Loaded Keras Model Throws Error While Predicting (Likely Issues with Masking)

[JoeDoyle12]

I am currently developing and testing a RNN that relies upon a large amount of data for training, and so have attempted to separate my training and testing files. I have one file where I create, train, and save a tensorflow.keras model to a file 'model.keras' I then load this model in another file and predict some values, but get the following error: Failed to convert elements of {'class_name': 'tensor', 'config': {'dtype': 'float64', 'value': [0.0, 0.0, 0.0, 0.0]}} to Tensor. Consider casting elements to a supported type. See https://www.tensorflow.org/api_docs/python/tf/dtypes for supported TF dtypes

By the way, I have tried running model.predict with this exact same data in the file where I train the model, and it works smoothly. The model loading must be the problem, not the data used to predict.

This mysterious float64 tensor is the value I passed into the masking layer. I don't understand why keras is unable to recognize this JSON object as a Tensor and apply the masking operation as such. I have included snippets of my code below, edited for clarity and brevity:

model_generation.py:

# Create model

model = tf.keras.Sequential([
    tf.keras.layers.Input((352, 4)),
    tf.keras.layers.Masking(mask_value=tf.convert_to_tensor(np.array([0.0, 0.0, 0.0, 0.0]))),
    tf.keras.layers.GRU(50, return_sequences=True, activation='tanh'),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.GRU(50,activation='tanh'),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Dense(units=1, activation='sigmoid')])

# Compile Model...
# Train Model...
model.save('model.keras')

model.predict(data) # Line works here

model_testing.py

model = tf.keras.models.load_model('model.keras')

model.predict(data) # this line generates the error

I have tried to re-load the model in the model_generation.py file and I get the exact same issue.

[dhantule]

Hi @JoeDoyle12, thanks for reporting this. The error seems to be coming from the mask_value that you are trying to pass. If you try passing your like this mask_value=[0.0,0.0,0.0,0.0] , your code should work fine. Attaching gist

[github-actions]

This issue is stale because it has been open for 14 days with no activity. It will be closed if no further activity occurs. Thank you.

[Surya2k1]

@dhantule ,

The Masking layer accepting Tensor also for argument mask_value. After saving and loading the model the mask_value no longer a tensor. Hence the issue.

Either the Masking layer should not allow Tensors for mask_value or while reloading the model we need to take care of this explicitly.

[google-ml-butler]

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