2 (SeparableConv2 (None, None, None, 2 67840 block3_sepconv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block3_sepconv2_bn (BatchNormal (None, None, None, 2 1024 block3_sepconv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_6 (Conv2D) (None, None, None, 2 32768 add_13[0][0] \n", + "__________________________________________________________________________________________________\n", + "block3_pool (MaxPooling2D) (None, None, None, 2 0 block3_sepconv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_6 (BatchNor (None, None, None, 2 1024 conv2d_6[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_14 (Add) (None, None, None, 2 0 block3_pool[0][0] \n", + " batch_normalization_6[0][0] \n", + "__________________________________________________________________________________________________\n", + "block4_sepconv1_act (Activation (None, None, None, 2 0 add_14[0][0] \n", + "__________________________________________________________________________________________________\n", + "block4_sepconv1 (SeparableConv2 (None, None, None, 7 188672 block4_sepconv1_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block4_sepconv1_bn (BatchNormal (None, None, None, 7 2912 block4_sepconv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block4_sepconv2_act (Activation (None, None, None, 7 0 block4_sepconv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block4_sepconv2 (SeparableConv2 (None, None, None, 7 536536 block4_sepconv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block4_sepconv2_bn (BatchNormal (None, None, None, 7 2912 block4_sepconv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_7 (Conv2D) (None, None, None, 7 186368 add_14[0][0] \n", + "__________________________________________________________________________________________________\n", + "block4_pool (MaxPooling2D) (None, None, None, 7 0 block4_sepconv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_7 (BatchNor (None, None, None, 7 2912 conv2d_7[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_15 (Add) (None, None, None, 7 0 block4_pool[0][0] \n", + " batch_normalization_7[0][0] \n", + "__________________________________________________________________________________________________\n", + "block5_sepconv1_act (Activation (None, None, None, 7 0 add_15[0][0] \n", + "__________________________________________________________________________________________________\n", + "block5_sepconv1 (SeparableConv2 (None, None, None, 7 536536 block5_sepconv1_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block5_sepconv1_bn (BatchNormal (None, None, None, 7 2912 block5_sepconv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block5_sepconv2_act (Activation (None, None, None, 7 0 block5_sepconv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block5_sepconv2 (SeparableConv2 (None, None, None, 7 536536 block5_sepconv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block5_sepconv2_bn (BatchNormal (None, None, None, 7 2912 block5_sepconv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "block5_sepconv3_act (Activation (None, None, None, 7 0 block5_sepconv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block5_sepconv3 (SeparableConv2 (None, None, None, 7 536536 block5_sepconv3_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block5_sepconv3_bn (BatchNormal (None, None, None, 7 2912 block5_sepconv3[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_16 (Add) (None, None, None, 7 0 block5_sepconv3_bn[0][0] \n", + " add_15[0][0] \n", + "__________________________________________________________________________________________________\n", + "block6_sepconv1_act (Activation (None, None, None, 7 0 add_16[0][0] \n", + "__________________________________________________________________________________________________\n", + "block6_sepconv1 (SeparableConv2 (None, None, None, 7 536536 block6_sepconv1_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block6_sepconv1_bn (BatchNormal (None, None, None, 7 2912 block6_sepconv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block6_sepconv2_act (Activation (None, None, None, 7 0 block6_sepconv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block6_sepconv2 (SeparableConv2 (None, None, None, 7 536536 block6_sepconv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block6_sepconv2_bn (BatchNormal (None, None, None, 7 2912 block6_sepconv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "block6_sepconv3_act (Activation (None, None, None, 7 0 block6_sepconv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block6_sepconv3 (SeparableConv2 (None, None, None, 7 536536 block6_sepconv3_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block6_sepconv3_bn (BatchNormal (None, None, None, 7 2912 block6_sepconv3[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_17 (Add) (None, None, None, 7 0 block6_sepconv3_bn[0][0] \n", + " add_16[0][0] \n", + "__________________________________________________________________________________________________\n", + "block7_sepconv1_act (Activation (None, None, None, 7 0 add_17[0][0] \n", + "__________________________________________________________________________________________________\n", + "block7_sepconv1 (SeparableConv2 (None, None, None, 7 536536 block7_sepconv1_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block7_sepconv1_bn (BatchNormal (None, None, None, 7 2912 block7_sepconv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block7_sepconv2_act (Activation (None, None, None, 7 0 block7_sepconv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block7_sepconv2 (SeparableConv2 (None, None, None, 7 536536 block7_sepconv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block7_sepconv2_bn (BatchNormal (None, None, None, 7 2912 block7_sepconv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "block7_sepconv3_act (Activation (None, None, None, 7 0 block7_sepconv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block7_sepconv3 (SeparableConv2 (None, None, None, 7 536536 block7_sepconv3_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block7_sepconv3_bn (BatchNormal (None, None, None, 7 2912 block7_sepconv3[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_18 (Add) (None, None, None, 7 0 block7_sepconv3_bn[0][0] \n", + " add_17[0][0] \n", + "__________________________________________________________________________________________________\n", + "block8_sepconv1_act (Activation (None, None, None, 7 0 add_18[0][0] \n", + "__________________________________________________________________________________________________\n", + "block8_sepconv1 (SeparableConv2 (None, None, None, 7 536536 block8_sepconv1_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block8_sepconv1_bn (BatchNormal (None, None, None, 7 2912 block8_sepconv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block8_sepconv2_act (Activation (None, None, None, 7 0 block8_sepconv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block8_sepconv2 (SeparableConv2 (None, None, None, 7 536536 block8_sepconv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block8_sepconv2_bn (BatchNormal (None, None, None, 7 2912 block8_sepconv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "block8_sepconv3_act (Activation (None, None, None, 7 0 block8_sepconv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block8_sepconv3 (SeparableConv2 (None, None, None, 7 536536 block8_sepconv3_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block8_sepconv3_bn (BatchNormal (None, None, None, 7 2912 block8_sepconv3[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_19 (Add) (None, None, None, 7 0 block8_sepconv3_bn[0][0] \n", + " add_18[0][0] \n", + "__________________________________________________________________________________________________\n", + "block9_sepconv1_act (Activation (None, None, None, 7 0 add_19[0][0] \n", + "__________________________________________________________________________________________________\n", + "block9_sepconv1 (SeparableConv2 (None, None, None, 7 536536 block9_sepconv1_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block9_sepconv1_bn (BatchNormal (None, None, None, 7 2912 block9_sepconv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block9_sepconv2_act (Activation (None, None, None, 7 0 block9_sepconv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block9_sepconv2 (SeparableConv2 (None, None, None, 7 536536 block9_sepconv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block9_sepconv2_bn (BatchNormal (None, None, None, 7 2912 block9_sepconv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "block9_sepconv3_act (Activation (None, None, None, 7 0 block9_sepconv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block9_sepconv3 (SeparableConv2 (None, None, None, 7 536536 block9_sepconv3_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block9_sepconv3_bn (BatchNormal (None, None, None, 7 2912 block9_sepconv3[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_20 (Add) (None, None, None, 7 0 block9_sepconv3_bn[0][0] \n", + " add_19[0][0] \n", + "__________________________________________________________________________________________________\n", + "block10_sepconv1_act (Activatio (None, None, None, 7 0 add_20[0][0] \n", + "__________________________________________________________________________________________________\n", + "block10_sepconv1 (SeparableConv (None, None, None, 7 536536 block10_sepconv1_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block10_sepconv1_bn (BatchNorma (None, None, None, 7 2912 block10_sepconv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block10_sepconv2_act (Activatio (None, None, None, 7 0 block10_sepconv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block10_sepconv2 (SeparableConv (None, None, None, 7 536536 block10_sepconv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block10_sepconv2_bn (BatchNorma (None, None, None, 7 2912 block10_sepconv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "block10_sepconv3_act (Activatio (None, None, None, 7 0 block10_sepconv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block10_sepconv3 (SeparableConv (None, None, None, 7 536536 block10_sepconv3_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block10_sepconv3_bn (BatchNorma (None, None, None, 7 2912 block10_sepconv3[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_21 (Add) (None, None, None, 7 0 block10_sepconv3_bn[0][0] \n", + " add_20[0][0] \n", + "__________________________________________________________________________________________________\n", + "block11_sepconv1_act (Activatio (None, None, None, 7 0 add_21[0][0] \n", + "__________________________________________________________________________________________________\n", + "block11_sepconv1 (SeparableConv (None, None, None, 7 536536 block11_sepconv1_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block11_sepconv1_bn (BatchNorma (None, None, None, 7 2912 block11_sepconv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block11_sepconv2_act (Activatio (None, None, None, 7 0 block11_sepconv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block11_sepconv2 (SeparableConv (None, None, None, 7 536536 block11_sepconv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block11_sepconv2_bn (BatchNorma (None, None, None, 7 2912 block11_sepconv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "block11_sepconv3_act (Activatio (None, None, None, 7 0 block11_sepconv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block11_sepconv3 (SeparableConv (None, None, None, 7 536536 block11_sepconv3_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block11_sepconv3_bn (BatchNorma (None, None, None, 7 2912 block11_sepconv3[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_22 (Add) (None, None, None, 7 0 block11_sepconv3_bn[0][0] \n", + " add_21[0][0] \n", + "__________________________________________________________________________________________________\n", + "block12_sepconv1_act (Activatio (None, None, None, 7 0 add_22[0][0] \n", + "__________________________________________________________________________________________________\n", + "block12_sepconv1 (SeparableConv (None, None, None, 7 536536 block12_sepconv1_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block12_sepconv1_bn (BatchNorma (None, None, None, 7 2912 block12_sepconv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block12_sepconv2_act (Activatio (None, None, None, 7 0 block12_sepconv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block12_sepconv2 (SeparableConv (None, None, None, 7 536536 block12_sepconv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block12_sepconv2_bn (BatchNorma (None, None, None, 7 2912 block12_sepconv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "block12_sepconv3_act (Activatio (None, None, None, 7 0 block12_sepconv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block12_sepconv3 (SeparableConv (None, None, None, 7 536536 block12_sepconv3_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block12_sepconv3_bn (BatchNorma (None, None, None, 7 2912 block12_sepconv3[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_23 (Add) (None, None, None, 7 0 block12_sepconv3_bn[0][0] \n", + " add_22[0][0] \n", + "__________________________________________________________________________________________________\n", + "block13_sepconv1_act (Activatio (None, None, None, 7 0 add_23[0][0] \n", + "__________________________________________________________________________________________________\n", + "block13_sepconv1 (SeparableConv (None, None, None, 7 536536 block13_sepconv1_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block13_sepconv1_bn (BatchNorma (None, None, None, 7 2912 block13_sepconv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block13_sepconv2_act (Activatio (None, None, None, 7 0 block13_sepconv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block13_sepconv2 (SeparableConv (None, None, None, 1 752024 block13_sepconv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block13_sepconv2_bn (BatchNorma (None, None, None, 1 4096 block13_sepconv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_8 (Conv2D) (None, None, None, 1 745472 add_23[0][0] \n", + "__________________________________________________________________________________________________\n", + "block13_pool (MaxPooling2D) (None, None, None, 1 0 block13_sepconv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_8 (BatchNor (None, None, None, 1 4096 conv2d_8[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_24 (Add) (None, None, None, 1 0 block13_pool[0][0] \n", + " batch_normalization_8[0][0] \n", + "__________________________________________________________________________________________________\n", + "block14_sepconv1 (SeparableConv (None, None, None, 1 1582080 add_24[0][0] \n", + "__________________________________________________________________________________________________\n", + "block14_sepconv1_bn (BatchNorma (None, None, None, 1 6144 block14_sepconv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block14_sepconv1_act (Activatio (None, None, None, 1 0 block14_sepconv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block14_sepconv2 (SeparableConv (None, None, None, 2 3159552 block14_sepconv1_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block14_sepconv2_bn (BatchNorma (None, None, None, 2 8192 block14_sepconv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "block14_sepconv2_act (Activatio (None, None, None, 2 0 block14_sepconv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "avg_pool (GlobalAveragePooling2 (None, 2048) 0 block14_sepconv2_act[0][0] \n", + "==================================================================================================\n", + "Total params: 20,861,480\n", + "Trainable params: 20,806,952\n", + "Non-trainable params: 54,528\n", + "__________________________________________________________________________________________________\n", + "Tensor(\"predictions_1/Softmax:0\", shape=(?, 1000), dtype=float32)\n" + ] + } + ], + "source": [ + "base_model.layers.pop()\n", + "base_model.summary()\n", + "print(base_model.output) # not changed after layers.pop() is called" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tensor(\"avg_pool_1/Mean:0\", shape=(?, 2048), dtype=float32) avg_pool\n", + "(None, None, None, 3)\n", + "xception\n" + ] + } + ], + "source": [ + "print(base_model.layers[-1].output, base_model.layers[-1].name)\n", + "print(base_model.layers[0].input_shape)\n", + "print(base_model.name)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "x = base_model.layers[-1].output\n", + "x = Dropout(0.5)(x)\n", + "\n", + "logits = Dense(num_class, kernel_regularizer= keras.regularizers.l2(weight_decay), name='logits')(x)\n", + "probabilities = Activation('softmax')(logits)\n", + "\n", + "model = Model(base_model.input, probabilities, name='xception')" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "__________________________________________________________________________________________________\n", + "Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + "input_2 (InputLayer) (None, None, None, 3 0 \n", + "__________________________________________________________________________________________________\n", + "block1_conv1 (Conv2D) (None, None, None, 3 864 input_2[0][0] \n", + "__________________________________________________________________________________________________\n", + "block1_conv1_bn (BatchNormaliza (None, None, None, 3 128 block1_conv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block1_conv1_act (Activation) (None, None, None, 3 0 block1_conv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block1_conv2 (Conv2D) (None, None, None, 6 18432 block1_conv1_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block1_conv2_bn (BatchNormaliza (None, None, None, 6 256 block1_conv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "block1_conv2_act (Activation) (None, None, None, 6 0 block1_conv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block2_sepconv1 (SeparableConv2 (None, None, None, 1 8768 block1_conv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block2_sepconv1_bn (BatchNormal (None, None, None, 1 512 block2_sepconv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block2_sepconv2_act (Activation (None, None, None, 1 0 block2_sepconv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block2_sepconv2 (SeparableConv2 (None, None, None, 1 17536 block2_sepconv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block2_sepconv2_bn (BatchNormal (None, None, None, 1 512 block2_sepconv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_5 (Conv2D) (None, None, None, 1 8192 block1_conv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block2_pool (MaxPooling2D) (None, None, None, 1 0 block2_sepconv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_5 (BatchNor (None, None, None, 1 512 conv2d_5[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_13 (Add) (None, None, None, 1 0 block2_pool[0][0] \n", + " batch_normalization_5[0][0] \n", + "__________________________________________________________________________________________________\n", + "block3_sepconv1_act (Activation (None, None, None, 1 0 add_13[0][0] \n", + "__________________________________________________________________________________________________\n", + "block3_sepconv1 (SeparableConv2 (None, None, None, 2 33920 block3_sepconv1_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block3_sepconv1_bn (BatchNormal (None, None, None, 2 1024 block3_sepconv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block3_sepconv2_act (Activation (None, None, None, 2 0 block3_sepconv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block3_sepconv2 (SeparableConv2 (None, None, None, 2 67840 block3_sepconv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block3_sepconv2_bn (BatchNormal (None, None, None, 2 1024 