DL之VGG16：基于VGG16(Keras)利用Knifey-Spoony數據集對網絡架構FineTuning

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輸出結果

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False: input_1 False: block1_conv1 False: block1_conv2 False: block1_pool False: block2_conv1 False: block2_conv2 False: block2_pool False: block3_conv1 False: block3_conv2 False: block3_conv3 False: block3_pool True: block4_conv1 True: block4_conv2 True: block4_conv3 True: block4_pool True: block5_conv1 True: block5_conv2 True: block5_conv3 True: block5_poolEpoch 1/20 100/100 [==============================] - 814s 8s/step - loss: 0.4626 - categorical_accuracy: 0.8095 - val_loss: 0.5332 - val_categorical_accuracy: 0.7717 Epoch 2/20 100/100 [==============================] - 823s 8s/step - loss: 0.4662 - categorical_accuracy: 0.8150 - val_loss: 0.5236 - val_categorical_accuracy: 0.7755 Epoch 3/20 100/100 [==============================] - 824s 8s/step - loss: 0.4506 - categorical_accuracy: 0.8140 - val_loss: 0.5153 - val_categorical_accuracy: 0.7830 Epoch 4/20 100/100 [==============================] - 821s 8s/step - loss: 0.4491 - categorical_accuracy: 0.8170 - val_loss: 0.5236 - val_categorical_accuracy: 0.7717 Epoch 5/20 100/100 [==============================] - 820s 8s/step - loss: 0.4612 - categorical_accuracy: 0.8150 - val_loss: 0.5244 - val_categorical_accuracy: 0.7698 Epoch 6/20 100/100 [==============================] - 824s 8s/step - loss: 0.4440 - categorical_accuracy: 0.8215 - val_loss: 0.5078 - val_categorical_accuracy: 0.7849 Epoch 7/20 100/100 [==============================] - 824s 8s/step - loss: 0.4339 - categorical_accuracy: 0.8200 - val_loss: 0.5070 - val_categorical_accuracy: 0.7906 Epoch 8/20 100/100 [==============================] - 820s 8s/step - loss: 0.4188 - categorical_accuracy: 0.8335 - val_loss: 0.5068 - val_categorical_accuracy: 0.7887 Epoch 9/20 100/100 [==============================] - 823s 8s/step - loss: 0.4307 - categorical_accuracy: 0.8345 - val_loss: 0.5192 - val_categorical_accuracy: 0.7792 Epoch 10/20 100/100 [==============================] - 820s 8s/step - loss: 0.4432 - categorical_accuracy: 0.8180 - val_loss: 0.4945 - val_categorical_accuracy: 0.7887 Epoch 11/20 100/100 [==============================] - 824s 8s/step - loss: 0.4171 - categorical_accuracy: 0.8295 - val_loss: 0.5012 - val_categorical_accuracy: 0.7887 Epoch 12/20 100/100 [==============================] - 820s 8s/step - loss: 0.4071 - categorical_accuracy: 0.8335 - val_loss: 0.5064 - val_categorical_accuracy: 0.7830 Epoch 13/20 100/100 [==============================] - 824s 8s/step - loss: 0.4164 - categorical_accuracy: 0.8200 - val_loss: 0.5065 - val_categorical_accuracy: 0.7811 Epoch 14/20 100/100 [==============================] - 825s 8s/step - loss: 0.4060 - categorical_accuracy: 0.8350 - val_loss: 0.5021 - val_categorical_accuracy: 0.7830 Epoch 15/20 100/100 [==============================] - 821s 8s/step - loss: 0.3948 - categorical_accuracy: 0.8390 - val_loss: 0.4985 - val_categorical_accuracy: 0.7925 Epoch 16/20 100/100 [==============================] - 824s 8s/step - loss: 0.3724 - categorical_accuracy: 0.8570 - val_loss: 0.4909 - val_categorical_accuracy: 0.7981 Epoch 17/20 100/100 [==============================] - 821s 8s/step - loss: 0.4084 - categorical_accuracy: 0.8305 - val_loss: 0.4888 - val_categorical_accuracy: 0.8000 Epoch 18/20 100/100 [==============================] - 824s 8s/step - loss: 0.3975 - categorical_accuracy: 0.8400 - val_loss: 0.4907 - val_categorical_accuracy: 0.8019 Epoch 19/20 100/100 [==============================] - 822s 8s/step - loss: 0.4093 - categorical_accuracy: 0.8430 - val_loss: 0.5156 - val_categorical_accuracy: 0.7792 Epoch 20/20 100/100 [==============================] - 824s 8s/step - loss: 0.4007 - categorical_accuracy: 0.8270 - val_loss: 0.4917 - val_categorical_accuracy: 0.7962

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設計思路

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核心代碼

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conv_model.trainable = True for layer in conv_model.layers:# Boolean whether this layer is trainable.trainable = ('block5' in layer.name or 'block4' in layer.name)# Set the layer's bool.layer.trainable = trainable print_layer_trainable() optimizer_fine = Adam(lr=1e-7)FT_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)print(FT_history) plot_training_history(FT_history) #VGG16_FT_model_result = VGG16_TL_model.evaluate_generator(generator_test, steps=steps_test) print("Test-set classification accuracy: {0:.2%}".format(VGG16_FT_model_result[1])) example_errors()

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總結

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