我想对我的数据应用CNN和LSTM，我只选择一小组数据;我的培训数据大小为(400,50)，我的测试数据为(200,50)。
只有CNN模型，它没有任何错误，我添加LSTM模型时只有很多错误：
model = Sequential()
model.add(Conv1D(filters=8,
kernel_size=16,
padding='valid',
activation='relu',
strides=1, input_shape=(50,1)))
model.add(MaxPooling1D(pool_size=2,strides=None, padding='valid', input_shape=(50,1))) # strides=None means strides=pool_size
model.add(Conv1D(filters=8,
kernel_size=8,
padding='valid',
activation='relu',
strides=1))
model.add(MaxPooling1D(pool_size=2,strides=None, padding='valid',input_shape=(50,1)))
model.add(LSTM(32, return_sequences=True,
activation='tanh', recurrent_activation='hard_sigmoid',
dropout=0.2,recurrent_dropout=0.2)) # 100 num of LSTM units
model.add(LSTM(32, return_sequences=True,
activation='tanh', recurrent_activation='hard_sigmoid',
dropout=0.2,recurrent_dropout=0.2))
model.add(LSTM(32, return_sequences=True,
activation='tanh', recurrent_activation='hard_sigmoid',
dropout=0.2,recurrent_dropout=0.2))
model.add(LSTM(32, return_sequences=True,
activation='tanh', recurrent_activation='hard_sigmoid',
dropout=0.2,recurrent_dropout=0.2))
model.add(LSTM(32, return_sequences=True,
activation='tanh', recurrent_activation='hard_sigmoid',
dropout=0.2,recurrent_dropout=0.2))
model.add(TimeDistributed(Dense(256, activation='softmax')))
# # # 4. Compile model
print('########################### Compilation of the model ######################################')
model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
print(model.summary())
print('###########################Fitting the model ######################################')
# # # # # 5. Fit model on training data
x_train = x_train.reshape((400,50,1))
print(x_train.shape) # (400,50,1)
x_test = x_test.reshape((200,50,1))
print(x_test.shape) # (200,50,1)
model.fit(x_train, y_train, batch_size=100, epochs=100,verbose=0)
print(model.summary())
# # # # # 6. Evaluate model on test data
score = model.evaluate(x_test, y_test, verbose=0)
print (score)
这是错误：
Traceback (most recent call last):
File "CNN_LSTM_Based_Attack.py", line 156, in <module>
model.fit(x_train, y_train, batch_size=100, epochs=100,verbose=0)
File "/home/doc/.local/lib/python2.7/site-packages/keras/models.py", line 853, in fit
initial_epoch=initial_epoch)
File "/home/doc/.local/lib/python2.7/site-packages/keras/engine/training.py", line 1424, in fit
batch_size=batch_size)
File "/home/doc/.local/lib/python2.7/site-packages/keras/engine/training.py", line 1304, in _standardize_user_data
exception_prefix='target')
File "/home/doc/.local/lib/python2.7/site-packages/keras/engine/training.py", line 127, in _standardize_input_data
str(array.shape))
ValueError: Error when checking target: expected time_distributed_1 to have 3 dimensions, but got array with shape (400, 256)
你可以在这里找到这个模型的完整摘要:(我是LSTM的新手，这是我第一次使用它。）
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv1d_1 (Conv1D) (None, 35, 8) 136
_________________________________________________________________
max_pooling1d_1 (MaxPooling1 (None, 17, 8) 0
_________________________________________________________________
dropout_1 (Dropout) (None, 17, 8) 0
_________________________________________________________________
conv1d_2 (Conv1D) (None, 10, 8) 520
_________________________________________________________________
max_pooling1d_2 (MaxPooling1 (None, 5, 8) 0
_________________________________________________________________
dropout_2 (Dropout) (None, 5, 8) 0
_________________________________________________________________
lstm_1 (LSTM) (None, 5, 32) 5248
_________________________________________________________________
lstm_2 (LSTM) (None, 5, 32) 8320
_________________________________________________________________
lstm_3 (LSTM) (None, 5, 32) 8320
_________________________________________________________________
lstm_4 (LSTM) (None, 5, 32) 8320
_________________________________________________________________
lstm_5 (LSTM) (None, 5, 32) 8320
_________________________________________________________________
time_distributed_1 (TimeDist (None, 5, 256) 8448
=================================================================
Total params: 47,632
Trainable params: 47,632
Non-trainable params: 0
_________________________________________________________________
当我替换这行代码时：
model.add(LSTM(32, return_sequences=True,
activation='tanh', recurrent_activation='hard_sigmoid',
dropout=0.2,recurrent_dropout=0.2)) # 100 num of LSTM units
model.add(LSTM(32, return_sequences=True,
activation='tanh', recurrent_activation='hard_sigmoid',
dropout=0.2,recurrent_dropout=0.2))
model.add(LSTM(32, return_sequences=True,
activation='tanh', recurrent_activation='hard_sigmoid',
dropout=0.2,recurrent_dropout=0.2))
model.add(LSTM(32, return_sequences=True,
activation='tanh', recurrent_activation='hard_sigmoid',
dropout=0.2,recurrent_dropout=0.2))
model.add(LSTM(32, return_sequences=True,
activation='tanh', recurrent_activation='hard_sigmoid',
dropout=0.2,recurrent_dropout=0.2))
model.add(TimeDistributed(Dense(256, activation='softmax')))
只有这一行：
model.add(LSTM(26, activation='tanh'))
比它运作得很好。
如果你能帮助我，我将不胜感激。
答案 0 :(得分：4)
因此LSTM层期望输入形状（样本，时间步长，特征）。堆叠LSTM时，你应该return_sequences = True。这将给出形状的输出（样本，时间步长，单位），从而允许堆栈合在一起 - 如果您只想预测一步（即下一个值），您应该在最后一个LSTM层上设置return_sequences = False在序列/时间序列中） - 如果不这样做，它将预测与输入中相同的时间步数。你可以预测一个不同的数字（例如，给定50个过去的观察预测接下来的10个，但在Keras中有点棘手）。
在你的情况下，Conv / MaxPool层输出5个“时间步长”，你在最后一个LSTM层上有return_sequences = True - 所以你的“y”必须有形状（Samples，5,256） - 否则转为return_sequences =在最后一层上为假，并且不使用TimeDistributed，因为您只预测前一步。