Hi as a suggestion when computing the WGAN loss in your descriminator.
Newer versions of tensorflow will throw a warning an error at the usage of bolean_mask with sparse and shape changing tensors (for example when switching the batch size, fro a training/test set).
It is suggested to replace with dynamic_partition.
The warning will be:
UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape.
This may consume a large amount of memory.
"Converting sparse IndexedSlices to a dense Tensor of unknown shape. "
The original code is:
xy_neg = tf.boolean_mask(tf.transpose(self.scores), tf.cast(self.input_y[:, 0], tf.bool))
xy_pos = tf.boolean_mask(tf.transpose(self.scores), tf.cast(self.input_y[:, 1], tf.bool))
the change would look like this:
parts = tf.dynamic_partition(self.scores, tf.cast(self.input_y[:, 1], tf.int32l), 2)
xy_neg = parts[0]
xy_pos = parts[1]
Hi as a suggestion when computing the WGAN loss in your descriminator.
Newer versions of tensorflow will throw a warning an error at the usage of
bolean_maskwith sparse and shape changing tensors (for example when switching the batch size, fro a training/test set).It is suggested to replace with
dynamic_partition.The warning will be:
The original code is:
the change would look like this: