Python data_utils.read_names() Examples

The following are 13 code examples of data_utils.read_names(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module data_utils , or try the search function .
Example #1
Source File: names.py    From DOTA_models with Apache License 2.0 5 votes vote down vote up 
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)
Example #2
Source File: names.py    From yolo_v2 with Apache License 2.0 5 votes vote down vote up 
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)
Example #3
Source File: names.py    From Gun-Detector with Apache License 2.0 5 votes vote down vote up 
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)
Example #4
Source File: names.py    From Action_Recognition_Zoo with MIT License 5 votes vote down vote up 
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)
Example #5
Source File: names.py    From ECO-pytorch with BSD 2-Clause "Simplified" License 5 votes vote down vote up 
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)
Example #6
Source File: names.py    From hands-detection with MIT License 5 votes vote down vote up 
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)
Example #7
Source File: names.py    From object_detection_kitti with Apache License 2.0 5 votes vote down vote up 
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)
Example #8
Source File: names.py    From object_detection_with_tensorflow with MIT License 5 votes vote down vote up 
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)
Example #9
Source File: names.py    From AI_Reader with Apache License 2.0 5 votes vote down vote up 
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)
Example #10
Source File: names.py    From HumanRecognition with MIT License 5 votes vote down vote up 
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)
Example #11
Source File: names.py    From g-tensorflow-models with Apache License 2.0 5 votes vote down vote up 
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)
Example #12
Source File: names.py    From models with Apache License 2.0 5 votes vote down vote up 
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)
Example #13
Source File: names.py    From multilabel-image-classification-tensorflow with MIT License 5 votes vote down vote up 
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)