(model_params)
Train a sketch-rnn model.
Train a sketch-rnn model.
Train a sketch - rnn model .
def trainer(model_params):
  """Train a sketch-rnn model."""
  np.set_printoptions(precision=8, edgeitems=6, linewidth=200, suppress=True)

  tf.logging.info('sketch-rnn')
  tf.logging.info('Hyperparams:')
  tf.logging.info('Loading data files.')
  datasets = load_dataset(FLAGS.data_dir, model_params)

  train_set = datasets[0]
  valid_set = datasets[1]
  test_set = datasets[2]
  model_params = datasets[3]
  eval_model_params = datasets[4]

  reset_graph()
  model = sketch_rnn_model.Model(model_params)
  eval_model = sketch_rnn_model.Model(eval_model_params, reuse=True)

  sess = tf.InteractiveSession()
  sess.run(tf.global_variables_initializer())

  if FLAGS.resume_training:
    load_checkpoint(sess, FLAGS.log_root)

  # Write config file to json file.
  tf.gfile.MakeDirs(FLAGS.log_root)
  with tf.gfile.Open(
      os.path.join(FLAGS.log_root, 'model_config.json'), 'w') as f:
    json.dump(list(model_params.values()), f, indent=True)

  train(sess, model, eval_model, train_set, valid_set, test_set)
def trainer ( model_params ) : np . set_printoptions ( precision = 8 , edgeitems = 6 , linewidth = 200 , suppress = True ) tf . logging . info ( 'sketch-rnn' ) tf . logging . info ( 'Hyperparams:' ) tf . logging . info ( 'Loading data files.' ) datasets = load_dataset ( FLAGS . data_dir , model_params ) train_set = datasets [ 0 ] valid_set = datasets [ 1 ] test_set = datasets [ 2 ] model_params = datasets [ 3 ] eval_model_params = datasets [ 4 ] reset_graph ( ) model = sketch_rnn_model . Model ( model_params ) eval_model = sketch_rnn_model . Model ( eval_model_params , reuse = True ) sess = tf . InteractiveSession ( ) sess . run ( tf . global_variables_initializer ( ) ) if FLAGS . resume_training : load_checkpoint ( sess , FLAGS . log_root ) # Write config file to json file. tf . gfile . MakeDirs ( FLAGS . log_root ) with tf . gfile . Open ( os . path . join ( FLAGS . log_root , 'model_config.json' ) , 'w' ) as f : json . dump ( list ( model_params . values ( ) ) , f , indent = True ) train ( sess , model , eval_model , train_set , valid_set , test_set )