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January 24, 2017 08:31
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## Multi scale CNN in Keras Python | |
## https://i.stack.imgur.com/2H4xD.png | |
#main CNN model - CNN1 | |
main_model = Sequential() | |
main_model.add(Convolution2D(32, 3, 3, input_shape=(3, 224, 224))) | |
main_model.add(Activation('relu')) | |
main_model.add(MaxPooling2D(pool_size=(2, 2))) | |
main_model.add(Convolution2D(32, 3, 3)) | |
main_model.add(Activation('relu')) | |
main_model.add(MaxPooling2D(pool_size=(2, 2))) | |
main_model.add(Convolution2D(64, 3, 3)) | |
main_model.add(Activation('relu')) | |
main_model.add(MaxPooling2D(pool_size=(2, 2))) # the main_model so far outputs 3D feature maps (height, width, features) | |
main_model.add(Flatten()) | |
#lower features model - CNN2 | |
lower_model1 = Sequential() | |
lower_model1.add(Convolution2D(32, 3, 3, input_shape=(3, 224, 224))) | |
lower_model1.add(Activation('relu')) | |
lower_model1.add(MaxPooling2D(pool_size=(2, 2))) | |
lower_model1.add(Flatten()) | |
#lower features model - CNN3 | |
lower_model2 = Sequential() | |
lower_model2.add(Convolution2D(32, 3, 3, input_shape=(3, 224, 224))) | |
lower_model2.add(Activation('relu')) | |
lower_model2.add(MaxPooling2D(pool_size=(2, 2))) | |
lower_model2.add(Flatten()) | |
#merged model | |
merged_model = Merge([main_model, lower_model1, lower_model2], mode='concat') | |
final_model = Sequential() | |
final_model.add(merged_model) | |
final_model.add(Dense(64)) | |
final_model.add(Activation('relu')) | |
final_model.add(Dropout(0.5)) | |
final_model.add(Dense(1)) | |
final_model.add(Activation('sigmoid')) | |
final_model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy']) | |
print 'About to start training merged CNN' | |
train_datagen = ImageDataGenerator(rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True) | |
train_generator = train_datagen.flow_from_directory(train_data_dir, target_size=(224, 224), batch_size=32, class_mode='binary') | |
test_datagen = ImageDataGenerator(rescale=1./255) | |
test_generator = test_datagen.flow_from_directory(args.test_images, target_size=(224, 224), batch_size=32, class_mode='binary') | |
final_train_generator = zip(train_generator, train_generator, train_generator) | |
final_test_generator = zip(test_generator, test_generator, test_generator) | |
final_model.fit_generator(final_train_generator, samples_per_epoch=nb_train_samples, nb_epoch=nb_epoch, validation_data=final_test_generator, nb_val_samples=nb_validation_samples) |
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