Python summary.scalar方法代碼示例

# 需要導入模塊: from tensorflow.python.summary import summary [as 別名]
# 或者: from tensorflow.python.summary.summary import scalar [as 別名]
def add_gradients_summaries(grads_and_vars):
  """Add summaries to gradients.

  Args:
    grads_and_vars: A list of gradient to variable pairs (tuples).

  Returns:
    The list of created summaries.
  """
  summaries = []
  for grad, var in grads_and_vars:
    if grad is not None:
      if isinstance(grad, ops.IndexedSlices):
        grad_values = grad.values
      else:
        grad_values = grad
      summaries.append(
          summary.histogram(var.op.name + '/gradient', grad_values))
      summaries.append(
          summary.scalar(var.op.name + '/gradient_norm',
                         clip_ops.global_norm([grad_values])))
    else:
      logging.info('Var %s has no gradient', var.op.name)

  return summaries 

# 需要導入模塊: from tensorflow.python.summary import summary [as 別名]
# 或者: from tensorflow.python.summary.summary import scalar [as 別名]
def _add_scalar_summary(tensor, tag=None):
  """Add a scalar summary operation for the tensor.

  Args:
    tensor: The tensor to summarize.
    tag: The tag to use, if None then use tensor's op's name.

  Returns:
    The created histogram summary.

  Raises:
    ValueError: If the tag is already in use or the rank is not 0.
  """
  tensor.get_shape().assert_has_rank(0)
  tag = tag or '%s_summary' % tensor.op.name
  return summary.scalar(tag, tensor) 

# 需要導入模塊: from tensorflow.python.summary import summary [as 別名]
# 或者: from tensorflow.python.summary.summary import scalar [as 別名]
def add_gradients_summaries(grads_and_vars):
  """Add summaries to gradients.

  Args:
    grads_and_vars: A list of gradient to variable pairs (tuples).

  Returns:
    The list of created summaries.
  """
  summaries = []
  for grad, var in grads_and_vars:
    if grad is not None:
      if isinstance(grad, ops.IndexedSlices):
        grad_values = grad.values
      else:
        grad_values = grad
      summaries.append(
          summary.histogram(var.op.name + '_gradient', grad_values))
      summaries.append(
          summary.scalar(var.op.name + '_gradient_norm',
                         clip_ops.global_norm([grad_values])))
    else:
      logging.info('Var %s has no gradient', var.op.name)

  return summaries 

# 需要導入模塊: from tensorflow.python.summary import summary [as 別名]
# 或者: from tensorflow.python.summary.summary import scalar [as 別名]
def add_gradients_summaries(grads_and_vars):
  """Add summaries to gradients.
  Args:
    grads_and_vars: A list of gradient to variable pairs (tuples).
  Returns:
    The list of created summaries.
  """
  summaries = []
  for grad, var in grads_and_vars:
    if grad is not None:
      if isinstance(grad, ops.IndexedSlices):
        grad_values = grad.values
      else:
        grad_values = grad
      summaries.append(
          summary.histogram(var.op.name + '/gradient', grad_values))
      summaries.append(
          summary.scalar(var.op.name + '/gradient_norm',
                         clip_ops.global_norm([grad_values])))
    else:
      logging.info('Var %s has no gradient', var.op.name)

  return summaries 

# 需要導入模塊: from tensorflow.python.summary import summary [as 別名]
# 或者: from tensorflow.python.summary.summary import scalar [as 別名]
def add_scalar_summary(tensor, name=None, prefix=None, print_summary=False):
  """Adds a scalar summary for the given tensor.

  Args:
    tensor: a variable or op tensor.
    name: the optional name for the summary.
    prefix: An optional prefix for the summary names.
    print_summary: If `True`, the summary is printed to stdout when the summary
      is computed.

  Returns:
    A scalar `Tensor` of type `string` whose contents are the serialized
    `Summary` protocol buffer.
  """
  collections = [] if print_summary else None
  summary_name = _get_summary_name(tensor, name, prefix)

  # If print_summary, then we need to make sure that this call doesn't add the
  # non-printing op to the collection. We'll add it to the collection later.
  op = summary.scalar(
      name=summary_name, tensor=tensor, collections=collections)
  if print_summary:
    op = logging_ops.Print(op, [tensor], summary_name)
    ops.add_to_collection(ops.GraphKeys.SUMMARIES, op)
  return op 

# 需要導入模塊: from tensorflow.python.summary import summary [as 別名]
# 或者: from tensorflow.python.summary.summary import scalar [as 別名]
def add_zero_fraction_summary(tensor, name=None, prefix=None,
                              print_summary=False):
  """Adds a summary for the percentage of zero values in the given tensor.

