Qfyilyi

I am currently stuck at a problem. I need to create a temporary variable (of tensor type) for holding values when my custom layer is processing the input. The problem is, the layer gets its inputs in a batch. Batch size is variable. Also, one other dimension of the input is variable which will be provided at the time of running the model.

So, how can I create a tensor of variable dimension every time for a new input. Surely there is a way to do this because built-in layers also take input as a batch whose size is variable and also the shape of output tensor varies as it outputs the whole processed batch. But I don't know how to do it.

Here's the piece of my code:

similarity_matrix = K.zeros(shape=(int_shape(context_vectors)[0], int_shape(context_vectors)[1], int_shape(query_vectors)[1]), dtype='float32')

for i, context_vector in enumerate(modified_context_vectors):

    for j, query_vector in enumerate(modified_query_vectors):

        concatenated_vector = concatenate_and_multiply(context_vector, query_vector)

        result = dot(concatenated_vector, self.kernel)

        similarity_matrix[:, i, j] = result

return similarity_matrix

Here, context_vectors and query_vectors are of shape (None, None, 600). The first dimension is batch_size which is same for both. The second dimension is the number of words in context and query and hence will differ for both.