我在卷积层和lstm层之间的连接有问题。数据的形状为（75,5），其中每个时间步长有75个时间步长x 5个数据点。我想做的是在（75x5）上进行卷积，获取新的卷积（75x5）数据并将该数据馈送到lstm层。但是，由于卷积层的输出形状具有不需要的滤波器数量，因此不起作用。因此，卷积层输出的形状为（1,75,5），而lstm层所需的输入为（75,5）。我如何只使用第一个过滤器。
model = Sequential()
model.add(Convolution2D(1, 5,5,border_mode='same',input_shape=(1,75, 5)))
model.add(Activation('relu'))
model.add(LSTM(75, return_sequences=False, input_shape=(75, 5)))
model.add(Dropout(0.5))
model.add(Dense(1))
model.compile(loss='mse', optimizer='rmsprop')
这是出现的错误：
File "/usr/local/lib/python3.4/dist-packages/keras/layers/recurrent.py", line 378, in __init__
super(LSTM, self).__init__(**kwargs)
File "/usr/local/lib/python3.4/dist-packages/keras/layers/recurrent.py", line 97, in __init__
super(Recurrent, self).__init__(**kwargs)
File "/usr/local/lib/python3.4/dist-packages/keras/layers/core.py", line 43, in __init__
self.set_input_shape((None,) + tuple(kwargs['input_shape']))
File "/usr/local/lib/python3.4/dist-packages/keras/layers/core.py", line 138, in set_input_shape
', was provided with input shape ' + str(input_shape))
Exception: Invalid input shape - Layer expects input ndim=3, was provided with input shape (None, 1, 75, 5)