Python ops.op_scope函数代码示例

def rnn_decoder(decoder_inputs, initial_state, cell, scope=None):
  """RNN Decoder that creates training and sampling sub-graphs.

  Args:
    decoder_inputs: Inputs for decoder, list of tensors.
      This is used only in training sub-graph.
    initial_state: Initial state for the decoder.
    cell: RNN cell to use for decoder.
    scope: Scope to use, if None new will be produced.

  Returns:
    List of tensors for outputs and states for training and sampling sub-graphs.
  """
  with vs.variable_scope(scope or "dnn_decoder"):
    states, sampling_states = [initial_state], [initial_state]
    outputs, sampling_outputs = [], []
    with ops.op_scope([decoder_inputs, initial_state], "training"):
      for i, inp in enumerate(decoder_inputs):
        if i > 0:
          vs.get_variable_scope().reuse_variables()
        output, new_state = cell(inp, states[-1])
        outputs.append(output)
        states.append(new_state)
    with ops.op_scope([initial_state], "sampling"):
      for i, _ in enumerate(decoder_inputs):
        if i == 0:
          sampling_outputs.append(outputs[i])
          sampling_states.append(states[i])
        else:
          sampling_output, sampling_state = cell(sampling_outputs[-1],
                                                 sampling_states[-1])
          sampling_outputs.append(sampling_output)
          sampling_states.append(sampling_state)
  return outputs, states, sampling_outputs, sampling_states

  def __init__(self, example_indices, feature_indices, feature_values):
    """Creates a `SparseFeatureColumn` representation.

    Args:
      example_indices: A 1-D int64 tensor of shape `[N]`. Also, accepts
      python lists, or numpy arrays.
      feature_indices: A 1-D int64 tensor of shape `[N]`. Also, accepts
      python lists, or numpy arrays.
      feature_values: An optional 1-D tensor float tensor of shape `[N]`. Also,
      accepts python lists, or numpy arrays.

    Returns:
      A `SparseFeatureColumn`
    """
    with op_scope([example_indices, feature_indices], None,
                  'SparseFeatureColumn'):
      self._example_indices = convert_to_tensor(example_indices,
                                                name='example_indices',
                                                dtype=dtypes.int64)
      self._feature_indices = convert_to_tensor(feature_indices,
                                                name='feature_indices',
                                                dtype=dtypes.int64)
    self._feature_values = None
    if feature_values is not None:
      with op_scope([feature_values], None, 'SparseFeatureColumn'):
        self._feature_values = convert_to_tensor(feature_values,
                                                 name='feature_values',
                                                 dtype=dtypes.float32)

 def _testGraphElements(self, graph_elements):
   scope_name = "my_scope"
   with ops.op_scope(graph_elements, scope_name) as scope:
     self.assertEqual("%s/" % scope_name, scope)
     self.assertEqual(graph_elements[0].graph, ops.get_default_graph())
   g1 = ops.Graph()
   c = g1.create_op("c", [], [dtypes.float32])
   with self.assertRaises(ValueError):
     with ops.op_scope(graph_elements + [c], scope_name):
       pass

 def testEmptyScopeName(self):
   g0 = ops.Graph()
   a = g0.create_op("a", [], [dtypes.float32])
   b = g0.create_op("b", [], [dtypes.float32])
   with ops.op_scope([a, b], "") as scope:
     self.assertEqual("", scope)
     self.assertEqual(g0, ops.get_default_graph())
   with ops.op_scope([a, b], "", "my_default_scope") as scope:
     self.assertEqual("", scope)
     self.assertEqual(g0, ops.get_default_graph())

 def testNoScopeName(self):
   g0 = ops.Graph()
   values = [
       g0.create_op("a", [], [dtypes.float32]),
       g0.create_op("b", [], [dtypes.float32])]
   with self.assertRaises(ValueError):
     with ops.op_scope(values, None):
       pass
   with self.assertRaises(ValueError):
     with ops.op_scope(values, None, None):
       pass

 def testDefaultScopeName(self):
   g0 = ops.Graph()
   a = g0.create_op("a", [], [dtypes.float32])
   b = g0.create_op("b", [], [dtypes.float32])
   scope_name = "my_scope"
   default_scope_name = "my_default_scope"
   with ops.op_scope([a, b], scope_name, default_scope_name) as scope:
     self.assertEqual("%s/" % scope_name, scope)
     self.assertEqual(g0, ops.get_default_graph())
   with ops.op_scope([a, b], None, default_scope_name) as scope:
     self.assertEqual("%s/" % default_scope_name, scope)
     self.assertEqual(g0, ops.get_default_graph())

