Python model.NamignizerModel方法代码示例

# 需要导入模块: import model [as 别名]
# 或者: from model import NamignizerModel [as 别名]
def run_epoch(session, m, names, counts, epoch_size, eval_op, verbose=False):
    """Runs the model on the given data for one epoch

    Args:
        session: the tf session holding the model graph
        m: an instance of the NamignizerModel
        names: a set of lowercase names of 26 characters
        counts: a list of the frequency of the above names
        epoch_size: the number of batches to run
        eval_op: whether to change the params or not, and how to do it
    Kwargs:
        verbose: whether to print out state of training during the epoch
    Returns:
        cost: the average cost during the last stage of the epoch
    """
    start_time = time.time()
    costs = 0.0
    iters = 0
    for step, (x, y) in enumerate(data_utils.namignizer_iterator(names, counts,
                                                                 m.batch_size, m.num_steps, epoch_size)):

        cost, _ = session.run([m.cost, eval_op],
                              {m.input_data: x,
                               m.targets: y,
                               m.weights: np.ones(m.batch_size * m.num_steps)})
        costs += cost
        iters += m.num_steps

        if verbose and step % (epoch_size // 10) == 9:
            print("%.3f perplexity: %.3f speed: %.0f lps" %
                  (step * 1.0 / epoch_size, np.exp(costs / iters),
                   iters * m.batch_size / (time.time() - start_time)))

        if step >= epoch_size:
            break

    return np.exp(costs / iters) 

# 需要导入模块: import model [as 别名]
# 或者: from model import NamignizerModel [as 别名]
def train(data_dir, checkpoint_path, config):
    """Trains the model with the given data

    Args:
        data_dir: path to the data for the model (see data_utils for data
            format)
        checkpoint_path: the path to save the trained model checkpoints
        config: one of the above configs that specify the model and how it
            should be run and trained
    Returns:
        None
    """
    # Prepare Name data.
    print("Reading Name data in %s" % data_dir)
    names, counts = data_utils.read_names(data_dir)

    with tf.Graph().as_default(), tf.Session() as session:
        initializer = tf.random_uniform_initializer(-config.init_scale,
                                                    config.init_scale)
        with tf.variable_scope("model", reuse=None, initializer=initializer):
            m = NamignizerModel(is_training=True, config=config)

        tf.global_variables_initializer().run()

        for i in range(config.max_max_epoch):
            lr_decay = config.lr_decay ** max(i - config.max_epoch, 0.0)
            m.assign_lr(session, config.learning_rate * lr_decay)

            print("Epoch: %d Learning rate: %.3f" % (i + 1, session.run(m.lr)))
            train_perplexity = run_epoch(session, m, names, counts, config.epoch_size, m.train_op,
                                         verbose=True)
            print("Epoch: %d Train Perplexity: %.3f" %
                  (i + 1, train_perplexity))

            m.saver.save(session, checkpoint_path, global_step=i) 

# 需要导入模块: import model [as 别名]
# 或者: from model import NamignizerModel [as 别名]
def namignize(names, checkpoint_path, config):
    """Recognizes names and prints the Perplexity of the model for each names
    in the list

    Args:
        names: a list of names in the model format
        checkpoint_path: the path to restore the trained model from, should not
            include the model name, just the path to
        config: one of the above configs that specify the model and how it
            should be run and trained
    Returns:
        None
    """
    with tf.Graph().as_default(), tf.Session() as session:

        with tf.variable_scope("model"):
            m = NamignizerModel(is_training=False, config=config)

        m.saver.restore(session, checkpoint_path)

        for name in names:
            x, y = data_utils.name_to_batch(name, m.batch_size, m.num_steps)

            cost, loss, _ = session.run([m.cost, m.loss, tf.no_op()],
                                  {m.input_data: x,
                                   m.targets: y,
                                   m.weights: np.concatenate((
                                       np.ones(len(name)), np.zeros(m.batch_size * m.num_steps - len(name))))})

            print("Name {} gives us a perplexity of {}".format(
                name, np.exp(cost))) 

# 需要导入模块: import model [as 别名]
# 或者: from model import NamignizerModel [as 别名]
def namignator(checkpoint_path, config):
    """Generates names randomly according to a given model

    Args:
        checkpoint_path: the path to restore the trained model from, should not
            include the model name, just the path to
        config: one of the above configs that specify the model and how it
            should be run and trained
    Returns:
        None
    """
    # mutate the config to become a name generator config
    config.num_steps = 1
    config.batch_size = 1

    with tf.Graph().as_default(), tf.Session() as session:

        with tf.variable_scope("model"):
            m = NamignizerModel(is_training=False, config=config)

        m.saver.restore(session, checkpoint_path)

        activations, final_state, _ = session.run([m.activations, m.final_state, tf.no_op()],
                                                  {m.input_data: np.zeros((1, 1)),
                                                   m.targets: np.zeros((1, 1)),
                                                   m.weights: np.ones(1)})

