I'm working with RaggedTensors to manipulate a dense tensor. Something like this :
out_left = tf.ragged.boolean_mask(input, index)
index = tf.math.logical_not(index)
out_right = tf.ragged.boolean_mask(input, index)
reconstruced_tensor = tf.concat([out_left, out_right], axis=-1)
reconstruced_tensor = reconstruced_tensor.to_tensor()
As you can see, in my example, I'm just splitting my input tensor using RaggedTensors and reconstructing it (I know its useless, but it's just for the sake of simplicity)
The problem I'm having is that I'm getting the following warning:
IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model_14/channel_roll_13/RaggedToTensor/boolean_mask_1/GatherV2:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model_14/channel_roll_13/RaggedToTensor/boolean_mask/GatherV2:0", shape=(None,), dtype=float32), dense_shape=Tensor("gradient_tape/model_14/channel_roll_13/RaggedToTensor/Shape:0", shape=(1,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory. "shape. This may consume a large amount of memory." % value)
Since I know the shape of the output Tensor, I would have thought Tensorflow would too. Is there any way I can explicitly specify the shape of the output tensor since Tensorflow doesn't seem to deduce it on its own?
