diff --git a/457.py b/457.py new file mode 100644 index 000000000..9c915912b --- /dev/null +++ b/457.py @@ -0,0 +1,244 @@ + +# coding: utf-8 + +# In[ ]: + +from keras import backend as K +K.set_image_dim_ordering('th') +from keras.models import Sequential +from keras.layers.core import Dense, Dropout, Activation, Flatten +from keras.layers import Convolution2D, MaxPooling2D, Flatten +from keras.layers.convolutional import Convolution2D, MaxPooling2D +from keras.optimizers import SGD,RMSprop,adam +from keras.utils import np_utils +from keras.preprocessing.image import ImageDataGenerator +from matplotlib import pyplot +from sklearn.utils import shuffle +from sklearn.cross_validation import train_test_split +import numpy as np +import matplotlib.pyplot as plt +import matplotlib +import os +import theano +from PIL import Image +from numpy import * + + + +# input image dimensions +img_rows, img_cols = 32,32 + +# number of channels +img_channels =1 + + +# data + +path1 = 'C:/Users/dundeva/Documents/CroppedImages' #path of folder of images +path2 = 'C:/Users/dundeva/Documents/AugmentedImages' #path of folder to save images + +listing = os.listdir(path1) +num_samples=size(listing) +print num_samples + + +for file in listing: + im = Image.open(path1 + '//' + file) + img = im.resize((img_rows,img_cols)) + gray = img.convert('L') + #need to do some more processing here + gray.save(path2 +'//' + file, "JPEG") + +imlist = os.listdir(path1) + +im1 = array(Image.open('C:/Users/dundeva/Documents/AugmentedImages' + '//'+ imlist[0])) # open one image to get size +m,n = im1.shape[0:2] # get the size of the images +imnbr = len(imlist) # get the number of images + +# create matrix to store all flattened images +immatrix = array([array(Image.open('C:/Users/dundeva/Documents/AugmentedImages'+ '//' + im2)).flatten() + for im2 in imlist],'f') + +label=np.ones((num_samples,),dtype = int) +label[0:89]=0 +label[89:187]=1 +label[187:]=2 + + +data,Label = shuffle(immatrix,label, random_state=2) +train_data = [data,Label] + +img=immatrix[167].reshape(img_rows,img_cols) +plt.imshow(img) +plt.imshow(img,cmap='gray') +print (train_data[0].shape) +print (train_data[1].shape) + + + +#batch_size to train +batch_size = 32 +# number of output classes +nb_classes =10 +# number of epochs to train +nb_epoch =200 +data_augmentation = True + +# number of convolutional filters to use +nb_filters = 32 +# size of pooling area for max pooling +nb_pool = 3 +# convolution kernel size +nb_conv = 3 + +(X, y) = (train_data[0],train_data[1]) + + +# split X and y into training and testing sets + +X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=4) + + +X_train = X_train.reshape(X_train.shape[0], 1, img_rows, img_cols) +X_test = X_test.reshape(X_test.shape[0], 1, img_rows, img_cols) + +X_train = X_train.astype('float32') +X_test = X_test.astype('float32') + +X_train /= 255 +X_test /= 255 + +print('X_train shape:', X_train.shape) +print(X_train.shape[0], 'train samples') +print(X_test.shape[0], 'test samples') + +# convert class vectors to binary class matrices +Y_train = np_utils.to_categorical(y_train, nb_classes) +Y_test = np_utils.to_categorical(y_test, nb_classes) + +i = 100 +plt.imshow(X_train[i, 0], interpolation='nearest') +print("label : ", Y_train[i,:]) + + +model = Sequential() +model.add(Convolution2D(32, 4, 4, border_mode='same', + input_shape=X_train.shape[1:])) +model.add(Activation('relu')) +model.add(Convolution2D(32, 4, 4)) +model.add(Activation('relu')) +model.add(MaxPooling2D(pool_size=(2, 2))) +model.add(Dropout(0.5)) + +model.add(Convolution2D(64, 3, 