keras 的 example 文件 cifar10_cnn.py 解析
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keras 的 example 文件 cifar10_cnn.py 解析
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這個示例很簡單，就是從cifar10中讀取數據集，通過卷積神經網絡進行圖像識別
輸入數據的shape
x_train.shape (50000, 32, 32, 3)
y_train.shape (50000, 10)
?
神經網絡結構：
________________________________________________________________________________
Layer (type) Output Shape Param #
================================================================================
conv2d_1 (Conv2D) (None, 32, 32, 32) 896
________________________________________________________________________________
activation_1 (Activation) (None, 32, 32, 32) 0
________________________________________________________________________________
conv2d_2 (Conv2D) (None, 30, 30, 32) 9248
________________________________________________________________________________
activation_2 (Activation) (None, 30, 30, 32) 0
________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D) (None, 15, 15, 32) 0
________________________________________________________________________________
dropout_1 (Dropout) (None, 15, 15, 32) 0
________________________________________________________________________________
conv2d_3 (Conv2D) (None, 15, 15, 64) 18496
________________________________________________________________________________
activation_3 (Activation) (None, 15, 15, 64) 0
________________________________________________________________________________
conv2d_4 (Conv2D) (None, 13, 13, 64) 36928
________________________________________________________________________________
activation_4 (Activation) (None, 13, 13, 64) 0
________________________________________________________________________________
max_pooling2d_2 (MaxPooling2D) (None, 6, 6, 64) 0
________________________________________________________________________________
dropout_2 (Dropout) (None, 6, 6, 64) 0
________________________________________________________________________________
flatten_1 (Flatten) (None, 2304) 0
________________________________________________________________________________
dense_1 (Dense) (None, 512) 1180160
________________________________________________________________________________
activation_5 (Activation) (None, 512) 0
________________________________________________________________________________
dropout_3 (Dropout) (None, 512) 0
________________________________________________________________________________
dense_2 (Dense) (None, 10) 5130
________________________________________________________________________________
activation_6 (Activation) (None, 10) 0
================================================================================
Total params: 1,250,858
Trainable params: 1,250,858
Non-trainable params: 0
________________________________________________________________________________代碼同時演示了?ImageDataGenerator 的使用
?
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keras的example文件解析
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