LSTM和CNN:ValueError:检查目标时出错:预期time_distributed_1具有3个维度，但数组的形状为(400，256) [英] LSTM and CNN: ValueError: Error when checking target: expected time_distributed_1 to have 3 dimensions, but got array with shape (400, 256)
问题描述
我想在数据上应用CNN和LSTM，我只选择一小部分数据；我的训练数据的大小为(400,50)，我的测试数据为(200,50).
仅使用CNN模型，它就可以正常工作而没有任何错误，添加LSTM模型时我只会遇到很多错误:
I want to apply CNN and LSTM on my data, I just choose a small set of data; My training data's size is (400,50)and my testing data is (200,50).
With only CNN model, it works without any errors, I just have many errors when adding the LSTM model:
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)
您可以在这里找到该模型的全部摘要:(我是LSTM的新手，这是我第一次使用它.)
You can find here the whole summary for this model:(I am new with LSTM it is the first time that I use it).
_________________________________________________________________
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
_________________________________________________________________
当我替换以下代码行时:
When I replace this lines of code:
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')))
仅此行:
model.add(LSTM(26, activation='tanh'))
比它运作得非常好.
如果您能帮助我，我将不胜感激.
I would be grateful if you could help me please.
推荐答案
因此LSTM层希望输入形状(样本，时间步长，特征).堆叠LSTM时，您应该return_sequences = True.这将提供形状的输出(样本，时间步长，单位)，从而使堆栈适合在一起-如果仅想预测向前一步(即下一个值)，则应在最后一个LSTM层上设置return_sequences = False. (在序列/时间序列中)-如果您不这样做，它将预测与输入中相同的时间步数.您也可以预测一个不同的数字(例如，根据过去的50个观察值预测下一个10，但是在Keras中这有点棘手).
So LSTM layers expect input in shape (Samples, Time steps, Features). When stacking LSTM you should return_sequences = True. This will give an output of shape (Samples, Time steps, units), thus allowing the stack to fit together - You should set return_sequences = False on the last LSTM-layer if you only want to predict one step ahead (i.e. the next value in the sequence/time series) - if you don't it will predict the same number of time steps as is in the input. You can of cause also predict a different number (e.g. given 50 past observations predict the next 10, but it is a little tricky in Keras).
在您的情况下，Conv/MaxPool层输出5个时间步长"，并且您在最后一个LSTM层上具有return_sequences = True-因此您的"y"必须具有形状(Samples，5、256)-否则应返回return_sequences =在最后一层为False，并且不使用TimeDistributed，因为您仅预测了一个时间步长.
In your case the Conv/MaxPool-layers output 5 "time steps" and you have return_sequences = True on the last LSTM-layer - so your "y" must have shape (Samples, 5, 256) - otherwise turn return_sequences = False on the last layer and don't use TimeDistributed, as you only predict one time step ahead.
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