block3_sepconv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_6 (Conv2D) (None, None, None, 2 32768 add_13[0][0] \n", + "__________________________________________________________________________________________________\n", + "block3_pool (MaxPooling2D) (None, None, None, 2 0 block3_sepconv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_6 (BatchNor (None, None, None, 2 1024 conv2d_6[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_14 (Add) (None, None, None, 2 0 block3_pool[0][0] \n", + " batch_normalization_6[0][0] \n", + "__________________________________________________________________________________________________\n", + "block4_sepconv1_act (Activation (None, None, None, 2 0 add_14[0][0] \n", + "__________________________________________________________________________________________________\n", + "block4_sepconv1 (SeparableConv2 (None, None, None, 7 188672 block4_sepconv1_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block4_sepconv1_bn (BatchNormal (None, None, None, 7 2912 block4_sepconv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block4_sepconv2_act (Activation (None, None, None, 7 0 block4_sepconv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block4_sepconv2 (SeparableConv2 (None, None, None, 7 536536 block4_sepconv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block4_sepconv2_bn (BatchNormal (None, None, None, 7 2912 block4_sepconv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_7 (Conv2D) (None, None, None, 7 186368 add_14[0][0] \n", + "__________________________________________________________________________________________________\n", + "block4_pool (MaxPooling2D) (None, None, None, 7 0 block4_sepconv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_7 (BatchNor (None, None, None, 7 2912 conv2d_7[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_15 (Add) (None, None, None, 7 0 block4_pool[0][0] \n", + " batch_normalization_7[0][0] \n", + "__________________________________________________________________________________________________\n", + "block5_sepconv1_act (Activation (None, None, None, 7 0 add_15[0][0] \n", + "__________________________________________________________________________________________________\n", + "block5_sepconv1 (SeparableConv2 (None, None, None, 7 536536 block5_sepconv1_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block5_sepconv1_bn (BatchNormal (None, None, None, 7 2912 block5_sepconv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block5_sepconv2_act (Activation (None, None, None, 7 0 block5_sepconv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block5_sepconv2 (SeparableConv2 (None, None, None, 7 536536 block5_sepconv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block5_sepconv2_bn (BatchNormal (None, None, None, 7 2912 block5_sepconv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "block5_sepconv3_act (Activation (None, None, None, 7 0 block5_sepconv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block5_sepconv3 (SeparableConv2 (None, None, None, 7 536536 block5_sepconv3_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block5_sepconv3_bn (BatchNormal (None, None, None, 7 2912 block5_sepconv3[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_16 (Add) (None, None, None, 7 0 block5_sepconv3_bn[0][0] \n", + " add_15[0][0] \n", + "__________________________________________________________________________________________________\n", + "block6_sepconv1_act (Activation (None, None, None, 7 0 add_16[0][0] \n", + "__________________________________________________________________________________________________\n", + "block6_sepconv1 (SeparableConv2 (None, None, None, 7 536536 block6_sepconv1_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block6_sepconv1_bn (BatchNormal (None, None, None, 7 2912 block6_sepconv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block6_sepconv2_act (Activation (None, None, None, 7 0 block6_sepconv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block6_sepconv2 (SeparableConv2 (None, None, None, 7 536536 block6_sepconv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block6_sepconv2_bn (BatchNormal (None, None, None, 7 2912 block6_sepconv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "block6_sepconv3_act (Activation (None, None, None, 7 0 block6_sepconv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block6_sepconv3 (SeparableConv2 (None, None, None, 7 536536 block6_sepconv3_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block6_sepconv3_bn (BatchNormal (None, None, None, 7 2912 block6_sepconv3[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_17 (Add) (None, None, None, 7 0 block6_sepconv3_bn[0][0] \n", + " add_16[0][0] \n", + "__________________________________________________________________________________________________\n", + "block7_sepconv1_act (Activation (None, None, None, 7 0 add_17[0][0] \n", + "__________________________________________________________________________________________________\n", + "block7_sepconv1 (SeparableConv2 (None, None, None, 7 536536 block7_sepconv1_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block7_sepconv1_bn (BatchNormal (None, None, None, 7 2912 block7_sepconv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block7_sepconv2_act (Activation (None, None, None, 7 0 block7_sepconv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block7_sepconv2 (SeparableConv2 (None, None, None, 7 536536 block7_sepconv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block7_sepconv2_bn (BatchNormal (None, None, None, 7 2912 block7_sepconv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "block7_sepconv3_act (Activation (None, None, None, 7 0 block7_sepconv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block7_sepconv3 (SeparableConv2 (None, None, None, 7 536536 block7_sepconv3_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block7_sepconv3_bn (BatchNormal (None, None, None, 7 2912 block7_sepconv3[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_18 (Add) (None, None, None, 7 0 block7_sepconv3_bn[0][0] \n", + " add_17[0][0] \n", + "__________________________________________________________________________________________________\n", + "block8_sepconv1_act (Activation (None, None, None, 7 0 add_18[0][0] \n", + "__________________________________________________________________________________________________\n", + "block8_sepconv1 (SeparableConv2 (None, None, None, 7 536536 block8_sepconv1_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block8_sepconv1_bn (BatchNormal (None, None, None, 7 2912 block8_sepconv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block8_sepconv2_act (Activation (None, None, None, 7 0 block8_sepconv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block8_sepconv2 (SeparableConv2 (None, None, None, 7 536536 block8_sepconv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block8_sepconv2_bn (BatchNormal (None, None, None, 7 2912 block8_sepconv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "block8_sepconv3_act (Activation (None, None, None, 7 0 block8_sepconv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block8_sepconv3 (SeparableConv2 (None, None, None, 7 536536 block8_sepconv3_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block8_sepconv3_bn (BatchNormal (None, None, None, 7 2912 block8_sepconv3[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_19 (Add) (None, None, None, 7 0 block8_sepconv3_bn[0][0] \n", + " add_18[0][0] \n", + "__________________________________________________________________________________________________\n", + "block9_sepconv1_act (Activation (None, None, None, 7 0 add_19[0][0] \n", + "__________________________________________________________________________________________________\n", + "block9_sepconv1 (SeparableConv2 (None, None, None, 7 536536 block9_sepconv1_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block9_sepconv1_bn (BatchNormal (None, None, None, 7 2912 block9_sepconv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block9_sepconv2_act (Activation (None, None, None, 7 0 block9_sepconv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block9_sepconv2 (SeparableConv2 (None, None, None, 7 536536 block9_sepconv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block9_sepconv2_bn (BatchNormal (None, None, None, 7 2912 block9_sepconv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "block9_sepconv3_act (Activation (None, None, None, 7 0 block9_sepconv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block9_sepconv3 (SeparableConv2 (None, None, None, 7 536536 block9_sepconv3_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block9_sepconv3_bn (BatchNormal (None, None, None, 7 2912 block9_sepconv3[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_20 (Add) (None, None, None, 7 0 block9_sepconv3_bn[0][0] \n", + " add_19[0][0] \n", + "__________________________________________________________________________________________________\n", + "block10_sepconv1_act (Activatio (None, None, None, 7 0 add_20[0][0] \n", + "__________________________________________________________________________________________________\n", + "block10_sepconv1 (SeparableConv (None, None, None, 7 536536 block10_sepconv1_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block10_sepconv1_bn (BatchNorma (None, None, None, 7 2912 block10_sepconv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block10_sepconv2_act (Activatio (None, None, None, 7 0 block10_sepconv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block10_sepconv2 (SeparableConv (None, None, None, 7 536536 block10_sepconv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block10_sepconv2_bn (BatchNorma (None, None, None, 7 2912 block10_sepconv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "block10_sepconv3_act (Activatio (None, None, None, 7 0 block10_sepconv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block10_sepconv3 (SeparableConv (None, None, None, 7 536536 block10_sepconv3_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block10_sepconv3_bn (BatchNorma (None, None, None, 7 2912 block10_sepconv3[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_21 (Add) (None, None, None, 7 0 block10_sepconv3_bn[0][0] \n", + " add_20[0][0] \n", + "__________________________________________________________________________________________________\n", + "block11_sepconv1_act (Activatio (None, None, None, 7 0 add_21[0][0] \n", + "__________________________________________________________________________________________________\n", + "block11_sepconv1 (SeparableConv (None, None, None, 7 536536 block11_sepconv1_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block11_sepconv1_bn (BatchNorma (None, None, None, 7 2912 block11_sepconv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block11_sepconv2_act (Activatio (None, None, None, 7 0 block11_sepconv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block11_sepconv2 (SeparableConv (None, None, None, 7 536536 block11_sepconv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block11_sepconv2_bn (BatchNorma (None, None, None, 7 2912 block11_sepconv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "block11_sepconv3_act (Activatio (None, None, None, 7 0 block11_sepconv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block11_sepconv3 (SeparableConv (None, None, None, 7 536536 block11_sepconv3_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block11_sepconv3_bn (BatchNorma (None, None, None, 7 2912 block11_sepconv3[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_22 (Add) (None, None, None, 7 0 block11_sepconv3_bn[0][0] \n", + " add_21[0][0] \n", + "__________________________________________________________________________________________________\n", + "block12_sepconv1_act (Activatio (None, None, None, 7 0 add_22[0][0] \n", + "__________________________________________________________________________________________________\n", + "block12_sepconv1 (SeparableConv (None, None, None, 7 536536 block12_sepconv1_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block12_sepconv1_bn (BatchNorma (None, None, None, 7 2912 block12_sepconv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block12_sepconv2_act (Activatio (None, None, None, 7 0 block12_sepconv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block12_sepconv2 (SeparableConv (None, None, None, 7 536536 block12_sepconv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block12_sepconv2_bn (BatchNorma (None, None, None, 7 2912 block12_sepconv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "block12_sepconv3_act (Activatio (None, None, None, 7 0 block12_sepconv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block12_sepconv3 (SeparableConv (None, None, None, 7 536536 block12_sepconv3_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block12_sepconv3_bn (BatchNorma (None, None, None, 7 2912 block12_sepconv3[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_23 (Add) (None, None, None, 7 0 block12_sepconv3_bn[0][0] \n", + " add_22[0][0] \n", + "__________________________________________________________________________________________________\n", + "block13_sepconv1_act (Activatio (None, None, None, 7 0 add_23[0][0] \n", + "__________________________________________________________________________________________________\n", + "block13_sepconv1 (SeparableConv (None, None, None, 7 536536 block13_sepconv1_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block13_sepconv1_bn (BatchNorma (None, None, None, 7 2912 block13_sepconv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block13_sepconv2_act (Activatio (None, None, None, 7 0 block13_sepconv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block13_sepconv2 (SeparableConv (None, None, None, 1 752024 block13_sepconv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block13_sepconv2_bn (BatchNorma (None, None, None, 1 4096 block13_sepconv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_8 (Conv2D) (None, None, None, 1 745472 add_23[0][0] \n", + "__________________________________________________________________________________________________\n", + "block13_pool (MaxPooling2D) (None, None, None, 1 0 block13_sepconv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_8 (BatchNor (None, None, None, 1 4096 conv2d_8[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_24 (Add) (None, None, None, 1 0 block13_pool[0][0] \n", + " batch_normalization_8[0][0] \n", + "__________________________________________________________________________________________________\n", + "block14_sepconv1 (SeparableConv (None, None, None, 1 1582080 add_24[0][0] \n", + "__________________________________________________________________________________________________\n", + "block14_sepconv1_bn (BatchNorma (None, None, None, 1 6144 block14_sepconv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "block14_sepconv1_act (Activatio (None, None, None, 1 0 