def slice(self,x, turn):
    """ Define a tensor slice function
    """
    return x[:, turn, :, :]
  def reduce_mean(self, X):
    return K.mean(X, axis=-1)

from keras.models import load_model
import tensorflow as tf
import os, sys
from keras import backend as K
from tensorflow.python.framework import graph_util, graph_io

def h5_to_pb(h5_weight_path, output_dir, out_prefix="output_", log_tensorboard=True):
  if not os.path.exists(output_dir):
    os.mkdir(output_dir)
  h5_model = build_model()
  h5_model.load_weights(h5_weight_path)
  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))
  model_name = os.path.splitext(os.path.split(h5_weight_path)[-1])[0] + '.pb'
  sess = K.get_session()
  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(os.path.join(output_dir, model_name), output_dir)

def build_model():
  inputs = Input(shape=(784,), name='input_img')
  x = Dense(64, activation='relu')(inputs)
  x = Dense(64, activation='relu')(x)
  y = Dense(10, activation='softmax')(x)
  h5_model = Model(inputs=inputs, outputs=y)
  return h5_model

if __name__ == '__main__':
  if len(sys.argv) == 3:
    # usage: python3 h5_to_pb.py h5_weight_path output_dir
    h5_to_pb(h5_weight_path=sys.argv[1], output_dir=sys.argv[2])