def test_dumpFileArgsError(self): try: ujson.dump([], '') except TypeError: pass else: assert False, 'expected TypeError'
def test_dumpToFileLikeObject(self): class filelike: def __init__(self): self.bytes = '' def write(self, bytes): self.bytes += bytes f = filelike() ujson.dump([1, 2, 3], f) self.assertEquals("[1,2,3]", f.bytes)
def test_dumpToFileLikeObject(self): class filelike: def __init__(self): self.bytes = '' def write(self, bytes): self.bytes += bytes f = filelike() ujson.dump([1, 2, 3], f) self.assertEqual("[1,2,3]", f.bytes)
def main(unused_args): config = SmallConfig() model_path = FLAGS.model output_path = FLAGS.output np.random.seed() # data_loader if FLAGS.num_sample: config.batch_size = FLAGS.num_sample with tf.Graph().as_default(), tf.Session() as session: config.num_steps = 1 model, model_validate = build_model(session, config, model_path) sequence_list = simulate_sequence(session, model_validate) if output_path: ensure_base_dir(output_path) with open(output_path, 'w') as f: json.dump(sequence_list, f) else: json.dumps(sequence_list)
def test_dumpToFile(self): f = StringIO() ujson.dump([1, 2, 3], f) self.assertEquals("[1,2,3]", f.getvalue())
def on_epoch_end(self, epoch, logs={}): """ Args: epoch: logs: """ self.epoch_number += 1 current_val_loss = logs.get("val_loss") current_loss = logs.get("loss") if (self.last_loss - current_loss) > 0.01: current_weights_name = "weights" + str( self.current_model_number) + ".h5" print("loss improved from " + str(self.last_loss) + " to " + str(current_loss) + ", Saving model to " + current_weights_name) self.model.save_weights("models/" + current_weights_name) self.model.save_weights("models/last_weight.h5") self.current_model_number += 1 self.last_loss = current_loss with open("log.txt", "a+") as logfile: logfile.write( "________________________________________________________\n" ) logfile.write("EPOCH =") logfile.write(str(epoch) + "\n") logfile.write("TRAIN_LOSS =") logfile.write(str(current_loss) + "\n") logfile.write("VAL_LOSS =") logfile.write(str(current_val_loss) + "\n") logfile.write( "---------------------------------------------------------\n" ) logfile.write("TRAIN_Age_LOSS =") logfile.write(str(logs.get("age_estimation_loss")) + "\n") logfile.write("TRAIN_GENDER_LOSS =") logfile.write(str(logs.get("gender_probablity_loss")) + "\n") logfile.write( "---------------------------------------------------------\n" ) logfile.write("TRAIN_Age_ACC =") logfile.write(str(logs.get("age_estimation_acc")) + "\n") logfile.write("TRAIN_GENDER_ACC =") logfile.write(str(logs.get("gender_probablity_acc")) + "\n") logfile.write( "---------------------------------------------------------\n" ) logfile.write("VAL_Age_LOSS =") logfile.write(str(logs.get("val_age_estimation_loss")) + "\n") logfile.write("VAL_GENDER_LOSS =") logfile.write( str(logs.get("val_gender_probablity_loss")) + "\n") logfile.write( "---------------------------------------------------------\n" ) logfile.write("VAL_Age_ACC =") logfile.write(str(logs.get("val_age_estimation_acc")) + "\n") logfile.write("VAL_GENDER_ACC =") logfile.write( str(logs.get("val_gender_probablity_acc")) + "\n") logfile.write( "********************************************************\n" ) with open("epoch_number.json", "w+") as json_file: data = {"epoch_number": self.epoch_number} json.dump(data, json_file, indent=4)
def test_dumpToFile(self): f = io.StringIO() ujson.dump([1, 2, 3], f) self.assertEqual("[1,2,3]", f.getvalue())