I have saved deepFM model using libsering.serialization.tf_saved(model). I load the model using:
loaded_model = tf.saved_model.load('model')
The model predict signature looks like:
_SignatureMap({'predict': <ConcreteFunction pruned(dense_values, item_indices, sparse_indices, user_indices) at 0x56E44DA90>})
However, when I am trying to call model using following, it gives me error:
restored_output_tensor = test_model.signatures['predict'](feed_dict) # where feed_dict is dictionary of features.
`TypeError: pruned(dense_values, item_indices, sparse_indices, user_indices) takes 0 positional arguments, got 1.`
Please find screenshot of the same.
![Uploading Screenshot 2024-07-27 at 14.00.10.png…]()
Hello,
I have saved deepFM model using libsering.serialization.tf_saved(model). I load the model using: loaded_model =
tf.saved_model.load('model')
The model predict signature looks like:
_SignatureMap({'predict': <ConcreteFunction pruned(dense_values, item_indices, sparse_indices, user_indices) at 0x56E44DA90>})
However, when I am trying to call model using following, it gives me error:
Please find screenshot of the same. ![Uploading Screenshot 2024-07-27 at 14.00.10.png…]()