CODEWITHSUNDEEP
pythontensorflowkeras
Marcin Pietraszek
Marcin Pietraszek

Reputation: 3214

How to save dummy tensorflow model for serving?

I'd like to save dummy tensorflow model to use it later with tensorflow serving. I've tried to prepare such model using following snippet:

import tensorflow as tf

input0 = tf.keras.Input(shape=[2], name="input_0", dtype="int32")
input1 = tf.keras.Input(shape=[2], name="input_1", dtype="int32")
output = tf.keras.layers.Add()([input0, input1])

model = tf.keras.Model(inputs=[input0, input1], outputs=output)

predict_function = tf.function(
    func=model.call,
    input_signature=[input0.type_spec, input1.type_spec],
)

signatures = {
    "predict": predict_function.get_concrete_function(
        [input0.get_shape(), input1.get_shape()],
    ),
}

model.save(
    filepath="some/dummy/path",
    signatures=signatures,
)

Running the code to save the model ends with following error:

AssertionError: Could not compute output KerasTensor(type_spec=TensorSpec(shape=(None, 2), dtype=tf.int32, name=None), name='add/add:0', description="created by layer 'add'")

What should I do to be able to save dummy model with signatures to use it later with tensorflow serving?

Upvotes: 0

Views: 175

Answers (1)

AndrzejO
AndrzejO

Reputation: 1582

According to the model.call documentation, you should always use __call__:

call

This method should not be called directly. It is only meant to be overridden when subclassing tf.keras.Model. To call a model on an input, always use the __call__() method, i.e. model(inputs), which relies on the underlying call() method.

Then, I am not sure how several inputs in a list should be handled, so I would just use a lambda:

func = lambda x, y: model.__call__([x, y]),

When I changed the signatures such that they match, the model could be saved. Don't know about tensorflow serving.

import tensorflow as tf

input0 = tf.keras.Input(shape=[2], name="input_0", dtype="int32")
input1 = tf.keras.Input(shape=[2], name="input_1", dtype="int32")
output = tf.keras.layers.Add()([input0, input1])

model = tf.keras.Model(inputs=[input0, input1], outputs=output)

predict_function = tf.function(
    func = lambda x, y: model.__call__([x,y]),
    input_signature=[input0.type_spec, input1.type_spec],
)

signatures = {
    "predict": predict_function.get_concrete_function(
        input0.type_spec, input1.type_spec,
    ),
}

model.save(
    filepath="some/dummy/path",
    signatures=signatures,
)

After loading, the model seems to work correctly:

print(model([[5], [6]]))
tf.Tensor(11, shape=(), dtype=int32)

Upvotes: 1

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