DL之VGG16：基于VGG16(Keras)利用Knifey-Spoony数据集对网络架构迁移学习
目录
数据集
输出结果
设计思路
1、基模型
2、思路导图
核心代码
更多输出
数据集
Dataset之Knifey-Spoony：Knifey-Spoony数据集的简介、下载、使用方法之详细攻略
输出结果
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]))
更多输出
输出tensorflow的版本： 1.10.0
Data has apparently already been downloaded and unpacked.
maybe_download_and_extract()函数执行结束！
load()函数的data_dir： data/knifey-spoony/
Creating dataset from the files in: data/knifey-spoony/
- Data loaded from cache-file: data/knifey-spoony/knifey-spoony.pkl
执行load()函数结束！
get_paths()函数的self.in_dir输出： data/knifey-spoony
- Copied training-set to: data/knifey-spoony/train/
get_paths()函数的self.in_dir输出： data/knifey-spoony
- Copied test-set to: data/knifey-spoony/test/
data/knifey-spoony/train/ data/knifey-spoony/test/
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2019-08-14 11:44:51.782638: I T:\src\github\tensorflow\tensorflow\core\platform\cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
2019-08-14 11:44:53.212742: W T:\src\github\tensorflow\tensorflow\core\framework\allocator.cc:108] Allocation of 411041792 exceeds 10% of system memory.
2019-08-14 11:44:54.302588: W T:\src\github\tensorflow\tensorflow\core\framework\allocator.cc:108] Allocation of 411041792 exceeds 10% of system memory.
2019-08-14 11:44:54.310978: W T:\src\github\tensorflow\tensorflow\core\framework\allocator.cc:108] Allocation of 411041792 exceeds 10% of system memory.
(224, 224)
Found 4170 images belonging to 5 classes.
Found 530 images belonging to 5 classes.
26.5
['forky', 'knifey', 'spoony', 'test', 'train']
5
class_weight： [1.39839034 1.14876033 0.70701933]
['forky', 'knifey', 'spoony', 'test', 'train']
Downloading data from https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json
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16384/35363 [============>.................] - ETA: 0s
40960/35363 [==================================] - 0s 6us/step
79.02% : macaw
6.61% : bubble
3.64% : vine_snake
1.90% : pinwheel
1.22% : knot
50.31% : shower_curtain
17.08% : handkerchief
12.75% : mosquito_net
2.87% : window_shade
1.32% : toilet_tissue
45.08% : shower_curtain
21.84% : mosquito_net
11.55% : handkerchief
2.02% : window_shade
0.91% : Windsor_tie
26.75% : spoonbill
7.06% : black_stork
7.04% : wooden_spoon
4.21% : limpkin
3.72% : paddle
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) (None, 224, 224, 3) 0
_________________________________________________________________
block1_conv1 (Conv2D) (None, 224, 224, 64) 1792
_________________________________________________________________
block1_conv2 (Conv2D) (None, 224, 224, 64) 36928
_________________________________________________________________
block1_pool (MaxPooling2D) (None, 112, 112, 64) 0
_________________________________________________________________
block2_conv1 (Conv2D) (None, 112, 112, 128) 73856
_________________________________________________________________
block2_conv2 (Conv2D) (None, 112, 112, 128) 147584
_________________________________________________________________
block2_pool (MaxPooling2D) (None, 56, 56, 128) 0
_________________________________________________________________
block3_conv1 (Conv2D) (None, 56, 56, 256) 295168
_________________________________________________________________
block3_conv2 (Conv2D) (None, 56, 56, 256) 590080
_________________________________________________________________
block3_conv3 (Conv2D) (None, 56, 56, 256) 590080
_________________________________________________________________
block3_pool (MaxPooling2D) (None, 28, 28, 256) 0
_________________________________________________________________
block4_conv1 (Conv2D) (None, 28, 28, 512) 1180160
_________________________________________________________________
block4_conv2 (Conv2D) (None, 28, 28, 512) 2359808
_________________________________________________________________
block4_conv3 (Conv2D) (None, 28, 28, 512) 2359808
_________________________________________________________________
block4_pool (MaxPooling2D) (None, 14, 14, 512) 0
_________________________________________________________________
block5_conv1 (Conv2D) (None, 14, 14, 512) 2359808
