Python array_ops.placeholder方法代码示例

# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import placeholder [as 别名]
def _save_checkpoint_from_mock_model(self,
                                       checkpoint_path,
                                       use_moving_averages,
                                       enable_quantization=False):
    g = tf.Graph()
    with g.as_default():
      mock_model = FakeModel()
      preprocessed_inputs, true_image_shapes = mock_model.preprocess(
          tf.placeholder(tf.float32, shape=[None, None, None, 3]))
      predictions = mock_model.predict(preprocessed_inputs, true_image_shapes)
      mock_model.postprocess(predictions, true_image_shapes)
      if use_moving_averages:
        tf.train.ExponentialMovingAverage(0.0).apply()
      tf.train.get_or_create_global_step()
      if enable_quantization:
        graph_rewriter_config = graph_rewriter_pb2.GraphRewriter()
        graph_rewriter_config.quantization.delay = 500000
        graph_rewriter_fn = graph_rewriter_builder.build(
            graph_rewriter_config, is_training=False)
        graph_rewriter_fn()
      saver = tf.train.Saver()
      init = tf.global_variables_initializer()
      with self.test_session() as sess:
        sess.run(init)
        saver.save(sess, checkpoint_path) 

# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import placeholder [as 别名]
def test_rewrite_nn_resize_op(self):
    g = tf.Graph()
    with g.as_default():
      x = array_ops.placeholder(dtypes.float32, shape=(8, 10, 10, 8))
      y = array_ops.placeholder(dtypes.float32, shape=(8, 20, 20, 8))
      s = ops.nearest_neighbor_upsampling(x, 2)
      t = s + y
      exporter.rewrite_nn_resize_op()

    resize_op_found = False
    for op in g.get_operations():
      if op.type == 'ResizeNearestNeighbor':
        resize_op_found = True
        self.assertEqual(op.inputs[0], x)
        self.assertEqual(op.outputs[0].consumers()[0], t.op)
        break

    self.assertTrue(resize_op_found) 

# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import placeholder [as 别名]
def delete_session_tensor(handle, name=None):
  """Delete the tensor for the given tensor handle.

  This is EXPERIMENTAL and subject to change.

  Delete the tensor of a given tensor handle. The tensor is produced
  in a previous run() and stored in the state of the session.

  Args:
    handle: The string representation of a persistent tensor handle.
    name: Optional name prefix for the return tensor.

  Returns:
    A pair of graph elements. The first is a placeholder for feeding a
    tensor handle and the second is a deletion operation.
  """
  handle_device = TensorHandle._get_device_name(handle)
  with ops.device(handle_device):
    holder = array_ops.placeholder(dtypes.string)
    deleter = gen_data_flow_ops._delete_session_tensor(holder, name=name)
  return (holder, deleter) 

# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import placeholder [as 别名]
def create_op(self, op_type, inputs, data_types, **kwargs):
    for i, x in enumerate(inputs):
      if x.graph is not self:
        # Referring to a tensor from other graph.
        if x in self._captured:
          # Captured already.
          inputs[i] = self._captured[x]
        else:
          # Substitute with a placeholder.
          self.extra_inputs.append(x)
          ph = array_ops.placeholder(x.dtype, shape=x.get_shape())
          # pylint: disable=protected-access
          ph._handle_shape = x._handle_shape
          ph._handle_dtype = x._handle_dtype
          # pylint: enable=protected-access
          inputs[i] = ph
          self._captured[x] = ph
          self.extra_args.append(ph)
    return super(_FuncGraph, self).create_op(op_type, inputs, data_types,
                                             **kwargs) 

# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import placeholder [as 别名]
def is_sparse(tensor):
  """Returns whether a tensor is a sparse tensor.

  Arguments:
      tensor: A tensor instance.

  Returns:
      A boolean.

