NameError:name 'create_model' is not defined ....i have tried importing model from keras but it hasnt solved it .how to solve?

Question

I tried creating a model using tensorflow. When I tried executing it shows me

the other files are in this link------- github.com/llSourcell/tensorflow_chatbot

def train():

    enc_train, dec_train=data_utils.prepare_custom_data(
        gConfig['working_directory'])
    train_set = read_data(enc_train,dec_train)

def seq2seq_f(encoder_inputs,decoder_inputs,do_decode):
    return tf.nn.seq2seq.embedding_attention_seq2seq(
        encoder_inputs,decoder_inputs, cell,
        num_encoder_symbols=source_vocab_size,
        num_decoder_symbols=target_vocab_size,
        embedding_size=size,
        output_projection=output_projection,
        feed_previous=do_decode)

with tf.Session(config=config) as sess:
    model = create_model(sess,False)

    while True:
        sess.run(model)

        checkpoint_path = os.path.join(gConfig['working_directory'],'seq2seq.ckpt')
        model.saver.save(sess, checkpoint_path, global_step=model.global_step)

other than this the other python files ive used are in the github link specified in the comments section below

this is the code defining create_model in the execute.py file

def create_model(session, forward_only):

  """Create model and initialize or load parameters"""
  model = seq2seq_model.Seq2SeqModel( gConfig['enc_vocab_size'], gConfig['dec_vocab_size'], _buckets, gConfig['layer_size'], gConfig['num_layers'], gConfig['max_gradient_norm'], gConfig['batch_size'], gConfig['learning_rate'], gConfig['learning_rate_decay_factor'], forward_only=forward_only)

  if 'pretrained_model' in gConfig:
      model.saver.restore(session,gConfig['pretrained_model'])
      return model

  ckpt = tf.train.get_checkpoint_state(gConfig['working_directory'])
  # the checkpoint filename has changed in recent versions of tensorflow
  checkpoint_suffix = ""
  if tf.__version__ > "0.12":
      checkpoint_suffix = ".index"
  if ckpt and tf.gfile.Exists(ckpt.model_checkpoint_path + checkpoint_suffix):
    print("Reading model parameters from %s" % ckpt.model_checkpoint_path)
    model.saver.restore(session, ckpt.model_checkpoint_path)
  else:
    print("Created model with fresh parameters.")
    session.run(tf.initialize_all_variables())
  return model

Answer 1

Okay, it seems like you have copied code but you did not structure it. If create_model() is defined in another file then you have to import it. Have you done that? (ie from file_with_methods import create_model ). You should consider editing your post and adding more of your code, if you want us to help.

Alternative : You could also clone the github repository(that you shared in your comment) and just change whatever you want to change in the execution.py file. This way you can keep the "hierarchy" that the owner uses and you could add your own code where needed.