convert keras 2.3.1 model to onnx 1.13.1

keras_to_pb.py

import pickle
import json
import tensorflow as tf


from keras.models import load_model
from keras.preprocessing.sequence import pad_sequences
from keras.models import Sequential
from keras.layers import Dense, Embedding, LSTM
import tensorflow as tf
import keras
from tensorflow.keras.models import Model
import keras.backend as K

# keras==2.3.1   tensorflow==1.15.2  h5py==2.10.0      protobuf==3.6.1  tf2onnx

MODELS_FOLDER = "."


model = load_model(f"{MODELS_FOLDER}/model.hdf5")


K.set_learning_phase(0)

def keras_to_pb(model, output_filename, output_node_names):

   """
   This is the function to convert the Keras model to pb.

   Args:
      model: The Keras model.
      output_filename: The output .pb file name.
      output_node_names: The output nodes of the network. If None, then
      the function gets the last layer name as the output node.
   """

   # Get the names of the input and output nodes.
   in_name = model.layers[0].get_output_at(0).name.split(':')[0]

   if output_node_names is None:
       output_node_names = [model.layers[-1].get_output_at(0).name.split(':')[0]]

   sess = keras.backend.get_session()

   # The TensorFlow freeze_graph expects a comma-separated string of output node names.
   output_node_names_tf = ','.join(output_node_names)

   frozen_graph_def = tf.graph_util.convert_variables_to_constants(
       sess,
       sess.graph_def,
       output_node_names)

   sess.close()
   wkdir = ''
   tf.train.write_graph(frozen_graph_def, wkdir, output_filename, as_text=False)

   return in_name, output_node_names


# Convert the Keras ResNet-50 model to a .pb file
in_tensor_name, out_tensor_names = keras_to_pb(model, "model.pb", None) 
print("in", in_tensor_name)
print("out",out_tensor_names)

setup.md

in python 3.7.9:

pip install keras==2.3.1   tensorflow==1.15.2  h5py==2.10.0      protobuf==3.6.1

run keras_to_pb.py and remember output names then in a python 3.10.x shell run:

pip install tf2onnx
python -m tf2onnx.convert  --input model.pb --inputs Tokenized_Sent_Input:0 --outputs Output_Predictions/Softmax:0 --output model_from_pb.onnx