  Args:
    tensor: a variable or op tensor.
    name: the optional name for the summary.
    prefix: An optional prefix for the summary names.
    print_summary: If `True`, the summary is printed to stdout when the summary
      is computed.

  Returns:
    A scalar `Tensor` of type `string` whose contents are the serialized
    `Summary` protocol buffer.
  """
  name = _get_summary_name(tensor, name, prefix, 'Fraction_of_Zero_Values')
  tensor = nn.zero_fraction(tensor)
  return add_scalar_summary(tensor, name, print_summary=print_summary) 

# 需要導入模塊: from tensorflow.python.summary import summary [as 別名]
# 或者: from tensorflow.python.summary.summary import scalar [as 別名]
def testTrainWithNoneAsLogdirWhenUsingSummariesRaisesError(self):
    with ops.Graph().as_default():
      random_seed.set_random_seed(0)
      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
      tf_labels = tf.constant(self._labels, dtype=tf.float32)

      tf_predictions = LogisticClassifier(tf_inputs)
      loss_ops.log_loss(tf_labels, tf_predictions)
      total_loss = loss_ops.get_total_loss()
      summary.scalar('total_loss', total_loss)

      optimizer = gradient_descent.GradientDescentOptimizer(learning_rate=1.0)

      train_op = learning.create_train_op(total_loss, optimizer)
      summary_op = summary.merge_all()

      with self.assertRaises(ValueError):
        learning.train(
            train_op, None, number_of_steps=300, summary_op=summary_op) 

# 需要導入模塊: from tensorflow.python.summary import summary [as 別名]
# 或者: from tensorflow.python.summary.summary import scalar [as 別名]
def create_estimator_spec(
      self, features, mode, logits, labels=None, train_op_fn=None):
    """Returns `EstimatorSpec` that a model_fn can return.

    Please note that,
    + All args must be passed via name.

    Args:
      features: Input `dict` of `Tensor` objects.
      mode: Estimator's `ModeKeys`.
      logits: logits `Tensor` to be used by the head.
      labels: Labels `Tensor`, or `dict` of same.
      train_op_fn: Function that takes a scalar loss `Tensor` and returns an op
          to optimize the model with the loss. This is used in TRAIN mode and
          must not be None. None is allowed in other modes. If you want to
          optimize loss yourself you can pass `no_op_train_fn` and then use
          EstimatorSpec.loss to compute and apply gradients.

    Returns:
      `EstimatorSpec`.
    """
    raise NotImplementedError('Calling an abstract method.') 

# 需要導入模塊: from tensorflow.python.summary import summary [as 別名]
# 或者: from tensorflow.python.summary.summary import scalar [as 別名]
def summarize_tensor(tensor, tag=None):
  """Summarize a tensor using a suitable summary type.

  This function adds a summary op for `tensor`. The type of summary depends on
  the shape of `tensor`. For scalars, a `scalar_summary` is created, for all
  other tensors, `histogram_summary` is used.

  Args:
    tensor: The tensor to summarize
    tag: The tag to use, if None then use tensor's op's name.

  Returns:
    The summary op created or None for string tensors.
  """
  # Skips string tensors and boolean tensors (not handled by the summaries).
  if (tensor.dtype.is_compatible_with(dtypes.string) or
      tensor.dtype.base_dtype == dtypes.bool):
    return None

  if tensor.get_shape().ndims == 0:
    # For scalars, use a scalar summary.
    return _add_scalar_summary(tensor, tag)
  else:
    # We may land in here if the rank is still unknown. The histogram won't
    # hurt if this ends up being a scalar.
    return _add_histogram_summary(tensor, tag) 

# 需要導入模塊: from tensorflow.python.summary import summary [as 別名]
# 或者: from tensorflow.python.summary.summary import scalar [as 別名]
def range_input_producer(limit, num_epochs=None, shuffle=True, seed=None,
                         capacity=32, shared_name=None, name=None):
  """Produces the integers from 0 to limit-1 in a queue.

  Note: if `num_epochs` is not `None`, this function creates local counter
  `epochs`. Use `local_variables_initializer()` to initialize local variables.

  Args:
    limit: An int32 scalar tensor.
    num_epochs: An integer (optional). If specified, `range_input_producer`
      produces each integer `num_epochs` times before generating an
      OutOfRange error. If not specified, `range_input_producer` can cycle
      through the integers an unlimited number of times.
    shuffle: Boolean. If true, the integers are randomly shuffled within each
      epoch.
    seed: An integer (optional). Seed used if shuffle == True.
    capacity: An integer. Sets the queue capacity.
    shared_name: (optional). If set, this queue will be shared under the given
      name across multiple sessions.
    name: A name for the operations (optional).

