DL之VGG16：基于VGG16(Keras)利用Knifey-Spoony數(shù)據(jù)集對(duì)網(wǎng)絡(luò)架構(gòu)遷移學(xué)習(xí)

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目錄

數(shù)據(jù)集

輸出結(jié)果

設(shè)計(jì)思路

1、基模型

2、思路導(dǎo)圖

核心代碼

更多輸出

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數(shù)據(jù)集

Dataset之Knifey-Spoony：Knifey-Spoony數(shù)據(jù)集的簡(jiǎn)介、下載、使用方法之詳細(xì)攻略

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輸出結(jié)果

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設(shè)計(jì)思路

1、基模型

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2、思路導(dǎo)圖

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核心代碼

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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 = Falseprint_layer_trainable() loss = 'categorical_crossentropy' metrics = ['categorical_accuracy'] VGG16_TL_model.compile(optimizer=optimizer, loss=loss, metrics=metrics) epochs = 20 steps_per_epoch = 100history = 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]))

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更多輸出

輸出tensorflow的版本： 1.10.0 Data has apparently already been downloaded and unpacked. maybe_download_and_extract()函數(shù)執(zhí)行結(jié)束！ load()函數(shù)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 執(zhí)行l(wèi)oad()函數(shù)結(jié)束！ get_paths()函數(shù)的self.in_dir輸出： data/knifey-spoony - Copied training-set to: data/knifey-spoony/train/ get_paths()函數(shù)的self.in_dir輸出： data/knifey-spoony - Copied test-set to: data/knifey-spoony/test/ data/knifey-spoony/train/ data/knifey-spoony/test/ ……383418368/553467096 [===================>..........] - ETA: 2:09 383614976/553467096 [===================>..........] - ETA: 2:09 383811584/553467096 [===================>..........] - ETA: 2:09 383860736/553467096 [===================>..........] - ETA: 2:09 383942656/553467096 [===================>..........] - ETA: 2:09……394297344/553467096 [====================>.........] - ETA: 2:00 394510336/553467096 [====================>.........] - ETA: 1:59 394723328/553467096 [====================>.........] - ETA: 1:59 394821632/553467096 [====================>.........] - ETA: 1:59 395018240/553467096 [====================>.........] - ETA: 1:59 395214848/553467096 [====================>.........] - ETA: 1:59 395395072/553467096 [====================>.........] - ETA: 1:59 395542528/553467096 [====================>.........] - ETA: 1:58……469909504/553467096 [========================>.....] - ETA: 1:00 470040576/553467096 [========================>.....] - ETA: 1:00 470122496/553467096 [========================>.....] - ETA: 59s 470351872/553467096 [========================>.....] - ETA: 59s 470499328/553467096 [========================>.....] - ETA: 59s 470630400/553467096 [========================>.....] - ETA: 59s 470712320/553467096 [========================>.....] - ETA: 59s 470925312/553467096 [========================>.....] - ETA: 59s 471089152/553467096 [========================>.....] - ETA: 59s 471220224/553467096 [========================>.....] - ETA: 59s 471302144/553467096 [========================>.....] - ETA: 59s 471515136/553467096 [========================>.....] - ETA: 58s 471678976/553467096 [========================>.....] - ETA: 58s……536248320/553467096 [============================>.] - ETA: 12s 536772608/553467096 [============================>.] - ETA: 11s 537329664/553467096 [============================>.] - ETA: 11s 537378816/553467096 [============================>.] - ETA: 11s 537444352/553467096 [============================>.] - ETA: 11s 537640960/553467096 [============================>.] - ETA: 11s 537755648/553467096 [============================>.] - ETA: 11s 537788416/553467096 [============================>.] - ETA: 10s 537821184/553467096 [============================>.] - ETA: 10s……551862272/553467096 [============================>.] - ETA: 1s 551993344/553467096 [============================>.] - ETA: 1s 552042496/553467096 [============================>.] - ETA: 0s 552124416/553467096 [============================>.] - ETA: 0s 552337408/553467096 [============================>.] - ETA: 0s 552501248/553467096 [============================>.] - ETA: 0s 552517632/553467096 [============================>.] - ETA: 0s 552583168/553467096 [============================>.] - ETA: 0s 552697856/553467096 [============================>.] - ETA: 0s 552910848/553467096 [============================>.] - ETA: 0s 553041920/553467096 [============================>.] - ETA: 0s 553123840/553467096 [============================>.] - ETA: 0s 553287680/553467096 [============================>.] - ETA: 0s 553467904/553467096 [==============================] - 386s 1us/step 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.json8192/35363 [=====>........................] - ETA: 0s 16384/35363 [============>.................] - ETA: 0s 40960/35363 [==================================] - 0s 6us/step 79.02% : macaw6.61% : bubble3.64% : vine_snake1.90% : pinwheel1.22% : knot 50.31% : shower_curtain 17.08% : handkerchief 12.75% : mosquito_net2.87% : window_shade1.32% : toilet_tissue 45.08% : shower_curtain 21.84% : mosquito_net 11.55% : handkerchief2.02% : window_shade0.91% : Windsor_tie 26.75% : spoonbill7.06% : black_stork7.04% : wooden_spoon4.21% : limpkin3.72% : 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 1/201/100 [..............................] - 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 …… …… 100/100 [==============================] - 2850s 29s/step - loss: 0.9524 - categorical_accuracy: 0.5480 - val_loss: 0.8089 - val_categorical_accuracy: 0.6377 Epoch 3/201/100 [..............................] - 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 …… ……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 10/201/100 [..............................] - 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 11/201/100 [..............................] - 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 15/201/100 [..............................] - ETA: 20:10 - loss: 0.5337 - categorical_accuracy: 0.70002/100 [..............................] - 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 16/201/100 [..............................] - ETA: 20:07 - loss: 0.2315 - categorical_accuracy: 1.0000 ……19/100 [====>.........................] - ETA: 16:29 - loss: 0.5293 - categorical_accuracy: 0.7816

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總結(jié)

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