def load_env(data_dir, model_dir):
"""Loads environment for inference mode, used in jupyter notebook."""
model_params = sketch_rnn_model.get_default_hparams()
with tf.gfile.Open(os.path.join(model_dir, 'model_config.json'), 'r') as f:
model_config = json.load(f)
model_params.update(model_config)
return load_dataset(data_dir, model_params, inference_mode=True)
def load_env(data_dir, model_dir):
"""Loads environment for inference mode, used in jupyter notebook."""
model_params = sketch_rnn_model.get_default_hparams()
with tf.gfile.Open(os.path.join(model_dir, 'model_config.json'), 'r') as f:
model_config = json.load(f)
model_params.update(model_config)
return load_dataset(data_dir, model_params, inference_mode=True)
def load_model(model_dir):
"""Loads model for inference mode, used in jupyter notebook."""
model_params = sketch_rnn_model.get_default_hparams()
with tf.gfile.Open(os.path.join(model_dir, 'model_config.json'), 'r') as f:
model_params.parse_json(f.read())
model_params.batch_size = 1 # only sample one at a time
eval_model_params = sketch_rnn_model.copy_hparams(model_params)
eval_model_params.use_input_dropout = 0
eval_model_params.use_recurrent_dropout = 0
eval_model_params.use_output_dropout = 0
eval_model_params.is_training = 0
sample_model_params = sketch_rnn_model.copy_hparams(eval_model_params)
sample_model_params.max_seq_len = 1 # sample one point at a time
return [model_params, eval_model_params, sample_model_params]
def load_model(model_dir):
"""Loads model for inference mode, used in jupyter notebook."""
model_params = sketch_rnn_model.get_default_hparams()
with tf.gfile.Open(os.path.join(model_dir, 'model_config.json'), 'r') as f:
model_params.parse_json(f.read())
model_params.batch_size = 1 # only sample one at a time
eval_model_params = sketch_rnn_model.copy_hparams(model_params)
eval_model_params.use_input_dropout = 0
eval_model_params.use_recurrent_dropout = 0
eval_model_params.use_output_dropout = 0
eval_model_params.is_training = 0
sample_model_params = sketch_rnn_model.copy_hparams(eval_model_params)
sample_model_params.max_seq_len = 1 # sample one point at a time
return [model_params, eval_model_params, sample_model_params]
def main(unused_argv):
"""Load model params, save config file and start trainer."""
model_params = sketch_rnn_model.get_default_hparams()
if FLAGS.hparams:
model_params.parse(FLAGS.hparams)
trainer(model_params)
def main(unused_argv):
model_params = sketch_rnn_model.get_default_hparams()
if FLAGS.hparams:
model_params.parse(FLAGS.hparams)
np.set_printoptions(precision=8, edgeitems=6, linewidth=200, suppress=True)
tf.logging.info('sketch-rnn')
#tf.logging.info('Hyperparams:')
#print(model_params.values())
#for key, val in six.iteritems(model_params.values()):
# tf.logging.info('%s = %s', key, str(val))
#tf.logging.info('Loading data files.')
datasets = load_dataset(FLAGS.data_dir, model_params)
#parse train, valid and
train = datasets[0].strokes
valid = datasets[1].strokes
test = datasets[2].strokes
print("\n\ntrain length = %d, valid_length = %d, test length = %d\n\n" %
(len(train), len(valid), len(test)))
total_data_size = len(train) + len(valid) + len(test)
#train length = 164888, valid_length = 2500, test length = 2500
arr = np.arange(total_data_size)
np.random.shuffle(arr)
#replace data
#for i in range(a):
#datasets[0].strokes[i] = -100;
#for j in range(4):
#print(datasets[0].strokes[j])
retrain_times = 2
for i in range(retrain_times):
result = []
result = trainer(model_params, datasets)
#num_of_result = len(result)
# hostname = "54.82.94.146"
# port = 80
# check = 0
# for i in result:
# x_array,y_array = get_sketch(i)
# #print(x_array)
# #rint(y_array)
# r = requests.post("http://{}:{}/data".format(hostname,port),
# data = json.dumps({"data":{"x_data":x_array,"y_data":y_array,"id":i,"check":check}}))
print("#########final_result###########")
print(result)
print("result number [0]")
print(result[0])
print("#########final_result###########")
IP = ""
datasets[0].strokes[i] = result[0]
for j in range(4):
print(datasets[0].strokes[j])
def main(unused_argv):
"""Load model params, save config file and start trainer."""
model_params = sketch_rnn_model.get_default_hparams()
if FLAGS.hparams:
model_params.parse(FLAGS.hparams)
trainer(model_params)
