Python tensorflow.python.framework.dtypes.float32() Examples

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 _create_local(name, shape, collections=None, validate_shape=True,
                  dtype=dtypes.float32):
  """Creates a new local variable.

  Args:
    name: The name of the new or existing variable.
    shape: Shape of the new or existing variable.
    collections: A list of collection names to which the Variable will be added.
    validate_shape: Whether to validate the shape of the variable.
    dtype: Data type of the variables.

  Returns:
    The created variable.
  """
  # Make sure local variables are added to tf.GraphKeys.LOCAL_VARIABLES
  collections = list(collections or [])
  collections += [ops.GraphKeys.LOCAL_VARIABLES]
  return variable_scope.variable(
      array_ops.zeros(shape, dtype=dtype),
      name=name,
      trainable=False,
      collections=collections,
      validate_shape=validate_shape) 

def log_sigmoid(x, name=None):
  """Computes log sigmoid of `x` element-wise.

  Specifically, `y = log(1 / (1 + exp(-x)))`.  For numerical stability,
  we use `y = -tf.nn.softplus(-x)`.

  Args:
    x: A Tensor with type `float32` or `float64`.
    name: A name for the operation (optional).

  Returns:
    A Tensor with the same type as `x`.
  """
  with ops.name_scope(name, "LogSigmoid", [x]) as name:
    x = ops.convert_to_tensor(x, name="x")
    return gen_math_ops._neg(gen_nn_ops.softplus(-x), name=name) 

def testGraphStructureLookupGivesNodesAndAttributes(self):
    u_name, _, _, dump = self._session_run_for_graph_structure_lookup()

    u_read_name = u_name + "/read"

    # Test node name list lookup of the DebugDumpDir object.
    node_names = dump.nodes()
    self.assertTrue(u_name in node_names)
    self.assertTrue(u_read_name in node_names)

    # Test querying node attributes.
    u_attr = dump.node_attributes(u_name)
    self.assertEqual(dtypes.float32, u_attr["dtype"].type)
    self.assertEqual(1, len(u_attr["shape"].shape.dim))
    self.assertEqual(2, u_attr["shape"].shape.dim[0].size)

    with self.assertRaisesRegexp(ValueError, "No node named \"foo\" exists"):
      dump.node_attributes("foo") 

def round(x, name=None):
  """Rounds the values of a tensor to the nearest integer, element-wise.

  Rounds half to even.  Also known as bankers rounding. If you want to round
  according to the current system rounding mode use tf::cint.
  For example:

  ```python
  # 'a' is [0.9, 2.5, 2.3, 1.5, -4.5]
  tf.round(a) ==> [ 1.0, 2.0, 2.0, 2.0, -4.0 ]
  ```

  Args:
    x: A `Tensor` of type `float32` or `float64`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of same shape and type as `x`.
  """
  x = ops.convert_to_tensor(x, name="x")
  if x.dtype.is_integer:
    return x
  else:
    return gen_math_ops.round(x, name=name) 

def dataset(directory, subset, num_folds, fold, holdout):
    """ Return the mnist dataset """
    local_file = learn.datasets.base.maybe_download('omniglot.mat', directory, _DOWNLOAD_URL)
    data = scipy.io.loadmat(local_file)

    images = data[_SUBSET_TO_GROUP[subset]].astype(np.float32)
    images = images.transpose([1, 0]).reshape([-1] + IMAGE_SHAPE)

    if subset == 'train':
        images = images[:-_VALIDATION_SIZE]
    elif subset == 'validate':
        images = images[-_VALIDATION_SIZE:]

    images = get_folds(images, num_folds, fold, holdout)
    return slim.dataset.Dataset(
        images, None, None, images.shape[0], _ITEMS_TO_DESCRIPTIONS,
        data_shape=IMAGE_SHAPE) 

def __init__(self,
               reuse,
               name="",
               initializer=None,
               regularizer=None,
               caching_device=None,
               partitioner=None,
               custom_getter=None,
               name_scope="",
               dtype=dtypes.float32,
               use_resource=None):
    """Creates a new VariableScope with the given properties."""
    self._name = name
    self._initializer = initializer
    self._regularizer = regularizer
    self._reuse = reuse
    self._caching_device = caching_device
    self._partitioner = partitioner
    self._custom_getter = custom_getter
    self._name_scope = name_scope
    self._dtype = dtype
    self._use_resource = use_resource 

def softmax(logits, dim=-1, name=None):
  """Computes softmax activations.