_________________________________________________________________
block5_conv2 (Conv2D) (None, 14, 14, 512) 2359808
_________________________________________________________________
block5_conv3 (Conv2D) (None, 14, 14, 512) 2359808
_________________________________________________________________
block5_pool (MaxPooling2D) (None, 7, 7, 512) 0
_________________________________________________________________
flatten (Flatten) (None, 25088) 0
_________________________________________________________________
fc1 (Dense) (None, 4096) 102764544
_________________________________________________________________
fc2 (Dense) (None, 4096) 16781312
_________________________________________________________________
predictions (Dense) (None, 1000) 4097000
=================================================================
Total params: 138,357,544
Trainable params: 138,357,544
Non-trainable params: 0
_________________________________________________________________
Tensor("block5_pool/MaxPool:0", shape=(?, 7, 7, 512), dtype=float32)
True: input_1
True: block1_conv1
True: block1_conv2
True: block1_pool
True: block2_conv1
True: block2_conv2
True: block2_pool
True: block3_conv1
True: block3_conv2
True: block3_conv3
True: 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_pool
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
False: block4_conv1
False: block4_conv2
False: block4_conv3
False: block4_pool
False: block5_conv1
False: block5_conv2
False: block5_conv3
False: block5_pool
--------------
Epoch 1/20
1/100 [..............................] - ETA: 24:24 - loss: 2.0064 - categorical_accuracy: 0.2500
……
100/100 [==============================] - 4064s 41s/step - loss: 1.1529 - categorical_accuracy: 0.4490 - val_loss: 0.8731 - val_categorical_accuracy: 0.6189
……
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100/100 [==============================] - 2850s 29s/step - loss: 0.9524 - categorical_accuracy: 0.5480 - val_loss: 0.8089 - val_categorical_accuracy: 0.6377
Epoch 3/20
1/100 [..............................] - ETA: 22:19 - loss: 0.6235 - categorical_accuracy: 0.8000
……
99/100 [============================>.] - ETA: 18s - loss: 0.8497 - categorical_accuracy: 0.6056
100/100 [==============================] - 2404s 24s/step - loss: 0.8499 - categorical_accuracy: 0.6060 - val_loss: 0.7322 - val_categorical_accuracy: 0.7283
……
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99/100 [============================>.] - ETA: 11s - loss: 0.6253 - categorical_accuracy: 0.7389
100/100 [==============================] - 1519s 15s/step - loss: 0.6248 - categorical_accuracy: 0.7390 - val_loss: 0.5702 - val_categorical_accuracy: 0.7811
Epoch 10/20
1/100 [..............................] - ETA: 21:14 - loss: 0.4481 - categorical_accuracy: 0.8000
……
99/100 [============================>.] - ETA: 12s - loss: 0.6033 - categorical_accuracy: 0.7490
100/100 [==============================] - 1570s 16s/step - loss: 0.6045 - categorical_accuracy: 0.7475 - val_loss: 0.5199 - val_categorical_accuracy: 0.8075
Epoch 11/20
1/100 [..............................] - ETA: 19:40 - loss: 0.5531 - categorical_accuracy: 0.7500
……
99/100 [============================>.] - ETA: 12s - loss: 0.5403 - categorical_accuracy: 0.7813
100/100 [==============================] - 1559s 16s/step - loss: 0.5401 - categorical_accuracy: 0.7810 - val_loss: 0.5147 - val_categorical_accuracy: 0.8132
Epoch 15/20
1/100 [..............................] - ETA: 20:10 - loss: 0.5337 - categorical_accuracy: 0.7000
2/100 [..............................] - ETA: 19:46 - loss: 0.4598 - categorical_accuracy: 0.8250
……
99/100 [============================>.] - ETA: 12s - loss: 0.5495 - categorical_accuracy: 0.7601
100/100 [==============================] - 1578s 16s/step - loss: 0.5482 - categorical_accuracy: 0.7610 - val_loss: 0.5832 - val_categorical_accuracy: 0.7491
Epoch 16/20
1/100 [..............................] - ETA: 20:07 - loss: 0.2315 - categorical_accuracy: 1.0000
……
19/100 [====>.........................] - ETA: 16:29 - loss: 0.5293 - categorical_accuracy: 0.7816
