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?
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.
you can also consider TimeDistributed(LSTM(...))
inp = Input(shape=(64, 100, 50))
x = TimeDistributed(LSTM(256, return_sequences=True))(inp)
model = Model(inp, x)
model.compile('adam', 'mse')
model.summary()
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