本例子是“IMDB sentiment classification task”，用單層LSTM實現。
1. 輸入數據預處理
輸入文本數據統一規整到長度maxlen=80個單詞，爲什麼呢？
輸入文本數據統一規整到長度maxlen=80個單詞，爲什麼呢？
是不是長度太長時訓練容易發散掉，這樣就限制了記憶的長度了。
x_train = sequence.pad_sequences(x_train, maxlen=maxlen)
x_train = sequence.pad_sequences(x_train, maxlen=maxlen)
有沒有動態的呢？因爲輸入的句子長度本身是動態長度的。
2. Embedding layer
代碼中，max_features=20000對應的是詞彙量大小。
關於Embedding Vector,如果是中文的，怎麼處理呢？
在RNN訓練過程中Embedding Vector是否也參與了訓練呢？如何選擇？
3. 關於RNN的模型架構的理解
Embedding Vector是128維的，隱層是128個節點（也可以是其他數值）。
Embedding Vector與隱層的節點是全連接的，隱層每個節點自帶存儲單元的。
在這個128節點的隱層之上，有一個Dense節點。這個Dense節點是和128節點的隱層全連接的。
Foward過程就是每次輸入80個單詞中的一個，直到最後一個單詞輸入結束，Dense節點最終的輸出就是估計的Y值了
完整代碼如下：
'''Trains a LSTM on the IMDB sentiment classification task.
The dataset is actually too small for LSTM to be of any advantage
compared to simpler, much faster methods such as TF-IDF + LogReg.
Notes:
- RNNs are tricky. Choice of batch size is important,
choice of loss and optimizer is critical, etc.
Some configurations won't converge.
- LSTM loss decrease patterns during training can be quite different
from what you see with CNNs/MLPs/etc.
'''
from __future__ import print_function
from keras.preprocessing import sequence
from keras.models import Sequential
from keras.layers import Dense, Embedding
from keras.layers import LSTM
from keras.datasets import imdb
max_features = 20000
maxlen = 80 # cut texts after this number of words (among top max_features most common words)
batch_size = 32
print('Loading data...')
(x_train, y_train), (x_test, y_test) = imdb.load_data(nb_words=max_features)
print(len(x_train), 'train sequences')
print(len(x_test), 'test sequences')
print('Pad sequences (samples x time)')
x_train = sequence.pad_sequences(x_train, maxlen=maxlen)
x_test = sequence.pad_sequences(x_test, maxlen=maxlen)
print('x_train shape:', x_train.shape)
print('x_test shape:', x_test.shape)
print('Build model...')
model = Sequential()
model.add(Embedding(max_features, 128))
#model.add(LSTM(128, dropout=0.2, recurrent_dropout=0.2))
model.add(LSTM(128))
model.add(Dense(1, activation='sigmoid'))
# try using different optimizers and different optimizer configs
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
print('Train...')
model.fit(x_train, y_train,
batch_size=batch_size,
nb_epoch=15,
validation_data=(x_test, y_test))
score, acc = model.evaluate(x_test, y_test,
batch_size=batch_size)
print('Test score:', score)
print('Test accuracy:', acc)通過summary()得到的函數統計如下：
____________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
====================================================================================================
embedding_3 (Embedding) (None, None, 128) 2560000 embedding_input_1[0][0]
____________________________________________________________________________________________________
lstm_1 (LSTM) (None, 128) 131584 embedding_3[0][0]
____________________________________________________________________________________________________
dense_9 (Dense) (None, 1) 129 lstm_1[0][0]
====================================================================================================
Total params: 2,691,713
Trainable params: 2,691,713
Non-trainable params: 0