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I have fine-tuned inception model with a new dataset and saved it as ".h5" model in Keras. now my goal is to run my model on android Tensorflow which accepts ".pb" extension only. question is that is there any library in Keras or tensorflow to do this conversion? I have seen this post so far : https://blog.keras.io/keras-as-a-simplified-interface-to-tensorflow-tutorial.html but can't figure out yet.

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)

The freeze_session method works fine. But compared to saving to a checkpoint file then using the freeze_graph tool that comes with TensorFlow seems simpler to me, as it's easier to maintain. All you need to do is the following two steps:

First, add after your Keras code model.fit(...) and train your model:

from keras import backend as K

import tensorflow as tf

print(model.output.op.name)

saver = tf.train.Saver()

saver.save(K.get_session(), '/tmp/keras_model.ckpt')

Then cd to your TensorFlow root directory, run:

python tensorflow/python/tools/freeze_graph.py 

--input_meta_graph=/tmp/keras_model.ckpt.meta 

--input_checkpoint=/tmp/keras_model.ckpt 

--output_graph=/tmp/keras_frozen.pb 

--output_node_names="<output_node_name_printed_in_step_1>" 

--input_binary=true

The following simple example (XOR example) shows how to export Keras models (in both h5 format and pb format), and using the model in Python and C++:

train.py:

import numpy as np

import tensorflow as tf





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





X = np.array([[0,0], [0,1], [1,0], [1,1]], 'float32')

Y = np.array([[0], [1], [1], [0]], 'float32')



model = tf.keras.models.Sequential()

model.add(tf.keras.layers.Dense(64, input_dim=2, activation='relu'))

model.add(tf.keras.layers.Dense(64, activation='relu'))

model.add(tf.keras.layers.Dense(64, activation='relu'))

model.add(tf.keras.layers.Dense(64, activation='relu'))

model.add(tf.keras.layers.Dense(1, activation='sigmoid'))



model.compile(loss='mean_squared_error', optimizer='adam', metrics=['binary_accuracy'])



model.fit(X, Y, batch_size=1, nb_epoch=100, verbose=0)



# inputs:  ['dense_input']

print('inputs: ', [input.op.name for input in model.inputs])



# outputs:  ['dense_4/Sigmoid']

print('outputs: ', [output.op.name for output in model.outputs])



model.save('./xor.h5')



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

tf.train.write_graph(frozen_graph, './', 'xor.pbtxt', as_text=True)

tf.train.write_graph(frozen_graph, './', 'xor.pb', as_text=False)

predict.py:

import numpy as np

import tensorflow as tf



model = tf.keras.models.load_model('./xor.h5')



# 0 ^ 0 =  [[0.01974997]]

print('0 ^ 0 = ', model.predict(np.array([[0, 0]])))



# 0 ^ 1 =  [[0.99141496]]

print('0 ^ 1 = ', model.predict(np.array([[0, 1]])))



# 1 ^ 0 =  [[0.9897714]]

print('1 ^ 0 = ', model.predict(np.array([[1, 0]])))



# 1 ^ 1 =  [[0.00406971]]

print('1 ^ 1 = ', model.predict(np.array([[1, 1]])))

opencv-predict.py:

import numpy as np

import cv2 as cv





model = cv.dnn.readNetFromTensorflow('./xor.pb')



# 0 ^ 0 =  [[0.01974997]]

model.setInput(np.array([[0, 0]]), name='dense_input')

print('0 ^ 0 = ', model.forward(outputName='dense_4/Sigmoid'))



# 0 ^ 1 =  [[0.99141496]]

model.setInput(np.array([[0, 1]]), name='dense_input')

print('0 ^ 1 = ', model.forward(outputName='dense_4/Sigmoid'))



# 1 ^ 0 =  [[0.9897714]]

model.setInput(np.array([[1, 0]]), name='dense_input')

print('1 ^ 0 = ', model.forward(outputName='dense_4/Sigmoid'))



# 1 ^ 1 =  [[0.00406971]]

model.setInput(np.array([[1, 1]]), name='dense_input')

print('1 ^ 1 = ', model.forward(outputName='dense_4/Sigmoid'))

predict.cpp:

#include <cstdlib>

#include <iostream>

#include <opencv2/opencv.hpp>



int main(int argc, char **argv)

{

    cv::dnn::Net net;



    net = cv::dnn::readNetFromTensorflow("./xor.pb");



    // 0 ^ 0 = [0.018541215]

    float x0 = { 0, 0 };

    net.setInput(cv::Mat(1, 2, CV_32F, x0), "dense_input");

    std::cout << "0 ^ 0 = " << net.forward("dense_4/Sigmoid") << std::endl;



    // 0 ^ 1 = [0.98295897]

    float x1 = { 0, 1 };

    net.setInput(cv::Mat(1, 2, CV_32F, x1), "dense_input");

    std::cout << "0 ^ 1 = " << net.forward("dense_4/Sigmoid") << std::endl;



    // 1 ^ 0 = [0.98810625]

    float x2 = { 1, 0 };

    net.setInput(cv::Mat(1, 2, CV_32F, x2), "dense_input");

    std::cout << "1 ^ 0 = " << net.forward("dense_4/Sigmoid") << std::endl;



    // 1 ^ 1 = [0.010002014]

    float x3 = { 1, 1 };

    net.setInput(cv::Mat(1, 2, CV_32F, x3), "dense_input");

    std::cout << "1 ^ 1 = " << net.forward("dense_4/Sigmoid") << std::endl;



    return EXIT_SUCCESS;

}

If you want the model only for inference, you should first freeze the graph and then write it as a .pb file. The code snippet looks like this (code borrowed from here):

import tensorflow as tf

from tensorflow.python.framework import graph_util

from tensorflow.python.framework import graph_io

import keras

from keras import backend as K



sess = K.get_session()



constant_graph = graph_util.convert_variables_to_constants(

        sess,

        sess.graph.as_graph_def(),

        ["name_of_the_output_graph_node"])



graph_io.write_graph(constant_graph, "path/to/output/folder", 

                     "output_model_name", as_text=False)

You can do the above using the keras_to_tensorflow tool: https://github.com/amir-abdi/keras_to_tensorflow

The keras_to_tensorflow tool takes care of the above operations, with some extra features for a more diverse solution. Just call it with the correct input arguments (e.g. input_model and output_model flags).

If you want to retrain the model in tensorflow, use the above tool with the output_meta_ckpt flag to export checkpoints and meta graphs.