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Implementation of the model "sketch-RNN" by google for generating sketches with a variational auto encoder
Hi Team,
When I am using ISPR when training when we run history object.
Error:
TypeError: You are passing KerasTensor(type_spec=TensorSpec(shape=(), dtype=tf.float32, name=None), name='Placeholder:0', description="created by layer 'tf.cast_20'"), an intermediate Keras symbolic input/output, to a TF API that does not allow registering custom dispatchers, such as tf.cond
, tf.function
, gradient tapes, or tf.map_fn
. Keras Functional model construction only supports TF API calls that do support dispatching, such as tf.math.add
or tf.reshape
. Other APIs cannot be called directly on symbolic Kerasinputs/outputs. You can work around this limitation by putting the operation in a custom Keras layer call
and calling that layer on this symbolic input/output.
I feel that it has to do with tensorflow 2.x version also using custom KL divergence API
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