3, border_mode='same')) +model.add(Activation('relu')) +model.add(Convolution2D(64, 3, 3)) +model.add(Activation('relu')) +model.add(MaxPooling2D(pool_size=(2, 2))) +model.add(Dropout(0.5)) + +model.add(Flatten()) +model.add(Dense(512)) +model.add(Activation('relu')) +model.add(Dropout(0.5)) +model.add(Dense(nb_classes)) +model.add(Activation('softmax')) + + +#sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True) +model.compile(loss='categorical_crossentropy', + optimizer='adadelta', + metrics=['accuracy']) + + +hist = model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch, + show_accuracy=True, verbose=1, validation_data=(X_test, Y_test)) + + +hist = model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch, + show_accuracy=True, verbose=1, validation_split=0.2) + + +# visualizing losses and accuracy + +train_loss=hist.history['loss'] +val_loss=hist.history['val_loss'] +train_acc=hist.history['acc'] +val_acc=hist.history['val_acc'] +xc=range(nb_epoch) + +plt.figure(1,figsize=(7,5)) +plt.plot(xc,train_loss) +plt.plot(xc,val_loss) +plt.xlabel('num of Epochs') +plt.ylabel('loss') +plt.title('train_loss vs val_loss') +plt.grid(True) +plt.legend(['train','val']) +print plt.style.available # use bmh, classic,ggplot for big pictures +plt.style.use(['classic']) + +plt.figure(2,figsize=(7,5)) +plt.plot(xc,train_acc) +plt.plot(xc,val_acc) +plt.xlabel('num of Epochs') +plt.ylabel('accuracy') +plt.title('train_acc vs val_acc') +plt.grid(True) +plt.legend(['train','val'],loc=4) +#print plt.style.available # use bmh, classic,ggplot for big pictures +plt.style.use(['classic']) + +X_train = X_train.astype('float32') +X_test = X_test.astype('float32') +X_train /= 255 +X_test /= 255 + +if not data_augmentation: + print('Not using data augmentation.') + model.fit(X_train, Y_train, + batch_size=batch_size, + nb_epoch=nb_epoch, + validation_data=(X_test, Y_test), + shuffle=True) +else: + print('Using real-time data augmentation.') + + # this will do preprocessing and realtime data augmentation + datagen = ImageDataGenerator( + featurewise_center=False, # set input mean to 0 over the dataset + samplewise_center=False, # set each sample mean to 0 + featurewise_std_normalization=False, # divide inputs by std of the dataset + samplewise_std_normalization=False, # divide each input by its std + zca_whitening=False, # apply ZCA whitening + rotation_range=0, # randomly rotate images in the range (degrees, 0 to 180) + width_shift_range=0.1, # randomly shift images horizontally (fraction of total width) + height_shift_range=0.1, # randomly shift images vertically (fraction of total height) + horizontal_flip=True, # randomly flip images + vertical_flip=False) # randomly flip images + + # compute quantities required for featurewise normalization + # (std, mean, and principal components if ZCA whitening is applied) + datagen.fit(X_train) + + # fit the model on the batches generated by datagen.flow() + model.fit_generator(datagen.flow(X_train, Y_train, + batch_size=batch_size), + samples_per_epoch=X_train.shape[0], + nb_epoch=nb_epoch, + validation_data=(X_test, Y_test)) + + +#%% + +score = model.evaluate(X_test, Y_test, show_accuracy=True, verbose=0) +print('Test score:', score[0]) +print('Test accuracy:', score[1]) +print(model.predict_classes(X_test[1:5])) +print(Y_test[1:5]) + + +# In[ ]: + + + diff --git a/data/legend.csv b/data/legend.csv index d8f132f88..5f9f9a823 100644 --- a/data/legend.csv +++ b/data/legend.csv @@ -6174,3 +6174,525 @@ JN721,Lindsey_Graham_0002.jpg,neutral JN721,LK_Advani_0001.jpg,surprise JN721,LK_Advani_0002.jpg,neutral JN721,LK_Advani_0003.jpg,surprise +457,Cropped_94_images.jpg,surprise +457,Cropped_61_George_W_Bush_0517.jpg,surprise +457,Cropped_54_UY.SU3.145.tiff,surprise +457,Cropped_54_KA.SU2.37.tiff,surprise +457,Cropped_53_TM.SU2.188.tiff,surprise +457,Cropped_53_TM.SU1.187.tiff,surprise +457,Cropped_53_NA.SU3.210.tiff,surprise +457,Cropped_53_KA.SU3.38.tiff,surprise +457,Cropped_52_TM.SU3.189.tiff,surprise +457,Cropped_52_KR.SU2.81.tiff,surprise +457,Cropped_52_KR.SU1.80.tiff,surprise +457,Cropped_52_KL.SU3.166.tiff,surprise +457,Cropped_52_KL.SU2.165.tiff,surprise +457,Cropped_51_NA.SU2.209.tiff,surprise +457,Cropped_51_MK.SU2.123.tiff,surprise 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false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using Theano backend.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "257\n", + "(257L, 1024L)\n", + "(257L,)\n", + "('X_train shape:', (205L, 1L, 32L, 32L))\n", + "(205L, 'train samples')\n", + "(52L, 'test samples')\n", + "('label : ', array([ 0., 0., 1., 0., 0., 0., 0., 0., 0., 0.]))\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\dundeva\\Anaconda2\\lib\\site-packages\\keras\\backend\\theano_backend.py:1546: UserWarning: DEPRECATION: the 'ds' parameter is not going to exist anymore as it is going to be replaced by the parameter 'ws'.\n", + " mode='max')\n", + "C:\\Users\\dundeva\\Anaconda2\\lib\\site-packages\\keras\\backend\\theano_backend.py:1546: UserWarning: DEPRECATION: the 'st' parameter is not going to exist anymore as it is going to be replaced by the parameter 'stride'.\n", + " mode='max')\n", + "C:\\Users\\dundeva\\Anaconda2\\lib\\site-packages\\keras\\backend\\theano_backend.py:1546: UserWarning: DEPRECATION: the 'padding' parameter is not going to exist anymore as it is going to be replaced by the parameter 'pad'.\n", + " mode='max')\n", + "C:\\Users\\dundeva\\Anaconda2\\lib\\site-packages\\keras\\models.py:623: UserWarning: The \"show_accuracy\" argument is deprecated, instead you should pass the \"accuracy\" metric to the model at compile time:\n", + "`model.compile(optimizer, loss, metrics=[\"accuracy\"])`\n", + " warnings.warn('The \"show_accuracy\" argument is deprecated, '\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train on 205 samples, validate on 52 samples\n", + "Epoch 1/200\n", + "205/205 [==============================] - 4s - loss: 1.5061 - acc: 0.3268 - val_loss: 1.6048 - val_acc: 0.3654" + ] + } + ], + "source": [ + "from keras import backend as K\n", + "K.set_image_dim_ordering('th')\n", + "from keras.models import Sequential\n", + "from keras.layers.core import Dense, Dropout, Activation, Flatten\n", + "from keras.layers import Convolution2D, MaxPooling2D, Flatten\n", + "from keras.layers.convolutional import Convolution2D, MaxPooling2D\n", + "from keras.optimizers import SGD,RMSprop,adam\n", + "from keras.utils import np_utils\n", + "from keras.preprocessing.image import ImageDataGenerator\n", + "from matplotlib import pyplot\n", + "from sklearn.utils import shuffle\n", + "from sklearn.cross_validation import train_test_split\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib\n", + "import os\n", + "import theano\n", + "from PIL import Image\n", + "from numpy import *\n", + "\n", + "\n", + "\n", + "# input image dimensions\n", + "img_rows, img_cols = 32,32\n", + "\n", + "# number of channels\n", + "img_channels =1\n", + "\n", + "\n", + "# data\n", + "\n", + "path1 = 'C:/Users/dundeva/Documents/CroppedImages' #path of folder of images \n", + "path2 = 'C:/Users/dundeva/Documents/AugmentedImages' #path of folder to save images \n", + "\n", + "listing = os.listdir(path1) \n", + "num_samples=size(listing)\n", + "print