Python tensorflow.python.ops.array_ops.placeholder() Examples

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())
          inputs[i] = ph
          self._captured[x] = ph
          self.extra_args.append(ph)
    return super(_FuncGraph, self).create_op(op_type, inputs, data_types,
                                             **kwargs) 

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 

def placeholder(dtype, axes, name=None):
  """Create a placeholder for a labeled tensor.

  For example:

    lt.placeholder(tf.float32, ['batch', ('channel', ['r', 'g', 'b'])])

  See tf.placeholder for more details.

  Args:
    dtype: The type of elements in the tensor to be fed.
    axes: sequence of strings (denoting axes of unknown size) and/or objects
      convertable to lt.Axis to label the result.
    name: Optional op name.

  Returns:
    Placeholder labeled tensor.
  """
  with ops.name_scope(name, 'lt_placeholder', []) as scope:
    axes = core.Axes([(axis, None) if isinstance(axis, string_types) else axis
                      for axis in axes])
    shape = [axis.size for axis in axes.values()]
    tensor = array_ops.placeholder(dtype, shape, name=scope)
    return core.LabeledTensor(tensor, axes) 

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) 

def build_parsing_serving_input_fn(feature_spec, default_batch_size=1):
  """Build an input_fn appropriate for serving, expecting fed tf.Examples.

  Creates an input_fn that expects a serialized tf.Example fed into a string
  placeholder.  The function parses the tf.Example according to the provided
  feature_spec, and returns all parsed Tensors as features.  This input_fn is
  for use at serving time, so the labels return value is always None.

  Args:
    feature_spec: a dict of string to `VarLenFeature`/`FixedLenFeature`.
    default_batch_size: the number of query examples expected per batch.

  Returns:
    An input_fn suitable for use in serving.
  """
  def input_fn():
    """An input_fn that expects a serialized tf.Example."""
    serialized_tf_example = array_ops.placeholder(dtype=dtypes.string,
                                                  shape=[default_batch_size],
                                                  name='input_example_tensor')
    inputs = {'examples': serialized_tf_example}
    features = parsing_ops.parse_example(serialized_tf_example, feature_spec)
    labels = None  # these are not known in serving!
    return InputFnOps(features, labels, inputs)
  return input_fn 

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) 

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)) 

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)) 

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} 

def testUnknownImageShape(self):
    ops.reset_default_graph()
    batch_size = 2
    height, width = 224, 224
    num_classes = 1000
    input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
    with self.test_session() as sess:
      inputs = array_ops.placeholder(
          dtypes.float32, shape=(batch_size, None, None, 3))
      logits, end_points = inception_v2.inception_v2(inputs, num_classes)
      self.assertTrue(logits.op.name.startswith('InceptionV2/Logits'))
      self.assertListEqual(logits.get_shape().as_list(),
                           [batch_size, num_classes])
      pre_pool = end_points['Mixed_5c']
      feed_dict = {inputs: input_np}
      variables.global_variables_initializer().run()
      pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict)
      self.assertListEqual(list(pre_pool_out.shape), [batch_size, 7, 7, 1024]) 

def create_test_input(batch_size, height, width, channels):
  """Create test input tensor.

  Args:
    batch_size: The number of images per batch or `None` if unknown.
    height: The height of each image or `None` if unknown.
    width: The width of each image or `None` if unknown.
    channels: The number of channels per image or `None` if unknown.

  Returns:
    Either a placeholder `Tensor` of dimension
      [batch_size, height, width, channels] if any of the inputs are `None` or a
    constant `Tensor` with the mesh grid values along the spatial dimensions.
  """
  if None in [batch_size, height, width, channels]:
    return array_ops.placeholder(dtypes.float32,
                                 (batch_size, height, width, channels))
  else:
    return math_ops.to_float(
        np.tile(
            np.reshape(
                np.reshape(np.arange(height), [height, 1]) + np.reshape(
                    np.arange(width), [1, width]), [1, height, width, 1]),
            [batch_size, 1, 1, channels])) 

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}) 

def create_test_input(batch_size, height, width, channels):
  """Create test input tensor.

  Args:
    batch_size: The number of images per batch or `None` if unknown.
    height: The height of each image or `None` if unknown.
    width: The width of each image or `None` if unknown.
    channels: The number of channels per image or `None` if unknown.

  Returns:
    Either a placeholder `Tensor` of dimension
      [batch_size, height, width, channels] if any of the inputs are `None` or a
    constant `Tensor` with the mesh grid values along the spatial dimensions.
  """
  if None in [batch_size, height, width, channels]:
    return array_ops.placeholder(dtypes.float32,
                                 (batch_size, height, width, channels))
  else:
    return math_ops.to_float(
        np.tile(
            np.reshape(
                np.reshape(np.arange(height), [height, 1]) + np.reshape(
                    np.arange(width), [1, width]), [1, height, width, 1]),
            [batch_size, 1, 1, channels])) 

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) 

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 

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) 

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)) 

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)) 

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) 

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} 

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 

def placeholder(dtype, axes, name=None):
  """Create a placeholder for a labeled tensor.

  For example:

    lt.placeholder(tf.float32, ['batch', ('channel', ['r', 'g', 'b'])])

  See tf.placeholder for more details.

  Args:
    dtype: The type of elements in the tensor to be fed.
    axes: sequence of strings (denoting axes of unknown size) and/or objects
      convertable to lt.Axis to label the result.
    name: Optional op name.