def one_hot_encoding(labels,
                     num_classes,
                     on_value=1.0,
                     off_value=0.0,
                     outputs_collections=None,
                     scope=None):
  """Transform numeric labels into onehot_labels using tf.one_hot.
  Args:
    labels: [batch_size] target labels.
    num_classes: total number of classes.
    on_value: A scalar defining the on-value.
    off_value: A scalar defining the off-value.
    outputs_collections: collection to add the outputs.
    scope: Optional scope for op_scope.
  Returns:
    one hot encoding of the labels.
  """
  with ops.op_scope([labels, num_classes], scope, 'OneHotEncoding') as sc:
    if labels.dtype == dtypes.int32:
      labels = standard_ops.to_int64(labels)
    outputs = standard_ops.one_hot(labels,
                                   num_classes,
                                   on_value=on_value,
                                   off_value=off_value)
    return utils.collect_named_outputs(outputs_collections, sc, outputs)

def ones(shape, dtype=dtypes.float32, name=None):
  """Creates a tensor with all elements set to 1.

  This operation returns a tensor of type `dtype` with shape `shape` and all
  elements set to 1.

  For example:

  ```python
  tf.ones([2, 3], int32) ==> [[1, 1, 1], [1, 1, 1]]
  ```

  Args:
    shape: Either a list of integers, or a 1-D `Tensor` of type `int32`.
    dtype: The type of an element in the resulting `Tensor`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` with all elements set to 1.
  """
  with ops.op_scope([shape], name, "ones") as name:
    if isinstance(shape, list):
      output = constant(1, shape=shape, dtype=dtype, name=name)
    else:
      shape = ops.convert_to_tensor(shape, name="shape")
      output = fill(shape, constant(1, dtype=dtype), name=name)
  assert output.dtype.base_dtype == dtypes.as_dtype(dtype).base_dtype
  return output

def dropout(inputs,
            keep_prob=0.5,
            noise_shape=None,
            is_training=True,
            outputs_collections=None,
            scope=None):
  """Returns a dropout op applied to the input.
  With probability `keep_prob`, outputs the input element scaled up by
  `1 / keep_prob`, otherwise outputs `0`.  The scaling is so that the expected
  sum is unchanged.
  Args:
    inputs: the tensor to pass to the nn.dropout op.
    keep_prob: A scalar `Tensor` with the same type as x. The probability
      that each element is kept.
    noise_shape: A 1-D `Tensor` of type `int32`, representing the
      shape for randomly generated keep/drop flags.
    is_training: A bool `Tensor` indicating whether or not the model
      is in training mode. If so, dropout is applied and values scaled.
      Otherwise, inputs is returned.
    outputs_collections: collection to add the outputs.
    scope: Optional scope for op_scope.
  Returns:
    a tensor representing the output of the operation.
  """
  with ops.op_scope([inputs], scope, 'Dropout') as sc:
    is_training = ops.convert_to_tensor(is_training)
    outputs = control_flow_ops.cond(
        is_training,
        lambda: nn.dropout(inputs, keep_prob, noise_shape),
        lambda: inputs)
    return utils.collect_named_outputs(outputs_collections, sc, outputs)

def range_input_producer(limit, num_epochs=None, shuffle=True, seed=None,
                         capacity=32, name=None):
  """Produces the integers from 0 to limit-1 in a queue.

  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.
    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.op_scope([limit], name, "input_producer") as name:
    range_tensor = math_ops.range(limit)
    return _input_producer(
        range_tensor, dtypes.int32, num_epochs, shuffle, seed, capacity, name,
        "fraction_of_%d_full" % capacity)

def rgb_to_grayscale(images, name=None):
  """Converts one or more images from RGB to Grayscale.

  Outputs a tensor of the same `DType` and rank as `images`.  The size of the
  last dimension of the output is 1, containing the Grayscale value of the
  pixels.

  Args:
    images: The RGB tensor to convert. Last dimension must have size 3 and
      should contain RGB values.
    name: A name for the operation (optional).

  Returns:
    The converted grayscale image(s).
  """
  with ops.op_scope([images], name, 'rgb_to_grayscale') as name:
    images = ops.convert_to_tensor(images, name='images')
    # Remember original dtype to so we can convert back if needed
    orig_dtype = images.dtype
    flt_image = convert_image_dtype(images, dtypes.float32)

    # Reference for converting between RGB and grayscale.
    # https://en.wikipedia.org/wiki/Luma_%28video%29
    rgb_weights = [0.2989, 0.5870, 0.1140]
    rank_1 = array_ops.expand_dims(array_ops.rank(images) - 1, 0)
    gray_float = math_ops.reduce_sum(flt_image * rgb_weights,
                                     rank_1,
                                     keep_dims=True)
    gray_float.set_shape(images.get_shape()[:-1].concatenate([1]))
    return convert_image_dtype(gray_float, orig_dtype, name=name)

  def enqueue_many(self, vals, name=None):
    """Enqueues zero or elements to this queue.

