Initial commit message

DL training VGG16.py

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+from keras import applications,Model
+from keras.preprocessing.image import ImageDataGenerator
+from keras import optimizers
+from keras.models import Sequential
+from keras.layers import Dropout, Flatten, Dense
+
+# path to the model weights files.
+weights_path = 'vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5'
+# dimensions of our images.
+img_width, img_height = 150, 150
+
+train_data_dir = 'train'
+validation_data_dir = 'validation'
+nb_train_samples = 97
+nb_validation_samples = 23
+epochs = 50
+batch_size = 2
+
+# build the VGG16 network
+model = applications.VGG16(weights=weights_path, include_top=False, input_shape=(150,150,3))
+print('Model loaded.')
+
+# build a classifier model to put on top of the convolutional model
+top_model = Sequential()
+top_model.add(Flatten(input_shape=model.output_shape[1:]))
+top_model.add(Dense(256, activation='relu'))
+top_model.add(Dropout(0.5))
+top_model.add(Dense(1, activation='sigmoid'))
+
+# note that it is necessary to start with a fully-trained
+# classifier, including the top classifier,
+# in order to successfully do fine-tuning
+
+# add the model on top of the convolutional base
+#model.add(top_model)
+x=model.output
+x=Flatten(input_shape=model.output_shape[1:])(x)
+x=Dropout(0.5)(x)
+x=Dense(1, activation='sigmoid')(x)
+model = Model(model.input, x)
+# set the first 25 layers (up to the last conv block)
+# to non-trainable (weights will not be updated)
+for layer in model.layers[:25]:
+    layer.trainable = False
+
+# compile the model with a SGD/momentum optimizer
+# and a very slow learning rate.
+model.compile(loss='binary_crossentropy',
+              optimizer=optimizers.SGD(lr=1e-4, momentum=0.9),
+              metrics=['accuracy'])
+
+# prepare data augmentation configuration
+train_datagen = ImageDataGenerator(
+    rescale=1. / 255,
+    shear_range=0.2,
+    zoom_range=0.2,
+    horizontal_flip=True)
+
+test_datagen = ImageDataGenerator(rescale=1. / 255)
+
+train_generator = train_datagen.flow_from_directory(
+    train_data_dir,
+    target_size=(img_height, img_width),
+    batch_size=batch_size,
+    class_mode='binary')
+
+validation_generator = test_datagen.flow_from_directory(
+    validation_data_dir,
+    target_size=(img_height, img_width),
+    batch_size=batch_size,
+    class_mode='binary')
+
+# fine-tune the model
+model.fit_generator(
+    train_generator,
+    samples_per_epoch=nb_train_samples,
+    epochs=epochs,
+    validation_data=validation_generator,
+    nb_val_samples=nb_validation_samples)

DL training.py

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+from keras.models import Sequential
+from keras.layers import Conv2D, MaxPooling2D
+from keras.layers import Activation, Dropout, Flatten, Dense
+from keras.preprocessing.image import ImageDataGenerator
+model = Sequential()
+model.add(Conv2D(32, (3, 3), input_shape=(150, 150, 3)))
+model.add(Activation('relu'))
+model.add(MaxPooling2D(pool_size=(2, 2), dim_ordering="th"))
+
+model.add(Conv2D(32, (3, 3), dim_ordering="th"))
+model.add(Activation('relu'))
+model.add(MaxPooling2D(pool_size=(2, 2), dim_ordering="th"))
+
+model.add(Conv2D(64, (3, 3), dim_ordering="th"))
+model.add(Activation('relu'))
+model.add(MaxPooling2D(pool_size=(2, 2), dim_ordering="th"))
+
+model.add(Flatten())  # this converts our 3D feature maps to 1D feature vectors
+model.add(Dense(64))
+model.add(Activation('relu'))
+model.add(Dropout(0.5))
+model.add(Dense(1))
+model.add(Activation('sigmoid'))
+
+model.compile(loss='binary_crossentropy',
+              optimizer='rmsprop',
+              metrics=['accuracy'])
+
+batch_size = 16
+
+# this is the augmentation configuration we will use for training
+train_datagen = ImageDataGenerator(
+        rescale=1./255,
+        shear_range=0.2,
+        zoom_range=0.2,
+        horizontal_flip=True)
+
+# this is the augmentation configuration we will use for testing:
+# only rescaling
+test_datagen = ImageDataGenerator(rescale=1./255)
+
+# this is a generator that will read pictures found in
+# subfolers of 'data/train', and indefinitely generate
+# batches of augmented image data
+train_generator = train_datagen.flow_from_directory(
+        'train',  # this is the target directory
+        target_size=(150, 150),  # all images will be resized to 150x150
+        batch_size=batch_size,
+        class_mode='binary')  # since we use binary_crossentropy loss, we need binary labels
+labels = (train_generator.class_indices)
+labels = dict((v,k) for k,v in labels.items())
+# this is a similar generator, for validation data
+validation_generator = test_datagen.flow_from_directory(
+        'validation',
+        target_size=(150, 150),
+        batch_size=batch_size,
+        class_mode='binary')
+
+model.fit_generator(
+        train_generator,
+        steps_per_epoch=2000 // batch_size,
+        epochs=50,
+        validation_data=validation_generator,
+        validation_steps=800 // batch_size)
+
+model.save_weights('first_try.h5')  # always save your weights after training or during training