  Returns:
    A Queue with the output integers.  A `QueueRunner` for the Queue
    is added to the current `Graph`'s `QUEUE_RUNNER` collection.
  """
  with ops.name_scope(name, "input_producer", [limit]) as name:
    range_tensor = math_ops.range(limit)
    return input_producer(
        range_tensor, [], num_epochs, shuffle, seed, capacity,
        shared_name, "fraction_of_%d_full" % capacity, name) 

# 需要導入模塊: from tensorflow.python.summary import summary [as 別名]
# 或者: from tensorflow.python.summary.summary import scalar [as 別名]
def _batch(tensors, batch_size, keep_input, num_threads=1, capacity=32,
           enqueue_many=False, shapes=None, dynamic_pad=False,
           allow_smaller_final_batch=False, shared_name=None,
           name=None):
  """Helper function for `batch` and `maybe_batch`."""
  tensor_list = _as_tensor_list(tensors)
  with ops.name_scope(name, "batch", list(tensor_list) + [keep_input]) as name:
    tensor_list = _validate(tensor_list)
    keep_input = _validate_keep_input(keep_input, enqueue_many)
    (tensor_list, sparse_info) = _store_sparse_tensors(
        tensor_list, enqueue_many, keep_input)
    types = _dtypes([tensor_list])
    shapes = _shapes([tensor_list], shapes, enqueue_many)
    # TODO(josh11b,mrry): Switch to BatchQueue once it is written.
    queue = _which_queue(dynamic_pad)(
        capacity=capacity, dtypes=types, shapes=shapes, shared_name=shared_name)
    _enqueue(queue, tensor_list, num_threads, enqueue_many, keep_input)
    summary.scalar("fraction_of_%d_full" % capacity,
                   math_ops.cast(queue.size(), dtypes.float32) *
                   (1. / capacity))

    if allow_smaller_final_batch:
      dequeued = queue.dequeue_up_to(batch_size, name=name)
    else:
      dequeued = queue.dequeue_many(batch_size, name=name)
    dequeued = _restore_sparse_tensors(dequeued, sparse_info)
    return _as_original_type(tensors, dequeued)


# TODO(josh11b): Add a thread_multiplier or num_threads (that has to be
# a multiple of len(tensor_list_list)?) parameter, to address the use
# case where you want more parallelism than you can support different
# readers (either because you don't have that many files or can't
# read that many files in parallel due to the number of seeks required).
# Once this is done, batch() can be written as a call to batch_join(). 

# 需要導入模塊: from tensorflow.python.summary import summary [as 別名]
# 或者: from tensorflow.python.summary.summary import scalar [as 別名]
def _batch_join(tensors_list, batch_size, keep_input, capacity=32,
                enqueue_many=False, shapes=None, dynamic_pad=False,
                allow_smaller_final_batch=False, shared_name=None, name=None):
  """Helper function for `batch_join` and `maybe_batch_join`."""
  tensor_list_list = _as_tensor_list_list(tensors_list)
  with ops.name_scope(name, "batch_join",
                      _flatten(tensor_list_list) + [keep_input]) as name:
    tensor_list_list = _validate_join(tensor_list_list)
    keep_input = _validate_keep_input(keep_input, enqueue_many)
    tensor_list_list, sparse_info = _store_sparse_tensors_join(
        tensor_list_list, enqueue_many, keep_input)
    types = _dtypes(tensor_list_list)
    shapes = _shapes(tensor_list_list, shapes, enqueue_many)
    # TODO(josh11b,mrry): Switch to BatchQueue once it is written.
    queue = _which_queue(dynamic_pad)(
        capacity=capacity, dtypes=types, shapes=shapes, shared_name=shared_name)
    _enqueue_join(queue, tensor_list_list, enqueue_many, keep_input)
    summary.scalar("fraction_of_%d_full" % capacity,
                   math_ops.cast(queue.size(), dtypes.float32) *
                   (1. / capacity))

    if allow_smaller_final_batch:
      dequeued = queue.dequeue_up_to(batch_size, name=name)
    else:
      dequeued = queue.dequeue_many(batch_size, name=name)
    dequeued = _restore_sparse_tensors(dequeued, sparse_info)
    # tensors_list was validated to not be empty.
    return _as_original_type(tensors_list[0], dequeued) 