block14_sepconv1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "block14_sepconv2 (SeparableConv (None, None, None, 2 3159552 block14_sepconv1_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "block14_sepconv2_bn (BatchNorma (None, None, None, 2 8192 block14_sepconv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "block14_sepconv2_act (Activatio (None, None, None, 2 0 block14_sepconv2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "avg_pool (GlobalAveragePooling2 (None, 2048) 0 block14_sepconv2_act[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_4 (Dropout) (None, 2048) 0 avg_pool[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_2 (Dense) (None, 256) 524544 dropout_4[0][0] \n", + "__________________________________________________________________________________________________\n", + "activation_2 (Activation) (None, 256) 0 dense_2[0][0] \n", + "==================================================================================================\n", + "Total params: 21,386,024\n", + "Trainable params: 21,331,496\n", + "Non-trainable params: 54,528\n", + "__________________________________________________________________________________________________\n", + "xception\n" + ] + } + ], + "source": [ + "model.summary()\n", + "print (model.name)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "block14_sepconv2\n" + ] + } + ], + "source": [ + "print (model.layers[-7].name)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "input_2\n", + "block1_conv1\n", + "block1_conv1_bn\n", + "block1_conv1_act\n", + "block1_conv2\n", + "block1_conv2_bn\n", + "block1_conv2_act\n", + "block2_sepconv1\n", + "block2_sepconv1_bn\n", + "block2_sepconv2_act\n", + "block2_sepconv2\n", + "block2_sepconv2_bn\n", + "conv2d_5\n", + "block2_pool\n", + "batch_normalization_5\n", + "add_13\n", + "block3_sepconv1_act\n", + "block3_sepconv1\n", + "block3_sepconv1_bn\n", + "block3_sepconv2_act\n", + "block3_sepconv2\n", + "block3_sepconv2_bn\n", + "conv2d_6\n", + "block3_pool\n", + "batch_normalization_6\n", + "add_14\n", + "block4_sepconv1_act\n", + "block4_sepconv1\n", + "block4_sepconv1_bn\n", + "block4_sepconv2_act\n", + "block4_sepconv2\n", + "block4_sepconv2_bn\n", + "conv2d_7\n", + "block4_pool\n", + "batch_normalization_7\n", + "add_15\n", + "block5_sepconv1_act\n", + "block5_sepconv1\n", + "block5_sepconv1_bn\n", + "block5_sepconv2_act\n", + "block5_sepconv2\n", + "block5_sepconv2_bn\n", + "block5_sepconv3_act\n", + "block5_sepconv3\n", + "block5_sepconv3_bn\n", + "add_16\n", + "block6_sepconv1_act\n", + "block6_sepconv1\n", + "block6_sepconv1_bn\n", + "block6_sepconv2_act\n", + "block6_sepconv2\n", + "block6_sepconv2_bn\n", + "block6_sepconv3_act\n", + "block6_sepconv3\n", + "block6_sepconv3_bn\n", + "add_17\n", + "block7_sepconv1_act\n", + "block7_sepconv1\n", + "block7_sepconv1_bn\n", + "block7_sepconv2_act\n", + "block7_sepconv2\n", + "block7_sepconv2_bn\n", + "block7_sepconv3_act\n", + "block7_sepconv3\n", + "block7_sepconv3_bn\n", + "add_18\n", + "block8_sepconv1_act\n", + "block8_sepconv1\n", + "block8_sepconv1_bn\n", + "block8_sepconv2_act\n", + "block8_sepconv2\n", + "block8_sepconv2_bn\n", + "block8_sepconv3_act\n", + "block8_sepconv3\n", + "block8_sepconv3_bn\n", + "add_19\n", + "block9_sepconv1_act\n", + "block9_sepconv1\n", + "block9_sepconv1_bn\n", + "block9_sepconv2_act\n", + "block9_sepconv2\n", + "block9_sepconv2_bn\n", + "block9_sepconv3_act\n", + "block9_sepconv3\n", + "block9_sepconv3_bn\n", + "add_20\n", + "block10_sepconv1_act\n", + "block10_sepconv1\n", + "block10_sepconv1_bn\n", + "block10_sepconv2_act\n", + "block10_sepconv2\n", + "block10_sepconv2_bn\n", + "block10_sepconv3_act\n", + "block10_sepconv3\n", + "block10_sepconv3_bn\n", + "add_21\n", + "block11_sepconv1_act\n", + "block11_sepconv1\n", + "block11_sepconv1_bn\n", + "block11_sepconv2_act\n", + "block11_sepconv2\n", + "block11_sepconv2_bn\n", + "block11_sepconv3_act\n", + "block11_sepconv3\n", + "block11_sepconv3_bn\n", + "add_22\n", + "block12_sepconv1_act\n", + "block12_sepconv1\n", + "block12_sepconv1_bn\n", + "block12_sepconv2_act\n", + "block12_sepconv2\n", + "block12_sepconv2_bn\n", + "block12_sepconv3_act\n", + "block12_sepconv3\n", + "block12_sepconv3_bn\n", + "add_23\n", + "block13_sepconv1_act\n", + "block13_sepconv1\n", + "block13_sepconv1_bn\n", + "block13_sepconv2_act\n", + "block13_sepconv2\n", + "block13_sepconv2_bn\n", + "conv2d_8\n", + "block13_pool\n", + "batch_normalization_8\n", + "add_24\n", + "block14_sepconv1\n", + "block14_sepconv1_bn\n", + "block14_sepconv1_act\n" + ] + } + ], + "source": [ + "for layer in model.layers[:-7]: # why -7? layer upto block14_sepconv1_act, from block14_sepconv2 is trainable? \n", + " print (layer.name)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "input_2\n", + "block1_conv1\n" + ] + } + ], + "source": [ + "for layer in model.layers[:2]:\n", + " print (layer.name)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}

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