Description
The two options for inputs are scalar and one-hot, with one-hot being the default, right?
If I set the parameters in wavenet_params.json to use scalar, the network trains just fine. But when I try to generate, it says "Incremental generation does not support scalar input yet." That seems fair enough. It looks like the easiest solution to this is to turn off "fast_generation", which will call the function predict_proba instead of predict_proba_incremental. However, when I do that, I get this error:
ValueError: Shape must be rank 4 but is rank 3 for 'wavenet_1/causal_layer/causal_conv/conv1d/Conv2D' (op: 'Conv2D') with input shapes: [?,1,1], [1,32,1,32].
This kinda makes sense to me, because a scalar has one fewer dimension than a one-hot vector, so I could understand why the resulting tensor's shape now has 3 dimensions instead of 4. However, when I tried reshaping the tensor to have 4 dimensions, I still couldn't get it to work. I don't think I understand TensorFlow or this code closely enough to fix this problem. Help!