  Returns:
    Placeholder labeled tensor.
  """
  with ops.name_scope(name, 'lt_placeholder', []) as scope:
    axes = core.Axes([(axis, None) if isinstance(axis, string_types) else axis
                      for axis in axes])
    shape = [axis.size for axis in axes.values()]
    tensor = array_ops.placeholder(dtype, shape, name=scope)
    return core.LabeledTensor(tensor, axes) 

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) 

def tf_spec_summary(spec,
                    inputs=None,
                    input_shape=None,
                    input_type=dtypes.float32):
  """Output a summary of the specification.

  This prints a list of left-most tensor operations and summarized the
  variables found in the right branches. This kind of representation
  is particularly useful for networks that are generally structured
  like pipelines.

  Args:
      spec: specification
      inputs: input to the spec construction (usually a Tensor)
      input_shape: optional shape of input
      input_type: type of the input tensor
  """

  if inputs is None:
    inputs = array_ops.placeholder(input_type, input_shape)
  outputs = specs.create_net(spec, inputs)
  tf_parameter_summary(outputs) 

def tf_spec_print(spec,
                  inputs=None,
                  input_shape=None,
                  input_type=dtypes.float32):
  """Print a tree representing the spec.

  Args:
      spec: specification
      inputs: input to the spec construction (usually a Tensor)
      input_shape: optional shape of input
      input_type: type of the input tensor
  """

  if inputs is None:
    inputs = array_ops.placeholder(input_type, input_shape)
  outputs = specs.create_net(spec, inputs)
  tf_print(outputs) 

def setUp(self):
    self.a = variables.Variable(10.0, name="a")
    self.b = variables.Variable(20.0, name="b")

    self.c = math_ops.add(self.a, self.b, name="c")  # Should be 30.0.
    self.d = math_ops.subtract(self.a, self.c, name="d")  # Should be -20.0.
    self.e = math_ops.multiply(self.c, self.d, name="e")  # Should be -600.0.

    self.ph = array_ops.placeholder(dtypes.float32, shape=(2, 2), name="ph")
    self.f = math_ops.multiply(self.e, self.ph, name="f")

    self.opt = gradient_descent.GradientDescentOptimizer(0.1).minimize(
        self.e, name="opt")

    self.sess = session.Session()

    self.sess.run(self.a.initializer)
    self.sess.run(self.b.initializer) 

def setUp(self):
    self._tmp_dir = tempfile.mktemp()

    self.v = variables.Variable(10.0, name="v")
    self.delta = constant_op.constant(1.0, name="delta")
    self.inc_v = state_ops.assign_add(self.v, self.delta, name="inc_v")

    self.ph = array_ops.placeholder(dtypes.float32, name="ph")
    self.xph = array_ops.transpose(self.ph, name="xph")
    self.m = constant_op.constant(
        [[0.0, 1.0, 2.0], [-4.0, -1.0, 0.0]], dtype=dtypes.float32, name="m")
    self.y = math_ops.matmul(self.m, self.xph, name="y")

    self.sess = session.Session()

    # Initialize variable.
    self.sess.run(self.v.initializer) 

def testBasicUnbatch(self):
    """Tests that batch and unbatch work together."""
    with self.test_session() as sess:
      inp = array_ops.placeholder(dtype=dtypes.int32, shape=[1])
      batched, index, id_t = batch_ops.batch(
          [inp], num_batch_threads=1, max_batch_size=10,
          batch_timeout_micros=100000,  # 100ms
          allowed_batch_sizes=[3, 10],
          grad_timeout_micros=0, batching_queue="")
      computation = batched[0] + 1
      result = batch_ops.unbatch(computation, index, id_t,
                                 timeout_micros=1000000, shared_name="unbatch")
      thread_results = []

      def worker():
        thread_results.extend(sess.run([result], feed_dict={inp: [1]}))

      worker_thread = threading.Thread(target=worker)
      worker_thread.start()
      main_results = sess.run([result], feed_dict={inp: [2]})
      worker_thread.join()
      self.assertEqual(thread_results[0], [2])
      self.assertEqual(main_results[0], [3]) 

def testBasicUnbatchDecorated(self):
    """Tests that the batch_function decorator works."""
    with self.test_session() as sess:
      @batch_ops.batch_function(1, 10, 100000)
      def computation(in_t):
        return in_t + 1
      inp = array_ops.placeholder(dtype=dtypes.int32, shape=[1])
      result = computation(inp)
      thread_results = []

      def worker():
        thread_results.extend(sess.run([result], feed_dict={inp: [1]}))

      worker_thread = threading.Thread(target=worker)
      worker_thread.start()
      main_results = sess.run([result], feed_dict={inp: [2]})
      worker_thread.join()
      self.assertEqual(thread_results[0], [2])
      self.assertEqual(main_results[0], [3]) 

def testUnbatchGrad(self):
    """Tests that batch and unbatch are differentiable."""
    with self.test_session() as sess:
      inp = array_ops.placeholder(dtype=dtypes.int32, shape=[1])
      batched, index, id_t = batch_ops.batch(
          [inp], num_batch_threads=1, max_batch_size=2,
          batch_timeout_micros=36000000, grad_timeout_micros=1000000,
          batching_queue="")
      computation = batched[0] * batched[0]
      result = batch_ops.unbatch(computation, index, id_t,
                                 timeout_micros=1000000, shared_name="unbatch")
      grad = gradients_impl.gradients(result, inp)
      thread_results = []

      def worker():
        thread_results.extend(sess.run([grad], feed_dict={inp: [1]}))

      worker_thread = threading.Thread(target=worker)
      worker_thread.start()
      main_results = sess.run([grad], feed_dict={inp: [2]})
      worker_thread.join()
      self.assertEqual(thread_results[0], [2])
      self.assertEqual(main_results[0], [4])