Python keras how to change the size of input after convolution layer into lstm layer

Question

I have a problem with the connection between convolution layer and lstm layer. The data is of shape(75,5) where there is 75 timesteps x 5 data points for each time step. Wha

Answer 1

You can add Reshape() layer in between to make dimensions compatible.

http://keras.io/layers/core/#reshape

keras.layers.core.Reshape(dims)

Reshape an output to a certain shape.

Input shape

Arbitrary, although all dimensions in the input shaped must be fixed. Use the keyword argument input_shape (tuple of integers, does not include the samples axis) when using this layer as the first layer in a model.

Output shape

(batch_size,) + dims

Arguments

dims: target shape. Tuple of integers, does not include the samples dimension (batch size).