  For each batch `i` and class `j` we have

      softmax = exp(logits) / reduce_sum(exp(logits), dim)

  Args:
    logits: A non-empty `Tensor`. Must be one of the following types: `half`,
      `float32`, `float64`.
    dim: The dimension softmax would be performed on. The default is -1 which
      indicates the last dimension.
    name: A name for the operation (optional).

  Returns:
    A `Tensor`. Has the same type as `logits`. Same shape as `logits`.
  Raises:
    InvalidArgumentError: if `logits` is empty or `dim` is beyond the last
      dimension of `logits`.
  """
  return _softmax(logits, gen_nn_ops._softmax, dim, name) 

def log_softmax(logits, dim=-1, name=None):
  """Computes log softmax activations.

  For each batch `i` and class `j` we have

      logsoftmax = logits - log(reduce_sum(exp(logits), dim))

  Args:
    logits: A non-empty `Tensor`. Must be one of the following types: `half`,
      `float32`, `float64`.
    dim: The dimension softmax would be performed on. The default is -1 which
      indicates the last dimension.
    name: A name for the operation (optional).

  Returns:
    A `Tensor`. Has the same type as `logits`. Same shape as `logits`.

  Raises:
    InvalidArgumentError: if `logits` is empty or `dim` is beyond the last
      dimension of `logits`.
  """
  return _softmax(logits, gen_nn_ops._log_softmax, dim, name) 

def add_check_numerics_ops():
  """Connect a `check_numerics` to every floating point tensor.

  `check_numerics` operations themselves are added for each `half`, `float`,
  or `double` tensor in the graph. For all ops in the graph, the
  `check_numerics` op for all of its (`half`, `float`, or `double`) inputs
  is guaranteed to run before the `check_numerics` op on any of its outputs.

  Returns:
    A `group` op depending on all `check_numerics` ops added.
  """
  check_op = []
  # This code relies on the ordering of ops in get_operations().
  # The producer of a tensor always comes before that tensor's consumer in
  # this list. This is true because get_operations() returns ops in the order
  # added, and an op can only be added after its inputs are added.
  for op in ops.get_default_graph().get_operations():
    for output in op.outputs:
      if output.dtype in [dtypes.float16, dtypes.float32, dtypes.float64]:
        message = op.name + ":" + str(output.value_index)
        with ops.control_dependencies(check_op):
          check_op = [array_ops.check_numerics(output, message=message)]
  return control_flow_ops.group(*check_op) 

def shuffle_join(tensor_list_list, capacity,
                 min_ad, phase):
    name = 'shuffel_input'
    types = _dtypes(tensor_list_list)
    queue = data_flow_ops.RandomShuffleQueue(
        capacity=capacity, min_after_dequeue=min_ad,
        dtypes=types)

    # Build enque Operations
    _enqueue_join(queue, tensor_list_list)

    full = (math_ops.cast(math_ops.maximum(0, queue.size() - min_ad),
                          dtypes.float32) * (1. / (capacity - min_ad)))
    # Note that name contains a '/' at the end so we intentionally do not place
    # a '/' after %s below.
    summary_name = (
        "queue/%s/fraction_over_%d_of_%d_full" %
        (name + '_' + phase, min_ad, capacity - min_ad))
    tf.summary.scalar(summary_name, full)

    dequeued = queue.dequeue(name='shuffel_deqeue')
    # dequeued = _deserialize_sparse_tensors(dequeued, sparse_info)
    return dequeued 

def erf(x, name=None):
  """Computes the Gauss error function of `x` element-wise.

  Args:
    x: A `Tensor` of `SparseTensor`. Must be one of the following types: `half`,
      `float32`, `float64`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` or `SparseTensor`, respectively. Has the same type as `x`.
  """
  with ops.name_scope(name, "Erf", [x]) as name:
    if isinstance(x, sparse_tensor.SparseTensor):
      x_erf = gen_math_ops.erf(x.values, name=name)
      return sparse_tensor.SparseTensor(
          indices=x.indices, values=x_erf, dense_shape=x.dense_shape)
    else:
      return gen_math_ops.erf(x, name=name) 

def sqrt(x, name=None):
  r"""Computes square root of x element-wise.