  Example:
  ```python
      >>> from keras import backend as K
      >>> a = K.placeholder((2, 2), sparse=False)
      >>> print(K.is_sparse(a))
      False
      >>> b = K.placeholder((2, 2), sparse=True)
      >>> print(K.is_sparse(b))
      True
  ```
  """
  return isinstance(tensor, sparse_tensor.SparseTensor) 

# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import placeholder [as 别名]
def to_dense(tensor):
  """Converts a sparse tensor into a dense tensor and returns it.

  Arguments:
      tensor: A tensor instance (potentially sparse).

  Returns:
      A dense tensor.

  Examples:
  ```python
      >>> from keras import backend as K
      >>> b = K.placeholder((2, 2), sparse=True)
      >>> print(K.is_sparse(b))
      True
      >>> c = K.to_dense(b)
      >>> print(K.is_sparse(c))
      False
  ```
  """
  if is_sparse(tensor):
    return sparse_ops.sparse_tensor_to_dense(tensor)
  else:
    return tensor 

# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import placeholder [as 别名]
def ndim(x):
  """Returns the number of axes in a tensor, as an integer.

  Arguments:
      x: Tensor or variable.

  Returns:
      Integer (scalar), number of axes.

  Examples:
  ```python
      >>> from keras import backend as K
      >>> input = K.placeholder(shape=(2, 4, 5))
      >>> val = np.array([[1, 2], [3, 4]])
      >>> kvar = K.variable(value=val)
      >>> K.ndim(input)
      3
      >>> K.ndim(kvar)
      2
  ```
  """
  dims = x.get_shape()._dims
  if dims is not None:
    return len(dims)
  return None 

# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import placeholder [as 别名]
def set_value(x, value):
  """Sets the value of a variable, from a Numpy array.

  Arguments:
      x: Tensor to set to a new value.
      value: Value to set the tensor to, as a Numpy array
          (of the same shape).
  """
  value = np.asarray(value)
  tf_dtype = _convert_string_dtype(x.dtype.name.split('_')[0])
  if hasattr(x, '_assign_placeholder'):
    assign_placeholder = x._assign_placeholder
    assign_op = x._assign_op
  else:
    assign_placeholder = array_ops.placeholder(tf_dtype, shape=value.shape)
    assign_op = x.assign(assign_placeholder)
    x._assign_placeholder = assign_placeholder
    x._assign_op = assign_op
  get_session().run(assign_op, feed_dict={assign_placeholder: value}) 

# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import placeholder [as 别名]
def function(inputs, outputs, updates=None, **kwargs):
  """Instantiates a Keras function.

  Arguments:
      inputs: List of placeholder tensors.
      outputs: List of output tensors.
      updates: List of update ops.
      **kwargs: Not used with TensorFlow.

  Returns:
      Output values as Numpy arrays.
  """
  if kwargs:
    msg = [
        'Expected no kwargs, you passed %s' % len(kwargs),
        'kwargs passed to function are ignored with Tensorflow backend'
    ]
    warnings.warn('\n'.join(msg))
  return Function(inputs, outputs, updates=updates) 

# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import placeholder [as 别名]
def create_op(self, op_type, inputs, data_types, **kwargs):
    for i, x in enumerate(inputs):
      if x.graph is not self:
        # Referring to a tensor from other graph.
        if x in self._captured:
          # Captured already.
          inputs[i] = self._captured[x]
        elif self._capture_by_value:
          inputs[i] = self._add_tensor_and_parents(x)
        else:
          # Substitute with a placeholder.
          self.extra_inputs.append(x)
          ph = array_ops.placeholder(x.dtype, shape=x.get_shape())
          # pylint: disable=protected-access
          ph._handle_shape = x._handle_shape
          ph._handle_dtype = x._handle_dtype
          # pylint: enable=protected-access
          inputs[i] = ph
          self._captured[x] = ph
          self.extra_args.append(ph)
    return super(_ExperimentalFuncGraph, self).create_op(op_type, inputs,
                                                         data_types, **kwargs) 

# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import placeholder [as 别名]
def _add_op_and_parents(self, op):
    op_def = function._get_op_def(op)
    if op_def.is_stateful:
      raise ValueError("Cannot capture a stateful node by value.")
    elif op.type in ("Placeholder", "PlaceholderV2"):
      raise ValueError("Cannot capture a placeholder by value.")

    captured_inputs = [self._add_tensor_and_parents(x) for x in op.inputs]

    captured_op = self.create_op(op.type, captured_inputs,
                                 [o.dtype for o in op.outputs],
                                 name=op.name, attrs=op.node_def.attr,
                                 op_def=op_def)

    for t, captured_t in zip(op.outputs, captured_op.outputs):
      self._captured[t] = captured_t

    return captured_op 

# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import placeholder [as 别名]
def make_placeholder_from_tensor(t, scope=None):
  """Create a `tf.placeholder` for the Graph Editor.

  Note that the correct graph scope must be set by the calling function.

  Args:
    t: a `tf.Tensor` whose name will be used to create the placeholder
      (see function placeholder_name).
    scope: absolute scope within which to create the placeholder. None
      means that the scope of `t` is preserved. `""` means the root scope.
  Returns:
    A newly created `tf.placeholder`.
  Raises:
    TypeError: if `t` is not `None` or a `tf.Tensor`.
  """
  return tf_array_ops.placeholder(
      dtype=t.dtype, shape=t.get_shape(), name=placeholder_name(
          t, scope=scope)) 

# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import placeholder [as 别名]
def make_placeholder_from_dtype_and_shape(dtype, shape=None, scope=None):
  """Create a tf.placeholder for the Graph Editor.

  Note that the correct graph scope must be set by the calling function.
  The placeholder is named using the function placeholder_name (with no
  tensor argument).

  Args:
    dtype: the tensor type.
    shape: the tensor shape (optional).
    scope: absolute scope within which to create the placeholder. None
      means that the scope of t is preserved. "" means the root scope.
  Returns:
    A newly created tf.placeholder.
  """
  return tf_array_ops.placeholder(
      dtype=dtype, shape=shape, name=placeholder_name(scope=scope)) 

# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import placeholder [as 别名]
def create_placeholders_from_signatures(signatures):
  """Creates placeholders from given signatures.

  Args:
    signatures: Dict of `TensorSignature` objects or single `TensorSignature`,
      or `None`.

  Returns:
    Dict of `tf.placeholder` objects or single `tf.placeholder`, or `None`.
  """
  if signatures is None:
    return None
  if not isinstance(signatures, dict):
    return signatures.get_placeholder()
  return {
      key: signatures[key].get_placeholder()
      for key in signatures} 

# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import placeholder [as 别名]
def make_place_holder_tensors_for_base_features(feature_columns):
  """Returns placeholder tensors for inference.

  Args:
    feature_columns: An iterable containing all the feature columns. All items
      should be instances of classes derived from _FeatureColumn.
  Returns:
    A dict mapping feature keys to SparseTensors (sparse columns) or
    placeholder Tensors (dense columns).
  """
  # Get dict mapping features to FixedLenFeature or VarLenFeature values.
  dict_for_parse_example = create_feature_spec_for_parsing(feature_columns)
  placeholders = {}
  for column_name, column_type in dict_for_parse_example.items():
    if isinstance(column_type, parsing_ops.VarLenFeature):
      # Sparse placeholder for sparse tensors.
      placeholders[column_name] = array_ops.sparse_placeholder(
          column_type.dtype, name="Placeholder_{}".format(column_name))
    else:
      # Simple placeholder for dense tensors.
      placeholders[column_name] = array_ops.placeholder(
          column_type.dtype,
          shape=(None, column_type.shape[0]),
          name="Placeholder_{}".format(column_name))
  return placeholders