设计思路
1、基模型
2、思路导图
核心代码
model_VGG16.summary()
transfer_layer = model_VGG16.get_layer('block5_pool')
print('transfer_layer.output：', transfer_layer.output)
conv_model = Model(inputs=model_VGG16.input,
outputs=transfer_layer.output)
VGG16_TL_model = Sequential() # Start a new Keras Sequential model.
VGG16_TL_model.add(conv_model) # Add the convolutional part of the VGG16 model from above.
VGG16_TL_model.add(Flatten()) # Flatten the output of the VGG16 model because it is from a convolutional layer.
VGG16_TL_model.add(Dense(1024, activation='relu')) # Add a dense (aka. fully-connected) layer. This is for combining features that the VGG16 model has recognized in the image.
VGG16_TL_model.add(Dropout(0.5)) # Add a dropout-layer which may prevent overfitting and improve generalization ability to unseen data e.g. the test-set.
VGG16_TL_model.add(Dense(num_classes, activation='softmax')) # Add the final layer for the actual classification.
print_layer_trainable()
conv_model.trainable = False
for layer in conv_model.layers:
layer.trainable = False
print_layer_trainable()
loss = 'categorical_crossentropy'
metrics = ['categorical_accuracy']
VGG16_TL_model.compile(optimizer=optimizer, loss=loss, metrics=metrics)
epochs = 20
steps_per_epoch = 100
history = VGG16_TL_model.fit_generator(generator=generator_train,
epochs=epochs,
steps_per_epoch=steps_per_epoch,
class_weight=class_weight,
validation_data=generator_test,
validation_steps=steps_test)
plot_training_history(history)
VGG16_TL_model_result = VGG16_TL_model.evaluate_generator(generator_test, steps=steps_test)
print("Test-set classification accuracy: {0:.2%}".format(VGG16_TL_model_result[1]))
