i am currently working with convolutional recurrent units (ConvLSTM & ConvGRU).
Both expect as Input a Tensor of shape:
[batch_size, timestep, num_channels, height, width].
For further processing I need the tensor to be of shape:
[batch_size, num_channels, height, width].
In my scenario I get the timesteps from saving previous 2 results [t-2, t-1, t] and stacking them along the 1 dimension. The result after the Recurrent Unit is of the same shape.
What is the best way to get from
[ _, 3,_,_, _ ] to [ _, 1, _, _, _ ] such that i could do
I was thinking about doing:
my_output_tensor.shape() # [8, 3, 2, 217, 512] my_output_tensor = my_output_tensor[:,-1,:,:,:] # [8, 2, 217, 512]
which gives me the output tensor for [t] but neglects the output of [t-2, t-1].
Alternatively I was thinking about appling a 1x1-3D Convolution along the timestep dimension to reduce the number of timestep features from 3 to 1.
I was wondering if there are any meaningful ways to reduce the dimensionality without loosing to much information
Thanks in Advance!