How to convert trained .hdf5 model to a single TensorFlow .pb file

[VViiepan]

Hi! Thank you for your sharing.
I was trying to convert the .hdf5 model to convert .pb file,
(h5_model = load_model(weight_file ,custom_objects={'RoiPoolingConv' :RoiPoolingConv}))
however, when I do that I get the following error:
ValueError: No model found in config file.
Is it related to config.pickle this file?. How do deal with it?

[VViiepan]

This is my convert code:

`
from keras_frcnn.RoiPoolingConv import RoiPoolingConv
from keras.models import load_model
import tensorflow as tf
import os
import os.path as osp
from keras import backend as K

// input dir
input_path = './'
weight_file = 'model_frcnn.hdf5'
weight_file_path = osp.join(input_path ,weight_file)
output_graph_name = "fr" + '.pb'

// convert
def h5_to_pb(h5_model ,output_dir ,model_name ,out_prefix = "output_" ,log_tensorboard = True):
if osp.exists(output_dir) == False:
os.mkdir(output_dir)
out_nodes = []
for i in range(len(h5_model.outputs)):
out_nodes.append(out_prefix + str(i + 1))
tf.identity(h5_model.output[i] ,out_prefix + str(i + 1))
sess = K.get_session()
from tensorflow.python.framework import graph_util ,graph_io
init_graph = sess.graph.as_graph_def()
main_graph = graph_util.convert_variables_to_constants(sess ,init_graph ,out_nodes)
graph_io.write_graph(main_graph ,output_dir ,name = model_name ,as_text = False)
if log_tensorboard:
from tensorflow.python.tools import import_pb_to_tensorboard
import_pb_to_tensorboard.import_to_tensorboard(osp.join(output_dir ,model_name) ,output_dir)

// output dir
output_dir = osp.join(os.getcwd() ,"trans_model")
// load model
h5_model = load_model(weight_file ,custom_objects={'RoiPoolingConv' :RoiPoolingConv})
h5_to_pb(h5_model ,output_dir = output_dir ,model_name = output_graph_name)
print('model saved')`

[SHREYAS290601]

please use indentations