# 需要導入模塊: from tensorflow.python.summary import summary [as 別名]
# 或者: from tensorflow.python.summary.summary import scalar [as 別名]
def _shuffle_batch(tensors, batch_size, capacity, min_after_dequeue,
                   keep_input, num_threads=1, seed=None, enqueue_many=False,
                   shapes=None, allow_smaller_final_batch=False,
                   shared_name=None, name=None):
  """Helper function for `shuffle_batch` and `maybe_shuffle_batch`."""
  tensor_list = _as_tensor_list(tensors)
  with ops.name_scope(name, "shuffle_batch",
                      list(tensor_list) + [keep_input]) as name:
    tensor_list = _validate(tensor_list)
    keep_input = _validate_keep_input(keep_input, enqueue_many)
    tensor_list, sparse_info = _store_sparse_tensors(
        tensor_list, enqueue_many, keep_input)
    types = _dtypes([tensor_list])
    shapes = _shapes([tensor_list], shapes, enqueue_many)
    queue = data_flow_ops.RandomShuffleQueue(
        capacity=capacity, min_after_dequeue=min_after_dequeue, seed=seed,
        dtypes=types, shapes=shapes, shared_name=shared_name)
    _enqueue(queue, tensor_list, num_threads, enqueue_many, keep_input)
    full = (math_ops.cast(math_ops.maximum(0, queue.size() - min_after_dequeue),
                          dtypes.float32) *
            (1. / (capacity - min_after_dequeue)))
    # Note that name contains a '/' at the end so we intentionally do not place
    # a '/' after %s below.
    summary_name = (
        "fraction_over_%d_of_%d_full" %
        (min_after_dequeue, capacity - min_after_dequeue))
    summary.scalar(summary_name, full)

    if allow_smaller_final_batch:
      dequeued = queue.dequeue_up_to(batch_size, name=name)
    else:
      dequeued = queue.dequeue_many(batch_size, name=name)
    dequeued = _restore_sparse_tensors(dequeued, sparse_info)
    return _as_original_type(tensors, dequeued) 

# 需要導入模塊: from tensorflow.python.summary import summary [as 別名]
# 或者: from tensorflow.python.summary.summary import scalar [as 別名]
def _shuffle_batch_join(tensors_list, batch_size, capacity,
                        min_after_dequeue, keep_input, seed=None,
                        enqueue_many=False, shapes=None,
                        allow_smaller_final_batch=False, shared_name=None,
                        name=None):
  """Helper function for `shuffle_batch_join` and `maybe_shuffle_batch_join`."""
  tensor_list_list = _as_tensor_list_list(tensors_list)
  with ops.name_scope(name, "shuffle_batch_join",
                      _flatten(tensor_list_list) + [keep_input]) as name:
    tensor_list_list = _validate_join(tensor_list_list)
    keep_input = _validate_keep_input(keep_input, enqueue_many)
    tensor_list_list, sparse_info = _store_sparse_tensors_join(
        tensor_list_list, enqueue_many, keep_input)
    types = _dtypes(tensor_list_list)
    shapes = _shapes(tensor_list_list, shapes, enqueue_many)
    queue = data_flow_ops.RandomShuffleQueue(
        capacity=capacity, min_after_dequeue=min_after_dequeue, seed=seed,
        dtypes=types, shapes=shapes, shared_name=shared_name)
    _enqueue_join(queue, tensor_list_list, enqueue_many, keep_input)
    full = (math_ops.cast(math_ops.maximum(0, queue.size() - min_after_dequeue),
                          dtypes.float32) *
            (1. / (capacity - min_after_dequeue)))
    # Note that name contains a '/' at the end so we intentionally do not place
    # a '/' after %s below.
    summary_name = (
        "fraction_over_%d_of_%d_full" %
        (min_after_dequeue, capacity - min_after_dequeue))
    summary.scalar(summary_name, full)

    if allow_smaller_final_batch:
      dequeued = queue.dequeue_up_to(batch_size, name=name)
    else:
      dequeued = queue.dequeue_many(batch_size, name=name)
    dequeued = _restore_sparse_tensors(dequeued, sparse_info)
    # tensors_list was validated to not be empty.
    return _as_original_type(tensors_list[0], dequeued)

# Batching functions ---------------------------------------------------------- 

# 需要導入模塊: from tensorflow.python.summary import summary [as 別名]
# 或者: from tensorflow.python.summary.summary import scalar [as 別名]
def _add_hidden_layer_summary(self, value, tag):
    # TODO(zakaria): Move this code to tf.learn and add test.
    summary.scalar("%s/fraction_of_zero_values" % tag, nn.zero_fraction(value))
    summary.histogram("%s/activation" % tag, value)