def build_acoustic_training_rnn(is_mpi,is_chief, hyper_params, prog_params, train_set, test_set): model = AcousticModel(hyper_params["num_layers"], hyper_params["hidden_size"], hyper_params["batch_size"], hyper_params["max_input_seq_length"], hyper_params["max_target_seq_length"], hyper_params["input_dim"], hyper_params["batch_normalization"], hyper_params["char_map_length"]) # Create a Dataset from the train_set and the test_set train_dataset = model.build_dataset(train_set, hyper_params["batch_size"], hyper_params["max_input_seq_length"], hyper_params["max_target_seq_length"], hyper_params["signal_processing"], hyper_params["char_map"]) #train_dataset = train_dataset.shuffle(10,reshuffle_each_iteration=True) v_iterator = None if test_set is []: t_iterator = model.add_dataset_input(train_dataset) else: test_dataset = model.build_dataset(test_set, hyper_params["batch_size"], hyper_params["max_input_seq_length"], hyper_params["max_target_seq_length"], hyper_params["signal_processing"], hyper_params["char_map"]) # Build the input stream from the different datasets t_iterator, v_iterator = model.add_datasets_input(train_dataset, test_dataset) # Create the model #tensorboard_dir model.create_training_rnn(is_mpi, hyper_params["dropout_input_keep_prob"], hyper_params["dropout_output_keep_prob"], hyper_params["grad_clip"], hyper_params["learning_rate"], hyper_params["lr_decay_factor"], use_iterator=True,is_sync=prog_params['is_sync'],is_chief=is_chief) if is_chief: model.add_tensorboard(prog_params["train_dir"], prog_params["timeline"]) return model, t_iterator, v_iterator
def build_acoustic_training_rnn(is_chief, is_ditributed, sess, hyper_params, prog_params, train_set, test_set): model = AcousticModel( hyper_params["num_layers"], hyper_params["hidden_size"], hyper_params["batch_size"], hyper_params["max_input_seq_length"], hyper_params["max_target_seq_length"], hyper_params["input_dim"], hyper_params["batch_normalization"], hyper_params["char_map_length"]) # Create a Dataset from the train_set and the test_set train_dataset = model.build_dataset(train_set, hyper_params["batch_size"], hyper_params["max_input_seq_length"], hyper_params["max_target_seq_length"], hyper_params["signal_processing"], hyper_params["char_map"]) train_dataset = train_dataset.shuffle(10, reshuffle_each_iteration=True) v_iterator = None if test_set is []: t_iterator = model.add_dataset_input(train_dataset) else: test_dataset = model.build_dataset( test_set, hyper_params["batch_size"], hyper_params["max_input_seq_length"], hyper_params["max_target_seq_length"], hyper_params["signal_processing"], hyper_params["char_map"]) # Build the input stream from the different datasets t_iterator, v_iterator = model.add_datasets_input( train_dataset, test_dataset) # Create the model #tensorboard_dir model.create_training_rnn(is_chief, is_ditributed, hyper_params["dropout_input_keep_prob"], hyper_params["dropout_output_keep_prob"], hyper_params["grad_clip"], hyper_params["learning_rate"], hyper_params["lr_decay_factor"], use_iterator=True) model.add_tensorboard(sess, prog_params["train_dir"], prog_params["timeline"]) sv = None if is_ditributed: init_op = tf.global_variables_initializer() sv = tf.train.Supervisor(is_chief=is_chief, logdir=prog_params["train_dir"], init_op=init_op, recovery_wait_secs=1, summary_op=None, global_step=model.global_step) model.supervisor = sv else: model.initialize(sess) model.restore(sess, prog_params["train_dir"]) # Override the learning rate if given on the command line if prog_params["learn_rate"] is not None: model.set_learning_rate(sess, prog_params["learn_rate"]) return sv, model, t_iterator, v_iterator
def build_acoustic_training_rnn(sess, hyper_params, prog_params, train_set, test_set): model = AcousticModel( hyper_params["num_layers"], hyper_params["hidden_size"], hyper_params["batch_size"], hyper_params["max_input_seq_length"], hyper_params["max_target_seq_length"], hyper_params["input_dim"], hyper_params["batch_normalization"], hyper_params["char_map_length"]) # Create a Dataset from the train_set and the test_set train_dataset = model.build_dataset(train_set, hyper_params["batch_size"], hyper_params["max_input_seq_length"], hyper_params["max_target_seq_length"], hyper_params["signal_processing"], hyper_params["char_map"]) v_iterator = None if test_set is []: t_iterator = model.add_dataset_input(train_dataset) sess.run(t_iterator.initializer) else: test_dataset = model.build_dataset( test_set, hyper_params["batch_size"], hyper_params["max_input_seq_length"], hyper_params["max_target_seq_length"], hyper_params["signal_processing"], hyper_params["char_map"]) # Build the input stream from the different datasets t_iterator, v_iterator = model.add_datasets_input( train_dataset, test_dataset) sess.run(t_iterator.initializer) sess.run(v_iterator.initializer) # Create the model model.create_training_rnn(hyper_params["dropout_input_keep_prob"], hyper_params["dropout_output_keep_prob"], hyper_params["grad_clip"], hyper_params["learning_rate"], hyper_params["lr_decay_factor"], use_iterator=True) model.add_tensorboard(sess, hyper_params["tensorboard_dir"], prog_params["tb_name"], prog_params["timeline"]) model.initialize(sess) model.restore(sess, hyper_params["checkpoint_dir"] + "/acoustic/") # Override the learning rate if given on the command line if prog_params["learn_rate"] is not None: model.set_learning_rate(sess, prog_params["learn_rate"]) return model, t_iterator, v_iterator
def build_acoustic_training_rnn(sess, hyper_params, prog_params, train_set, test_set): model = AcousticModel(hyper_params["num_layers"], hyper_params["hidden_size"], hyper_params["batch_size"], hyper_params["max_input_seq_length"], hyper_params["max_target_seq_length"], hyper_params["input_dim"], hyper_params["batch_normalization"], hyper_params["char_map_length"]) # Create a Dataset from the train_set and the test_set train_dataset = model.build_dataset(train_set, hyper_params["batch_size"], hyper_params["max_input_seq_length"], hyper_params["max_target_seq_length"], hyper_params["signal_processing"], hyper_params["char_map"]) v_iterator = None if test_set is []: t_iterator = model.add_dataset_input(train_dataset) sess.run(t_iterator.initializer) else: test_dataset = model.build_dataset(test_set, hyper_params["batch_size"], hyper_params["max_input_seq_length"], hyper_params["max_target_seq_length"], hyper_params["signal_processing"], hyper_params["char_map"]) # Build the input stream from the different datasets t_iterator, v_iterator = model.add_datasets_input(train_dataset, test_dataset) sess.run(t_iterator.initializer) sess.run(v_iterator.initializer) # Create the model model.create_training_rnn(hyper_params["dropout_input_keep_prob"], hyper_params["dropout_output_keep_prob"], hyper_params["grad_clip"], hyper_params["learning_rate"], hyper_params["lr_decay_factor"], use_iterator=True) model.add_tensorboard(sess, hyper_params["tensorboard_dir"], prog_params["tb_name"], prog_params["timeline"]) model.initialize(sess) model.restore(sess, hyper_params["checkpoint_dir"] + "/acoustic/") # Override the learning rate if given on the command line if prog_params["learn_rate"] is not None: model.set_learning_rate(sess, prog_params["learn_rate"]) return model, t_iterator, v_iterator