        # sample from our softmax activations
        next_letter = np.random.choice(27, p=activations[0])
        name = [next_letter]
        while next_letter != 0:
            activations, final_state, _ = session.run([m.activations, m.final_state, tf.no_op()],
                                                      {m.input_data: [[next_letter]],
                                                       m.targets: np.zeros((1, 1)),
                                                       m.initial_state: final_state,
                                                       m.weights: np.ones(1)})

            next_letter = np.random.choice(27, p=activations[0])
            name += [next_letter]

        print(map(lambda x: chr(x + 96), name)) 

# 需要导入模块: import model [as 别名]
# 或者: from model import NamignizerModel [as 别名]
def run_epoch(session, m, names, counts, epoch_size, eval_op, verbose=False):
    """Runs the model on the given data for one epoch

    Args:
        session: the tf session holding the model graph
        m: an instance of the NamignizerModel
        names: a set of lowercase names of 26 characters
        counts: a list of the frequency of the above names
        epoch_size: the number of batches to run
        eval_op: whether to change the params or not, and how to do it
    Kwargs:
        verbose: whether to print out state of training during the epoch
    Returns:
        cost: the average cost during the last stage of the epoch
    """
    start_time = time.time()
    costs = 0.0
    iters = 0
    for step, (x, y) in enumerate(data_utils.namignizer_iterator(names, counts,
                                                                 m.batch_size, m.num_steps, epoch_size)):

        cost, _ = session.run([m.cost, eval_op],
                              {m.input_data: x,
                               m.targets: y,
                               m.initial_state: m.initial_state.eval(),
                               m.weights: np.ones(m.batch_size * m.num_steps)})
        costs += cost
        iters += m.num_steps

        if verbose and step % (epoch_size // 10) == 9:
            print("%.3f perplexity: %.3f speed: %.0f lps" %
                  (step * 1.0 / epoch_size, np.exp(costs / iters),
                   iters * m.batch_size / (time.time() - start_time)))

        if step >= epoch_size:
            break

    return np.exp(costs / iters) 

# 需要导入模块: import model [as 别名]
# 或者: from model import NamignizerModel [as 别名]
def train(data_dir, checkpoint_path, config):
    """Trains the model with the given data

    Args:
        data_dir: path to the data for the model (see data_utils for data
            format)
        checkpoint_path: the path to save the trained model checkpoints
        config: one of the above configs that specify the model and how it
            should be run and trained
    Returns:
        None
    """
    # Prepare Name data.
    print("Reading Name data in %s" % data_dir)
    names, counts = data_utils.read_names(data_dir)

    with tf.Graph().as_default(), tf.Session() as session:
        initializer = tf.random_uniform_initializer(-config.init_scale,
                                                    config.init_scale)
        with tf.variable_scope("model", reuse=None, initializer=initializer):
            m = NamignizerModel(is_training=True, config=config)

        tf.initialize_all_variables().run()

        for i in range(config.max_max_epoch):
            lr_decay = config.lr_decay ** max(i - config.max_epoch, 0.0)
            m.assign_lr(session, config.learning_rate * lr_decay)

            print("Epoch: %d Learning rate: %.3f" % (i + 1, session.run(m.lr)))
            train_perplexity = run_epoch(session, m, names, counts, config.epoch_size, m.train_op,
                                         verbose=True)
            print("Epoch: %d Train Perplexity: %.3f" %
                  (i + 1, train_perplexity))

            m.saver.save(session, checkpoint_path, global_step=i) 

# 需要导入模块: import model [as 别名]
# 或者: from model import NamignizerModel [as 别名]
def namignize(names, checkpoint_path, config):
    """Recognizes names and prints the Perplexity of the model for each names
    in the list

    Args:
        names: a list of names in the model format
        checkpoint_path: the path to restore the trained model from, should not
            include the model name, just the path to
        config: one of the above configs that specify the model and how it
            should be run and trained
    Returns:
        None
    """
    with tf.Graph().as_default(), tf.Session() as session:

        with tf.variable_scope("model"):
            m = NamignizerModel(is_training=False, config=config)

        m.saver.restore(session, checkpoint_path)

        for name in names:
            x, y = data_utils.name_to_batch(name, m.batch_size, m.num_steps)

            cost, loss, _ = session.run([m.cost, m.loss, tf.no_op()],
                                  {m.input_data: x,
                                   m.targets: y,
                                   m.initial_state: m.initial_state.eval(),
                                   m.weights: np.concatenate((
                                       np.ones(len(name)), np.zeros(m.batch_size * m.num_steps - len(name))))})

            print("Name {} gives us a perplexity of {}".format(
                name, np.exp(cost)))