ValueError: Could not find matching function to call loaded from the SavedModel. Got:
Positional arguments (2 total):
* Tensor("inputs:0", shape=(None, 28, 28), dtype=float32)
* Tensor("training:0", shape=(), dtype=bool)
Keyword arguments: {}
Expected these arguments to match one of the following 4 option(s):
Option 1:
Positional arguments (2 total):
* TensorSpec(shape=(None, 28, 28), dtype=tf.float32, name='input_1')
* False
Keyword arguments: {}
Option 2:
Positional arguments (2 total):
* TensorSpec(shape=(None, 28, 28), dtype=tf.float32, name='input_1')
* True
Keyword arguments: {}
Option 3:
Positional arguments (2 total):
* TensorSpec(shape=(None, 28, 28), dtype=tf.float32, name='inputs')
* False
Keyword arguments: {}
Option 4:
Positional arguments (2 total):
* TensorSpec(shape=(None, 28, 28), dtype=tf.float32, name='inputs')
* True
Keyword arguments: {}
There was probably just a problem with the model definition. Would be great to have this custom model with a dropout layer working so it mirror perfectly the Sequential example.
In Part 2 tutorial, for custom when including a Dropout layer in the custom model (
tf.keras.models.Modelsubclassing), we were getting the following error when sending the model to a worker ( model gets saved with tf.keras.models.save_model during the process), we were getting the following error in #33 :There was probably just a problem with the model definition. Would be great to have this custom model with a dropout layer working so it mirror perfectly the Sequential example.