Python data_utils.namignizer_iterator() Examples

The following are 13 code examples of data_utils.namignizer_iterator(). 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 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)
Example #2
Source File: names.py    From yolo_v2 with Apache License 2.0 5 votes vote down vote up 
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)
Example #3
Source File: names.py    From Gun-Detector with Apache License 2.0 5 votes vote down vote up 
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)
Example #4
Source File: names.py    From Action_Recognition_Zoo with MIT License 5 votes vote down vote up 
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)
Example #5
Source File: names.py    From ECO-pytorch with BSD 2-Clause "Simplified" License 5 votes vote down vote up 
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)
Example #6
Source File: names.py    From hands-detection with MIT License 5 votes vote down vote up 
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)
Example #7
Source File: names.py    From object_detection_kitti with Apache License 2.0 5 votes vote down vote up 
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)
Example #8
Source File: names.py    From object_detection_with_tensorflow with MIT License 5 votes vote down vote up 
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)
Example #9
Source File: names.py    From AI_Reader with Apache License 2.0 5 votes vote down vote up 
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)
Example #10
Source File: names.py    From HumanRecognition with MIT License 5 votes vote down vote up 
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)
Example #11
Source File: names.py    From g-tensorflow-models with Apache License 2.0 5 votes vote down vote up 
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)
Example #12
Source File: names.py    From models with Apache License 2.0 5 votes vote down vote up 
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)
Example #13
Source File: names.py    From multilabel-image-classification-tensorflow with MIT License 5 votes vote down vote up 
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)