Python dtypes.float32方法代碼示例

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import float32 [as 別名]
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 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import float32 [as 別名]
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) 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import float32 [as 別名]
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) 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import float32 [as 別名]
def distort_color(image, color_ordering=0, scope=None):
    """
    隨機進行圖像增強（亮度、對比度操作）
    :param image: 輸入圖片
    :param color_ordering:模式
    :param scope: 命名空間
    :return: 增強後的圖片
    """
    with tf.name_scope(scope, 'distort_color', [image]):
        if color_ordering == 0:  # 模式0.先調整亮度，再調整對比度
            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
########################################################################## 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import float32 [as 別名]
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 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import float32 [as 別名]
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) 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import float32 [as 別名]
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") 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import float32 [as 別名]
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) 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import float32 [as 別名]
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 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import float32 [as 別名]
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) 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import float32 [as 別名]
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) 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import float32 [as 別名]
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) 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import float32 [as 別名]
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) 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import float32 [as 別名]
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) 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import float32 [as 別名]
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)