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)