keras 的 example 文件 imdb_bidirectional_lstm.py 解析
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keras 的 example 文件 imdb_bidirectional_lstm.py 解析
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imdb是一個文本情感分析的數據集，通過評論來分析觀眾對電影是好評還是差評
其網絡結構比較簡單
________________________________________________________________________________
Layer (type) Output Shape Param #
================================================================================
embedding_1 (Embedding) (None, 100, 128) 2560000
________________________________________________________________________________
bidirectional_1 (Bidirectional) (None, 128) 98816
________________________________________________________________________________
dropout_1 (Dropout) (None, 128) 0
________________________________________________________________________________
dense_1 (Dense) (None, 1) 129
================================================================================
Total params: 2,658,945
Trainable params: 2,658,945
Non-trainable params: 0
________________________________________________________________________________
對imdb數據集稍微分析一下，
通過函數load_data獲取到的x_train, y_train，是一堆編號，這個編號不太直接，可以通過下面代碼解析出來：
word_index = imdb.get_word_index()word_index = {k:(v+3) for k,v in word_index.items()}
word_index["<PAD>"] = 0
word_index["<START>"] = 1
word_index["<UNK>"] = 2 # unknown
word_index["<UNUSED>"] = 3reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])def decode_review(text):return ' '.join([reverse_word_index.get(i, '?') for i in text])for i in range(10):print(decode_review(x_train[i]))print(y_train[i])就可以看到評論的具體內容，而y_train打印出來的是0和1，分別代表差評和好評
x_train和y_train的shape分別為
(25000, 100)
(25000,)
?
——————————————————————————————————
不另開帖子了，把其他幾個網絡的結構也貼出來備忘：
imdb_cnn_lstm.py的神經網絡結構如下：
________________________________________________________________________________
Layer (type) Output Shape Param #
================================================================================
embedding_1 (Embedding) (None, 100, 128) 2560000
________________________________________________________________________________
dropout_1 (Dropout) (None, 100, 128) 0
________________________________________________________________________________
conv1d_1 (Conv1D) (None, 96, 64) 41024
________________________________________________________________________________
max_pooling1d_1 (MaxPooling1D) (None, 24, 64) 0
________________________________________________________________________________
lstm_1 (LSTM) (None, 70) 37800
________________________________________________________________________________
dense_1 (Dense) (None, 1) 71
________________________________________________________________________________
activation_1 (Activation) (None, 1) 0
================================================================================
Total params: 2,638,895
Trainable params: 2,638,895
Non-trainable params: 0
________________________________________________________________________________imdb_cnn.py的神經網絡結構如下：
____________________________________________________________________________________________________
Layer (type) Output Shape Param #
====================================================================================================
embedding_1 (Embedding) (None, 400, 50) 250000
____________________________________________________________________________________________________
dropout_1 (Dropout) (None, 400, 50) 0
____________________________________________________________________________________________________
conv1d_1 (Conv1D) (None, 398, 250) 37750
____________________________________________________________________________________________________
global_max_pooling1d_1 (GlobalMaxPooling1D) (None, 250) 0
____________________________________________________________________________________________________
dense_1 (Dense) (None, 250) 62750
____________________________________________________________________________________________________
dropout_2 (Dropout) (None, 250) 0
____________________________________________________________________________________________________
activation_1 (Activation) (None, 250) 0
____________________________________________________________________________________________________
dense_2 (Dense) (None, 1) 251
____________________________________________________________________________________________________
activation_2 (Activation) (None, 1) 0
====================================================================================================
Total params: 350,751
Trainable params: 350,751
Non-trainable params: 0
____________________________________________________________________________________________________
?
imdb_lstm.py的神經網絡結構為：
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
embedding_1 (Embedding) (None, None, 128) 2560000
_________________________________________________________________
lstm_1 (LSTM) (None, 128) 131584
_________________________________________________________________
dense_1 (Dense) (None, 1) 129
=================================================================
Total params: 2,691,713
Trainable params: 2,691,713
Non-trainable params: 0
_________________________________________________________________——————————————————————
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keras的example文件解析
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