num_samples\n", + "\n", + "\n", + "for file in listing:\n", + " im = Image.open(path1 + '//' + file) \n", + " img = im.resize((img_rows,img_cols))\n", + " gray = img.convert('L')\n", + " #need to do some more processing here \n", + " gray.save(path2 +'//' + file, \"JPEG\")\n", + "\n", + "imlist = os.listdir(path1)\n", + "\n", + "im1 = array(Image.open('C:/Users/dundeva/Documents/AugmentedImages' + '//'+ imlist[0])) # open one image to get size\n", + "m,n = im1.shape[0:2] # get the size of the images\n", + "imnbr = len(imlist) # get the number of images\n", + "\n", + "# create matrix to store all flattened images\n", + "immatrix = array([array(Image.open('C:/Users/dundeva/Documents/AugmentedImages'+ '//' + im2)).flatten()\n", + " for im2 in imlist],'f')\n", + " \n", + "label=np.ones((num_samples,),dtype = int)\n", + "label[0:89]=0\n", + "label[89:187]=1\n", + "label[187:]=2\n", + "\n", + "\n", + "data,Label = shuffle(immatrix,label, random_state=2)\n", + "train_data = [data,Label]\n", + "\n", + "img=immatrix[167].reshape(img_rows,img_cols)\n", + "plt.imshow(img)\n", + "plt.imshow(img,cmap='gray')\n", + "print (train_data[0].shape)\n", + "print (train_data[1].shape)\n", + "\n", + "\n", + "\n", + "#batch_size to train\n", + "batch_size = 32\n", + "# number of output classes\n", + "nb_classes =10\n", + "# number of epochs to train\n", + "nb_epoch =200\n", + "data_augmentation = True\n", + "\n", + "# number of convolutional filters to use\n", + "nb_filters = 32\n", + "# size of pooling area for max pooling\n", + "nb_pool = 3\n", + "# convolution kernel size\n", + "nb_conv = 3\n", + "\n", + "(X, y) = (train_data[0],train_data[1])\n", + "\n", + "\n", + "# split X and y into training and testing sets\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=4)\n", + "\n", + "\n", + "X_train = X_train.reshape(X_train.shape[0], 1, img_rows, img_cols)\n", + "X_test = X_test.reshape(X_test.shape[0], 1, img_rows, img_cols)\n", + "\n", + "X_train = X_train.astype('float32')\n", + "X_test = X_test.astype('float32')\n", + "\n", + "X_train /= 255\n", + "X_test /= 255\n", + "\n", + "print('X_train shape:', X_train.shape)\n", + "print(X_train.shape[0], 'train samples')\n", + "print(X_test.shape[0], 'test samples')\n", + "\n", + "# convert class vectors to binary class matrices\n", + "Y_train = np_utils.to_categorical(y_train, nb_classes)\n", + "Y_test = np_utils.to_categorical(y_test, nb_classes)\n", + "\n", + "i = 100\n", + "plt.imshow(X_train[i, 0], interpolation='nearest')\n", + "print(\"label : \", Y_train[i,:])\n", + "\n", + "\n", + "model = Sequential()\n", + "model.add(Convolution2D(32, 4, 4, border_mode='same',\n", + " input_shape=X_train.shape[1:]))\n", + "model.add(Activation('relu'))\n", + "model.add(Convolution2D(32, 4, 4))\n", + "model.add(Activation('relu'))\n", + "model.add(MaxPooling2D(pool_size=(2, 2)))\n", + "model.add(Dropout(0.5))\n", + "\n", + "model.add(Convolution2D(64, 3, 3, border_mode='same'))\n", + "model.add(Activation('relu'))\n", + "model.add(Convolution2D(64, 3, 3))\n", + "model.add(Activation('relu'))\n", + "model.add(MaxPooling2D(pool_size=(2, 2)))\n", + "model.add(Dropout(0.5))\n", + "\n", + "model.add(Flatten())\n", + "model.add(Dense(512))\n", + "model.add(Activation('relu'))\n", + "model.add(Dropout(0.5))\n", + "model.add(Dense(nb_classes))\n", + "model.add(Activation('softmax'))\n", + "\n", + "\n", + "#sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)\n", + "model.compile(loss='categorical_crossentropy',\n", + " optimizer='adadelta',\n", + " metrics=['accuracy'])\n", + "\n", + "\n", + "hist = model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch,\n", + " show_accuracy=True, verbose=1, validation_data=(X_test, Y_test))\n", + " \n", + " \n", + "hist = model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch,\n", + " show_accuracy=True, verbose=1, validation_split=0.2)\n", + "\n", + "\n", + "# visualizing losses and accuracy\n", + "\n", + "train_loss=hist.history['loss']\n", + "val_loss=hist.history['val_loss']\n", + "train_acc=hist.history['acc']\n", + "val_acc=hist.history['val_acc']\n", + "xc=range(nb_epoch)\n", + "\n", + "plt.figure(1,figsize=(7,5))\n", + "plt.plot(xc,train_loss)\n", + "plt.plot(xc,val_loss)\n", + "plt.xlabel('num of Epochs')\n", + "plt.ylabel('loss')\n", + "plt.title('train_loss vs val_loss')\n", + "plt.grid(True)\n", + "plt.legend(['train','val'])\n", + "print plt.style.available # use bmh, classic,ggplot for big pictures\n", + "plt.style.use(['classic'])\n", + "\n", + "plt.figure(2,figsize=(7,5))\n", + "plt.plot(xc,train_acc)\n", + "plt.plot(xc,val_acc)\n", + "plt.xlabel('num of Epochs')\n", + "plt.ylabel('accuracy')\n", + "plt.title('train_acc vs val_acc')\n", + "plt.grid(True)\n", + "plt.legend(['train','val'],loc=4)\n", + "#print plt.style.available # use bmh, classic,ggplot for big pictures\n", + "plt.style.use(['classic'])\n", + "\n", + "X_train = X_train.astype('float32')\n", + "X_test = X_test.astype('float32')\n", + "X_train /= 255\n", + "X_test /= 255\n", + "\n", + "if not data_augmentation:\n", + " print('Not using data augmentation.')\n", + " model.fit(X_train, Y_train,\n", + " batch_size=batch_size,\n", + " nb_epoch=nb_epoch,\n", + " validation_data=(X_test, Y_test),\n", + " shuffle=True)\n", + "else:\n", + " print('Using real-time data augmentation.')\n", + "\n", + " # this will do preprocessing and realtime data augmentation\n", + " datagen = ImageDataGenerator(\n", + " featurewise_center=False, # set input mean to 0 over the dataset\n", + " samplewise_center=False, # set each sample mean to 0\n", + " featurewise_std_normalization=False, # divide inputs by std of the dataset\n", + " samplewise_std_normalization=False, # divide each input by its std\n", + " zca_whitening=False, # apply ZCA whitening\n", + " rotation_range=0, # randomly rotate images in the range (degrees, 0 to 180)\n", + " width_shift_range=0.1, # randomly shift images horizontally (fraction of total width)\n", + " height_shift_range=0.1, # randomly shift images vertically (fraction of total height)\n", + " horizontal_flip=True, # randomly flip images\n", + " vertical_flip=False) # randomly flip images\n", + "\n", + " # compute quantities required for featurewise normalization\n", + " # (std, mean, and principal components if ZCA whitening is applied)\n", + " datagen.fit(X_train)\n", + "\n", + " # fit the model on the batches generated by datagen.flow()\n", + " model.fit_generator(datagen.flow(X_train, Y_train,\n", + " batch_size=batch_size),\n", + " samples_per_epoch=X_train.shape[0],\n", + " nb_epoch=nb_epoch,\n", + " validation_data=(X_test, Y_test))\n", + "\n", + "\n", + "#%% \n", + "\n", + "score = model.evaluate(X_test, Y_test, show_accuracy=True, verbose=0)\n", + "print('Test score:', score[0])\n", + "print('Test accuracy:', score[1])\n", + "print(model.predict_classes(X_test[1:5]))\n", + "print(Y_test[1:5])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/images/cropped_104_Arnold_Schwarzenegger_0003.jpg b/images/cropped_104_Arnold_Schwarzenegger_0003.jpg new file mode 100644 index 000000000..ee6e580dc Binary files /dev/null and b/images/cropped_104_Arnold_Schwarzenegger_0003.jpg differ diff 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