Usually the input tensor of the
Conv2D in Keras is a 4D tensor with the dimension
batch_size * n * n * channel_size. Now I have a 5D tensor with the dimension
batch_size * N * n * n * channel_size and I want to apply the 2D convolutional layer for the last three dimensions for each i in
N. For example, if the kernel size is 1, then I expect that the output will have the dimension
batch_size * N * n * n * 1.
Anyone knows some easy ways to implement it with Keras?
For example, for the fully-connected layer Keras can do it automatically. If the input has the shape
batch_size * N * n, then the Dense layer in Keras will set a FC layer for each i in
N. Hence we will get the output with
batch_size * N * m, if we set