How To Save Keras Model As Frozen Graph?

I am working with Tensorflow 2.0 and want to store the following Keras model as frozen graph. import tensorflow as tf model = tf.keras.Sequential() model.add(tf.keras.layers.Dense

Solution 1:

Freeze_Graph is now gone in Tensorflow 2.0. You can check it here Tensorflow 2.0 : frozen graph support.

Except for the .save method that you have in your code. .save Method is already saving a .pb ready for inference. As an alternative, you can also use the below code.

You can also use convert_variables_to_constants_v2

Below is the sample code.

import tensorflow as tf
import os
from tensorflow.python.tools import freeze_graph
from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2

model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(64, input_shape=(1,)))
model.add(tf.keras.layers.Dense(32, activation='relu'))
model.add(tf.keras.layers.Dense(16, activation='relu'))
model.add(tf.keras.layers.Dense(1, activation='softmax'))
model.compile(optimizer='adam', loss='mse')
model.summary()

# Convert Keras model to ConcreteFunction
full_model = tf.function(lambda x: model(x))
full_model = full_model.get_concrete_function(
    tf.TensorSpec(model.inputs[0].shape, model.inputs[0].dtype, name="yourInputName"))
# Get frozen ConcreteFunction
frozen_func = convert_variables_to_constants_v2(full_model)
frozen_func.graph.as_graph_def()
layers = [op.name for op in frozen_func.graph.get_operations()]
print("-" * 50)
print("Frozen model layers: ")
for layer in layers:
    print(layer)
print("-" * 50)
print("Frozen model inputs: ")
print(frozen_func.inputs)
print("Frozen model outputs: ")
print(frozen_func.outputs)
# Save frozen graph from frozen ConcreteFunction to hard drive
tf.io.write_graph(graph_or_graph_def=frozen_func.graph,
                  logdir="./frozen_models",
                  name="frozen_graph.pb",
                  as_text=False)

### USAGE ##defwrap_frozen_graph(graph_def, inputs, outputs, print_graph=False):
    def_imports_graph_def():
        tf.compat.v1.import_graph_def(graph_def, name="")

    wrapped_import = tf.compat.v1.wrap_function(_imports_graph_def, [])
    import_graph = wrapped_import.graph

    print("-" * 50)
    print("Frozen model layers: ")
    layers = [op.name for op in import_graph.get_operations()]
    if print_graph == True:
        for layer in layers:
            print(layer)
    print("-" * 50)

    return wrapped_import.prune(
        tf.nest.map_structure(import_graph.as_graph_element, inputs),
        tf.nest.map_structure(import_graph.as_graph_element, outputs))

## Example Usage #### Load frozen graph using TensorFlow 1.x functionswith tf.io.gfile.GFile("./frozen_models/frozen_graph.pb", "rb") as f:
    graph_def = tf.compat.v1.GraphDef()
    loaded = graph_def.ParseFromString(f.read())

# Wrap frozen graph to ConcreteFunctions
frozen_func = wrap_frozen_graph(graph_def=graph_def,
                                inputs=["yourInputName:0"],
                                outputs=["Identity:0"],
                                print_graph=True)
print("-" * 50)
print("Frozen model inputs: ")
print(frozen_func.inputs)
print("Frozen model outputs: ")
print(frozen_func.outputs)
# Get predictions for test images
predictions = frozen_func(yourInputName=tf.constant([[3.]]))
# Print the prediction for the first imageprint("-" * 50)
print("Example prediction reference:")
print(predictions[0].numpy())