问题
I have a sequence input in this shape: (6000, 64, 100, 50)
The 6000 is just the number of sample sequences. Each sequences is 64 in length.
I plan to fit this input into an LSTM using Keras.
I setup my input this way:
input = Input(shape=(64, 100, 50))
This gives me an input shape of (?, 64, 100, 50)
However, when I put input into my LSTM like so:
x = LSTM(256, return_sequences=True)(input)
I get this error:
Input 0 is incompatible with layer lstm_37: expected ndim=3, found ndim=4
This would have worked if my input shape was something like (?, 64, 100), but not when I've a 4th dimension.
Does this mean that LSTM can only take an input of 3 dimensional? How can I feed a 4 or even higher dimension input into LSTM using Keras?
回答1:
The answer is you can't.
The Keras Documentation provides the following information for Recurrent Layer:
Input shape
3D tensor with shape (batch_size, timesteps, input_dim).
In your case you have 64 timesteps where each step is of shape (100, 50). The easiest way to get the model working is to reshape your data to (100*50).
Numpy provides an easy function to do so:
X = numpy.zeros((6000, 64, 100, 50), dtype=numpy.uint8)
X = numpy.reshape(X, (6000, 64, 100*50))
Wheter this is reasonable or not highly depends on your data.
来源：https://stackoverflow.com/questions/47410239/how-to-feed-into-lstm-with-4-dimensional-input