  I.e., \\(y = \sqrt{x} = x^{1/2}\\).

  Args:
    x: A `Tensor` or `SparseTensor`. Must be one of the following types: `half`,
      `float32`, `float64`, `complex64`, `complex128`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` or `SparseTensor`, respectively. Has the same type as `x`.
  """
  with ops.name_scope(name, "Sqrt", [x]) as name:
    if isinstance(x, sparse_tensor.SparseTensor):
      x_sqrt = gen_math_ops.sqrt(x.values, name=name)
      return sparse_tensor.SparseTensor(
          indices=x.indices, values=x_sqrt, dense_shape=x.dense_shape)
    else:
      return gen_math_ops.sqrt(x, name=name) 

def square(x, name=None):
  r"""Computes square of x element-wise.

  I.e., \\(y = x * x = x^2\\).

  Args:
    x: A `Tensor` or `SparseTensor`. Must be one of the following types: `half`,
      `float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` or `SparseTensor`. Has the same type as `x`.
  """
  with ops.name_scope(name, "Square", [x]) as name:
    if isinstance(x, sparse_tensor.SparseTensor):
      x_square = gen_math_ops.square(x.values, name=name)
      return sparse_tensor.SparseTensor(
          indices=x.indices, values=x_square, dense_shape=x.dense_shape)
    else:
      return gen_math_ops.square(x, name=name) 

def distort_color(image, color_ordering=0, scope=None):
    """
    随机进行图像增强（亮度、对比度操作）
    :param image: 输入图片
    :param color_ordering:模式
    :param scope: 命名空间
    :return: 增强后的图片
    """
    with tf.name_scope(scope, 'distort_color', [image]):
        if color_ordering == 0:  # 模式0.先调整亮度，再调整对比度
            rand_temp = random_ops.random_uniform([], -55, 20, seed=None) # [-70, 30] for generate img, [-50, 20] for true img 
            image = math_ops.add(image, math_ops.cast(rand_temp, dtypes.float32))
            image = tf.image.random_contrast(image, lower=0.45, upper=1.5) # [0.3, 1.75] for generate img, [0.45, 1.5] for true img 
        else:
            image = tf.image.random_contrast(image, lower=0.45, upper=1.5)
            rand_temp = random_ops.random_uniform([], -55, 30, seed=None)
            image = math_ops.add(image, math_ops.cast(rand_temp, dtypes.float32))

        # The random_* ops do not necessarily clamp.
        print(color_ordering)
        return tf.clip_by_value(image, 0.0, 255.0)  # 限定在0-255
########################################################################## 

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 postprocess(self, prediction_dict, true_image_shapes):
    with tf.control_dependencies(prediction_dict.values()):
      postprocessed_tensors = {
          'detection_boxes': tf.constant([[[0.0, 0.0, 0.5, 0.5],
                                           [0.5, 0.5, 0.8, 0.8]],
                                          [[0.5, 0.5, 1.0, 1.0],
                                           [0.0, 0.0, 0.0, 0.0]]], tf.float32),
          'detection_scores': tf.constant([[0.7, 0.6],
                                           [0.9, 0.0]], tf.float32),
          'detection_classes': tf.constant([[0, 1],
                                            [1, 0]], tf.float32),
          'num_detections': tf.constant([2, 1], tf.float32)
      }
      if self._add_detection_keypoints:
        postprocessed_tensors['detection_keypoints'] = tf.constant(
            np.arange(48).reshape([2, 2, 6, 2]), tf.float32)
      if self._add_detection_masks:
        postprocessed_tensors['detection_masks'] = tf.constant(
            np.arange(64).reshape([2, 2, 4, 4]), tf.float32)
    return postprocessed_tensors 

def _IFFT3DGrad(_, grad):
  rsize = 1. / math_ops.cast(_FFTSizeForGrad(grad, 3), dtypes.float32)
  return spectral_ops.fft3d(grad) * math_ops.complex(rsize, 0.) 

def pow(x, y, name=None):
  r"""Computes the power of one value to another.

  Given a tensor `x` and a tensor `y`, this operation computes \\\\(x^y\\\\) for
  corresponding elements in `x` and `y`. For example:

  ```
  # tensor 'x' is [[2, 2], [3, 3]]
  # tensor 'y' is [[8, 16], [2, 3]]
  tf.pow(x, y) ==> [[256, 65536], [9, 27]]
  ```

  Args:
    x: A `Tensor` of type `float32`, `float64`, `int32`, `int64`, `complex64`,
     or `complex128`.
    y: A `Tensor` of type `float32`, `float64`, `int32`, `int64`, `complex64`,
     or `complex128`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor`.
  """
  with ops.name_scope(name, "Pow", [x]) as name:
    return gen_math_ops._pow(x, y, name=name)


# pylint: disable=redefined-builtin,redefined-outer-name 

def __init__(self, dtype=dtypes.float32):
    self.dtype = dtypes.as_dtype(dtype) 

def cholesky_solve(chol, rhs, name=None):
  """Solves systems of linear eqns `A X = RHS`, given Cholesky factorizations.

  ```python
  # Solve 10 separate 2x2 linear systems:
  A = ... # shape 10 x 2 x 2
  RHS = ... # shape 10 x 2 x 1
  chol = tf.cholesky(A)  # shape 10 x 2 x 2
  X = tf.cholesky_solve(chol, RHS)  # shape 10 x 2 x 1
  # tf.matmul(A, X) ~ RHS
  X[3, :, 0]  # Solution to the linear system A[3, :, :] x = RHS[3, :, 0]

  # Solve five linear systems (K = 5) for every member of the length 10 batch.
  A = ... # shape 10 x 2 x 2
  RHS = ... # shape 10 x 2 x 5
  ...
  X[3, :, 2]  # Solution to the linear system A[3, :, :] x = RHS[3, :, 2]
  ```

  Args:
    chol:  A `Tensor`.  Must be `float32` or `float64`, shape is `[..., M, M]`.
      Cholesky factorization of `A`, e.g. `chol = tf.cholesky(A)`.
      For that reason, only the lower triangular parts (including the diagonal)
      of the last two dimensions of `chol` are used.  The strictly upper part is
      assumed to be zero and not accessed.
    rhs:  A `Tensor`, same type as `chol`, shape is `[..., M, K]`.
    name:  A name to give this `Op`.  Defaults to `cholesky_solve`.

  Returns:
    Solution to `A x = rhs`, shape `[..., M, K]`.
  """
  # To solve C C^* x = rhs, we
  # 1. Solve C y = rhs for y, thus y = C^* x
  # 2. Solve C^* x = y for x
  with ops.name_scope(name, 'cholesky_solve', [chol, rhs]):
    y = gen_linalg_ops.matrix_triangular_solve(
        chol, rhs, adjoint=False, lower=True)
    x = gen_linalg_ops.matrix_triangular_solve(
        chol, y, adjoint=True, lower=True)
    return x 

def _ProdGrad(op, grad):
  """Gradient for Prod."""
  # The gradient can be expressed by dividing the product by each entry of the
  # input tensor, but this approach can't deal with zeros in the input.
  # Here, we avoid this problem by composing the output as a product of two
  # cumprod operations.

  input_shape = array_ops.shape(op.inputs[0])
  # Reshape reduction indices for the case where the parameter is a scalar
  reduction_indices = array_ops.reshape(op.inputs[1], [-1])

  # Expand grad to full input shape
  output_shape_kept_dims = math_ops.reduced_shape(input_shape, op.inputs[1])
  tile_scaling = _safe_shape_div(input_shape, output_shape_kept_dims)
  grad = array_ops.reshape(grad, output_shape_kept_dims)
  grad = array_ops.tile(grad, tile_scaling)

  # Pack all reduced dimensions into a single one, so we can perform the
  # cumprod ops. If the reduction dims list is empty, it defaults to float32,
  # so we need to cast here.  We put all the shape-related ops on CPU to avoid
  # copying back and forth, and since listdiff is CPU only.
  with ops.device("/cpu:0"):
    reduced = math_ops.cast(reduction_indices, dtypes.int32)
    idx = math_ops.range(0, array_ops.rank(op.inputs[0]))
    other, _ = array_ops.setdiff1d(idx, reduced)
    perm = array_ops.concat([reduced, other], 0)
    reduced_num = math_ops.reduce_prod(array_ops.gather(input_shape, reduced))
    other_num = math_ops.reduce_prod(array_ops.gather(input_shape, other))
  permuted = array_ops.transpose(op.inputs[0], perm)
  permuted_shape = array_ops.shape(permuted)
  reshaped = array_ops.reshape(permuted, (reduced_num, other_num))

  # Calculate product, leaving out the current entry
  left = math_ops.cumprod(reshaped, axis=0, exclusive=True)
  right = math_ops.cumprod(reshaped, axis=0, exclusive=True, reverse=True)
  y = array_ops.reshape(left * right, permuted_shape)

  # Invert the transpose and reshape operations.
  # Make sure to set the statically known shape information through a reshape.
  out = grad * array_ops.transpose(y, array_ops.invert_permutation(perm))
  return array_ops.reshape(out, input_shape), None 

def sign(x, name=None):
  """Returns an element-wise indication of the sign of a number.

  `y = sign(x) = -1` if `x < 0`; 0 if `x == 0` or `tf.is_nan(x)`; 1 if `x > 0`.

  Zero is returned for NaN inputs.

  For complex numbers, `y = sign(x) = x / |x|` if `x != 0`, otherwise `y = 0`.

  Args:
    x: A `Tensor` or `SparseTensor`. Must be one of the following types: `half`,
      `float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` or `SparseTensor`, respectively. Has the same type as `x`.

  @compatibility(numpy)
  Equivalent to numpy.sign except for the behaviour for input values of NaN.
  @end_compatibility
  """
  with ops.name_scope(name, "Sign", [x]) as name:
    if isinstance(x, sparse_tensor.SparseTensor):
      x_sign = gen_math_ops.sign(x.values, name=name)
      return sparse_tensor.SparseTensor(
          indices=x.indices, values=x_sign, dense_shape=x.dense_shape)
    else:
      return gen_math_ops.sign(x, name=name) 

def per_image_standardization(image):
  """Linearly scales `image` to have zero mean and unit norm.

  This op computes `(x - mean) / adjusted_stddev`, where `mean` is the average
  of all values in image, and
  `adjusted_stddev = max(stddev, 1.0/sqrt(image.NumElements()))`.

  `stddev` is the standard deviation of all values in `image`. It is capped
  away from zero to protect against division by 0 when handling uniform images.

  Args:
    image: 3-D tensor of shape `[height, width, channels]`.

  Returns:
    The standardized image with same shape as `image`.

  Raises:
    ValueError: if the shape of 'image' is incompatible with this function.
  """
  image = ops.convert_to_tensor(image, name='image')
  image = control_flow_ops.with_dependencies(
      _Check3DImage(image, require_static=False), image)
  num_pixels = math_ops.reduce_prod(array_ops.shape(image))

  image = math_ops.cast(image, dtype=dtypes.float32)
  image_mean = math_ops.reduce_mean(image)

  variance = (math_ops.reduce_mean(math_ops.square(image)) -
              math_ops.square(image_mean))
  variance = gen_nn_ops.relu(variance)
  stddev = math_ops.sqrt(variance)

  # Apply a minimum normalization that protects us against uniform images.
  min_stddev = math_ops.rsqrt(math_ops.cast(num_pixels, dtypes.float32))
  pixel_value_scale = math_ops.maximum(stddev, min_stddev)
  pixel_value_offset = image_mean

  image = math_ops.subtract(image, pixel_value_offset)
  image = math_ops.div(image, pixel_value_scale)
  return image 

def _compute_gradient(x,
                      x_shape,
                      dx,
                      y,
                      y_shape,
                      dy,
                      x_init_value=None,
                      delta=1e-3,
                      extra_feed_dict=None):
  """Computes the theoretical and numerical jacobian."""
  t = dtypes.as_dtype(x.dtype)
  allowed_types = [dtypes.float16, dtypes.float32, dtypes.float64,
                   dtypes.complex64, dtypes.complex128]
  assert t.base_dtype in allowed_types, "Don't support type %s for x" % t.name
  t2 = dtypes.as_dtype(y.dtype)
  assert t2.base_dtype in allowed_types, "Don't support type %s for y" % t2.name

  if x_init_value is not None:
    i_shape = list(x_init_value.shape)
    assert(list(x_shape) == i_shape), "x_shape = %s, init_data shape = %s" % (
        x_shape, i_shape)
    x_data = x_init_value
  else:
    x_data = np.random.random_sample(x_shape).astype(t.as_numpy_dtype)
    if t.is_complex:
      x_data.imag = np.random.random_sample(x_shape)

  jacob_t = _compute_theoretical_jacobian(
      x, x_shape, x_data, dy, y_shape, dx, extra_feed_dict=extra_feed_dict)
  jacob_n = _compute_numeric_jacobian(
      x, x_shape, x_data, y, y_shape, delta, extra_feed_dict=extra_feed_dict)
  return jacob_t, jacob_n 

def truediv(x, y, name=None):
  """Divides x / y elementwise (using Python 3 division operator semantics).

  NOTE: Prefer using the Tensor operator or tf.divide which obey Python
  division operator semantics.

  This function forces Python 3 division operator semantics where all integer
  arguments are cast to floating types first.   This op is generated by normal
  `x / y` division in Python 3 and in Python 2.7 with
  `from __future__ import division`.  If you want integer division that rounds
  down, use `x // y` or `tf.floordiv`.

  `x` and `y` must have the same numeric type.  If the inputs are floating
  point, the output will have the same type.  If the inputs are integral, the
  inputs are cast to `float32` for `int8` and `int16` and `float64` for `int32`
  and `int64` (matching the behavior of Numpy).

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

  Returns:
    `x / y` evaluated in floating point.

  Raises:
    TypeError: If `x` and `y` have different dtypes.
  """
  return _truediv_python3(x, y, name) 

def to_float(x, name="ToFloat"):
  """Casts a tensor to type `float32`.

  Args:
    x: A `Tensor` or `SparseTensor`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` or `SparseTensor` with same shape as `x` with type `float32`.

  Raises:
    TypeError: If `x` cannot be cast to the `float32`.
  """
  return cast(x, dtypes.float32, name=name) 

def __init__(self, mean=0.0, stddev=1.0, seed=None, dtype=dtypes.float32):
    self.mean = mean
    self.stddev = stddev
    self.seed = seed
    self.dtype = _assert_float_dtype(dtypes.as_dtype(dtype)) 

def imag(input, name=None):
  r"""Returns the imaginary part of a complex number.

  Given a tensor `input` of complex numbers, this operation returns a tensor of
  type `float32` or `float64` that is the imaginary part of each element in
  `input`. All elements in `input` must be complex numbers of the form \\(a +
  bj\\), where *a* is the real part and *b* is the imaginary part returned by
  this operation.

  For example:

  ```
  # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j]
  tf.imag(input) ==> [4.75, 5.75]
  ```

  Args:
    input: A `Tensor`. Must be one of the following types: `complex64`,
      `complex128`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `float32` or `float64`.
  """
  with ops.name_scope(name, "Imag", [input]) as name:
    return gen_math_ops.imag(input, Tout=input.dtype.real_dtype, name=name)


# pylint: enable=redefined-outer-name,redefined-builtin 

def real(input, name=None):
  r"""Returns the real part of a complex number.

  Given a tensor `input` of complex numbers, this operation returns a tensor of
  type `float32` or `float64` that is the real part of each element in `input`.
  All elements in `input` must be complex numbers of the form \\(a + bj\\),
  where *a* is the real part returned by this operation and *b* is the
  imaginary part.

  For example:

  ```
  # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j]
  tf.real(input) ==> [-2.25, 3.25]
  ```

  If `input` is already real, it is returned unchanged.

  Args:
    input: A `Tensor`. Must have numeric type.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `float32` or `float64`.
  """
  with ops.name_scope(name, "Real", [input]) as name:
    real_dtype = input.dtype.real_dtype
    if input.dtype.base_dtype == real_dtype:
      return input
    return gen_math_ops.real(input, Tout=real_dtype, name=name)