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- model = Sequential()
- model.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1],
- border_mode='valid',
- input_shape=input_shape))
- model.add(Activation('relu'))
- model.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1]))
- model.add(Activation('relu'))
- model.add(MaxPooling2D(pool_size=pool_size))
- # (16, 16, 32)
- model.add(Convolution2D(nb_filters*2, kernel_size[0], kernel_size[1]))
- model.add(Activation('relu'))
- model.add(Convolution2D(nb_filters*2, kernel_size[0], kernel_size[1]))
- model.add(Activation('relu'))
- model.add(MaxPooling2D(pool_size=pool_size))
- # (8, 8, 64) = (2048)
- model.add(Flatten())
- model.add(Dense(1024))
- model.add(Activation('relu'))
- model.add(Dropout(0.5))
- model.add(Dense(2)) # define a binary classification problem
- model.add(Activation('softmax'))
- model.compile(loss='categorical_crossentropy',
- optimizer='adadelta',
- metrics=['accuracy'])
- model.fit(x_train, y_train,
- batch_size=batch_size,
- nb_epoch=nb_epoch,
- verbose=1,
- validation_data=(x_test, y_test))
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