很顯然，我沒有看懂 HRNN 是啥意思，沒有去看論文，應該就是一種RNN結構的變形吧

網絡結構如下：

__________________________________________________________________________________________
Layer (type)                            Output Shape                        Param #
==========================================================================================
input_1 (InputLayer)                    (None, 28, 28, 1)                   0
__________________________________________________________________________________________
time_distributed_1 (TimeDistributed)    (None, 28, 128)                     66560
__________________________________________________________________________________________
lstm_2 (LSTM)                           (None, 128)                         131584
__________________________________________________________________________________________
dense_1 (Dense)                         (None, 10)                          1290
==========================================================================================
Total params: 199,434
Trainable params: 199,434
Non-trainable params: 0
__________________________________________________________________________________________

輸入是圖片，輸出是分類

類似的，mnist_irnn.py 的網絡結構如下：

_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
simple_rnn_1 (SimpleRNN)     (None, 100)               10200
_________________________________________________________________
dense_1 (Dense)              (None, 10)                1010
_________________________________________________________________
activation_1 (Activation)    (None, 10)                0
=================================================================
Total params: 11,210
Trainable params: 11,210
Non-trainable params: 0
_________________________________________________________________

——————————————————————

總目錄

keras的example文件解析

總結

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