keras 的 example 文件 mnist_cnn.py 解析
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keras 的 example 文件 mnist_cnn.py 解析
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mnist_cnn.py 基本上就是最簡單的一個卷積神經(jīng)網(wǎng)絡(luò)了，其結(jié)構(gòu)如下：
________________________________________________________________________________
Layer (type) Output Shape Param #
================================================================================
conv2d_1 (Conv2D) (None, 26, 26, 32) 320
________________________________________________________________________________
conv2d_2 (Conv2D) (None, 24, 24, 64) 18496
________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D) (None, 12, 12, 64) 0
________________________________________________________________________________
dropout_1 (Dropout) (None, 12, 12, 64) 0
________________________________________________________________________________
flatten_1 (Flatten) (None, 9216) 0
________________________________________________________________________________
dense_1 (Dense) (None, 128) 1179776
________________________________________________________________________________
dropout_2 (Dropout) (None, 128) 0
________________________________________________________________________________
dense_2 (Dense) (None, 10) 1290
================================================================================
Total params: 1,199,882
Trainable params: 1,199,882
Non-trainable params: 0
________________________________________________________________________________不再過多解釋
?
另一個更簡單的網(wǎng)絡(luò)結(jié)構(gòu)為?mnist_mlp.py，即?多層感知器（MLP，Multilayer Perceptron）
__________________________________________________
Layer (type) Output Shape Param #
==================================================
dense_1 (Dense) (None, 512) 401920
__________________________________________________
dropout_1 (Dropout) (None, 512) 0
__________________________________________________
dense_2 (Dense) (None, 512) 262656
__________________________________________________
dropout_2 (Dropout) (None, 512) 0
__________________________________________________
dense_3 (Dense) (None, 10) 5130
==================================================
Total params: 669,706
Trainable params: 669,706
Non-trainable params: 0
__________________________________________________用全連接堆疊起來的圖像識別
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keras的example文件解析
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