    This operation slices each component tensor along the 0th dimension to
    make multiple queue elements. All of the tensors in `vals` must have the
    same size in the 0th dimension.

    If the queue is full when this operation executes, it will block
    until all of the elements have been enqueued.

    Args:
      vals: The tensor or tuple of tensors from which the queue elements
        are taken.
      name: A name for the operation (optional).

    Returns:
      The operation that enqueues a batch of tuples of tensors to the queue.
    """
    if not isinstance(vals, (list, tuple)):
      vals = [vals]

    with ops.op_scope(vals, name, "%s_EnqueueMany" % self._name) as scope:
      vals = self._check_enqueue_dtypes(vals)

      # NOTE(mrry): Not using a shape function because we need access to
      # the `QueueBase` object.
      batch_dim = vals[0].get_shape().with_rank_at_least(1)[0]
      for val, shape in zip(vals, self._shapes):
        batch_dim = batch_dim.merge_with(
            val.get_shape().with_rank_at_least(1)[0])
        val.get_shape()[1:].assert_is_compatible_with(shape)

      return gen_data_flow_ops._queue_enqueue_many(
          self._queue_ref, vals, name=scope)

  def pdf(self, x, name="pdf"):
    """The PDF of observations in `x` under these Uniform distribution(s).

    Args:
      x: tensor of dtype `dtype`, must be broadcastable with `a` and `b`.
      name: The name to give this op.

    Returns:
      pdf: tensor of dtype `dtype`, the pdf values of `x`. If `x` is `nan`, will
          return `nan`.
    """
    with ops.name_scope(self.name):
      with ops.op_scope([self.a, self.b, x], name):
        x = ops.convert_to_tensor(x, name="x")
        if x.dtype != self.dtype:
          raise TypeError("Input x dtype does not match dtype: %s vs. %s" %
                          (x.dtype, self.dtype))

        broadcasted_x = x * self._ones()
        return math_ops.select(
            math_ops.is_nan(broadcasted_x), broadcasted_x, math_ops.select(
                math_ops.logical_or(broadcasted_x < self.a,
                                    broadcasted_x > self.b),
                array_ops.zeros_like(broadcasted_x),
                (1.0 / self.range()) * array_ops.ones_like(broadcasted_x)))

  def log_prob(self, x, name="log_prob"):
    """Log prob of observations in `x` under these Gamma distribution(s).

    Args:
      x: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
      name: The name to give this op.

    Returns:
      log_prob: tensor of dtype `dtype`, the log-PDFs of `x`.

    Raises:
      TypeError: if `x` and `alpha` are different dtypes.
    """
    with ops.name_scope(self.name):
      with ops.op_scope([self._alpha, self._beta, x], name):
        alpha = self._alpha
        beta = self._beta
        x = ops.convert_to_tensor(x)
        x = control_flow_ops.with_dependencies(
            [check_ops.assert_positive(x)] if self.strict else [],
            x)
        contrib_tensor_util.assert_same_float_dtype(tensors=[x,],
                                                    dtype=self.dtype)

        return (alpha * math_ops.log(beta) + (alpha - 1) * math_ops.log(x) -
                beta * x - math_ops.lgamma(self._alpha))

def floordiv(x, y, name=None):
  """Divides `x / y` elementwise, rounding down for floating point.

  The same as `tf.div(x,y)` for integers, but uses `tf.floor(tf.div(x,y))` for
  floating point arguments so that the result is always an integer (though
  possibly an integer represented as floating point).  This op is generated by
  `x // y` floor division in Python 3 and in Python 2.7 with
  `from __future__ import division`.

  Note that for efficiency, `floordiv` uses C semantics for negative numbers
  (unlike Python and Numpy).

  `x` and `y` must have the same type, and the result will have the same type
  as well.

  Args:
    x: `Tensor` numerator of real numeric type.
    y: `Tensor` denominator of real numeric type.
    name: A name for the operation (optional).

  Returns:
    `x / y` rounded down (except possibly towards zero for negative integers).

  Raises:
    TypeError: If the inputs are complex.
  """
  with ops.op_scope([x, y], name, "floordiv") as name:
    x = ops.convert_to_tensor(x, name="x")
    dtype = x.dtype
    if dtype.is_floating:
      return floor(div(x, y), name=name)
    else:
      if not dtype.is_integer:
        raise TypeError("Expected floating point or integer, got %r" % dtype)
      return div(x, y, name=name)