keras 的 example 文件 mnist_cnn.py 解析keras的example文件解析

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我是靠谱客的博主冷静蜡烛，这篇文章主要介绍keras 的 example 文件 mnist_cnn.py 解析keras的example文件解析，现在分享给大家，希望可以做个参考。

mnist_cnn.py 基本上就是最简单的一个卷积神经网络了，其结构如下：

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________________________________________________________________________________
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
________________________________________________________________________________

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________________________________________________________________________________
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
________________________________________________________________________________

不再过多解释

另一个更简单的网络结构为 mnist_mlp.py，即多层感知器（MLP，Multilayer Perceptron）

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__________________________________________________
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
__________________________________________________