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I wrote in google-collab to get the model using keras, but I have to do predictions in Visual Studio using tensorflow

I've search for a method converting models from keras .h5 to tensorflow .pb, but none of them worked as it should.

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Keras does not include by itself any means to export a TensorFlow graph as a protocol buffers file, but you can do it using regular TensorFlow utilities. Here is a blog post explaining how to do it using the utility script freeze_graph.py included in TensorFlow, which is the "typical" way it is done.

However, I personally find a nuisance having to make a checkpoint and then run an external script to obtain a model, and instead prefer to do it from my own Python code, so I use a function like this:

def freeze_session(session, keep_var_names=None, output_names=None, clear_devices=True):
    """
    Freezes the state of a session into a pruned computation graph.
 
    Creates a new computation graph where variable nodes are replaced by
    constants taking their current value in the session. The new graph will be
    pruned so subgraphs that are not necessary to compute the requested
    outputs are removed.
    @param session The TensorFlow session to be frozen.
    @param keep_var_names A list of variable names that should not be frozen,
                          or None to freeze all the variables in the graph.
    @param output_names Names of the relevant graph outputs.
    @param clear_devices Remove the device directives from the graph for better portability.
    @return The frozen graph definition.
    """
    graph = session.graph
    with graph.as_default():
        freeze_var_names = list(set(v.op.name for v in tf.global_variables()).difference(keep_var_names or []))
        output_names = output_names or []
        output_names += [v.op.name for v in tf.global_variables()]
        input_graph_def = graph.as_graph_def()
        if clear_devices:
            for node in input_graph_def.node:
                node.device = ""
        frozen_graph = tf.graph_util.convert_variables_to_constants(
            session, input_graph_def, output_names, freeze_var_names)
        return frozen_graph

Which is inspired in the implementation of freeze_graph.py. The parameters are similar to the script too. session is the TensorFlow session object. keep_var_names is only needed if you want to keep some variable not frozen (e.g. for stateful models), so generally not. output_names is a list with the names of the operations that produce the outputs that you want. clear_devices just removes any device directives to make the graph more portable. So, for a typical Keras model with one output, you would do something like:

from keras import backend as K

# Create, compile and train model...

frozen_graph = freeze_session(K.get_session(),
                              output_names=[out.op.name for out in model.outputs])

Then you can write the graph to a file as usual with tf.train.write_graph:

tf.train.write_graph(frozen_graph, "some_directory", "my_model.pb", as_text=False)

Credits: jdehesa

You can find more at:

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There are different tools for converting hdf5 to .bp file as:

1 - convert trained Keras model to a single TensorFlow .pb file
2 - or keras-to-tensorflow as alternative

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    $\begingroup$ It'd be good if you could include more information than just links, so the answer is self-contained (or make a comment out of this answer; just including the links). $\endgroup$ – n1k31t4 Nov 4 '19 at 9:07
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for tensorflow 2

import tensorflow as tf
model = tf.keras.models.load_model(keras_model_path)
tf.saved_model.save(model, "save/folder/path")

Other alternative

import tensorflow as tf
model = tf.keras.models.load_model(keras_model_path)
model.save("save/folder/path")

These two approaches create a folder and stores the modelfile as saved_model.pb.

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