Python array_ops.ones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 ones [as 别名]
def dense_to_sparse(tensor, eos_token=0, outputs_collections=None, scope=None):
  """Converts a dense tensor into a sparse tensor.

  An example use would be to convert dense labels to sparse ones
  so that they can be fed to the ctc_loss.

  Args:
     tensor: An `int` `Tensor` to be converted to a `Sparse`.
     eos_token: An integer. It is part of the target label that signifies the
       end of a sentence.
     outputs_collections: Collection to add the outputs.
     scope: Optional scope for name_scope.
  """
  with variable_scope.variable_scope(scope, 'dense_to_sparse', [tensor]) as sc:
    tensor = ops.convert_to_tensor(tensor)
    indices = array_ops.where(
        math_ops.not_equal(tensor, constant_op.constant(eos_token,
                                                        tensor.dtype)))
    values = array_ops.gather_nd(tensor, indices)
    shape = array_ops.shape(tensor, out_type=dtypes.int64)
    outputs = sparse_tensor.SparseTensor(indices, values, shape)
    return utils.collect_named_outputs(outputs_collections, sc.name, outputs) 

# 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 ones [as 别名]
def _sample_n(self, n, seed=None):
    # The sampling method comes from the fact that if:
    #   X ~ Normal(0, 1)
    #   Z ~ Chi2(df)
    #   Y = X / sqrt(Z / df)
    # then:
    #   Y ~ StudentT(df).
    shape = array_ops.concat([[n], self.batch_shape_tensor()], 0)
    normal_sample = random_ops.random_normal(shape, dtype=self.dtype, seed=seed)
    df = self.df * array_ops.ones(self.batch_shape_tensor(), dtype=self.dtype)
    gamma_sample = random_ops.random_gamma(
        [n],
        0.5 * df,
        beta=0.5,
        dtype=self.dtype,
        seed=distribution_util.gen_new_seed(seed, salt="student_t"))
    samples = normal_sample * math_ops.rsqrt(gamma_sample / df)
    return samples * self.scale + self.loc  # Abs(scale) not wanted. 

# 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 ones [as 别名]
def _mean(self):
    mean = self.loc * array_ops.ones(self.batch_shape_tensor(),
                                     dtype=self.dtype)
    if self.allow_nan_stats:
      nan = np.array(np.nan, dtype=self.dtype.as_numpy_dtype())
      return array_ops.where(
          math_ops.greater(
              self.df,
              array_ops.ones(self.batch_shape_tensor(), dtype=self.dtype)),
          mean,
          array_ops.fill(self.batch_shape_tensor(), nan, name="nan"))
    else:
      return control_flow_ops.with_dependencies(
          [
              check_ops.assert_less(
                  array_ops.ones([], dtype=self.dtype),
                  self.df,
                  message="mean not defined for components of df <= 1"),
          ],
          mean) 

# 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 ones [as 别名]
def _mode(self):
    k = math_ops.cast(self.event_shape_tensor()[0], self.dtype)
    mode = (self.concentration - 1.) / (
        self.total_concentration[..., array_ops.newaxis] - k)
    if self.allow_nan_stats:
      nan = array_ops.fill(
          array_ops.shape(mode),
          np.array(np.nan, dtype=self.dtype.as_numpy_dtype()),
          name="nan")
      return array_ops.where(
          math_ops.reduce_all(self.concentration > 1., axis=-1),
          mode, nan)
    return control_flow_ops.with_dependencies([
        check_ops.assert_less(
            array_ops.ones([], self.dtype),
            self.concentration,
            message="Mode undefined when any concentration <= 1"),
    ], mode) 

# 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 ones [as 别名]
def ones_like(x, dtype=None, name=None):
  """Instantiates an all-ones variable of the same shape as another tensor.

  Arguments:
      x: Keras variable or tensor.
      dtype: String, dtype of returned Keras variable.
           None uses the dtype of x.
      name: String, name for the variable to create.

  Returns:
      A Keras variable with the shape of x filled with ones.

  Example:
  ```python
      >>> from keras import backend as K
      >>> kvar = K.variable(np.random.random((2,3)))
      >>> kvar_ones = K.ones_like(kvar)
      >>> K.eval(kvar_ones)
      array([[ 1.,  1.,  1.],
             [ 1.,  1.,  1.]], dtype=float32)
  ```
  """
  return array_ops.ones_like(x, dtype=dtype, name=name) 

# 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 ones [as 别名]
def random_binomial(shape, p=0.0, dtype=None, seed=None):
  """Returns a tensor with random binomial distribution of values.

  Arguments:
      shape: A tuple of integers, the shape of tensor to create.
      p: A float, `0. <= p <= 1`, probability of binomial distribution.
      dtype: String, dtype of returned tensor.
      seed: Integer, random seed.

  Returns:
      A tensor.
  """
  if dtype is None:
    dtype = floatx()
  if seed is None:
    seed = np.random.randint(10e6)
  return array_ops.where(
      random_ops.random_uniform(shape, dtype=dtype, seed=seed) <= p,
      array_ops.ones(shape, dtype=dtype), array_ops.zeros(shape, dtype=dtype)) 

# 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 ones [as 别名]
def _process_matrix(self, matrix, min_rank, event_ndims):
    """Helper to __init__ which gets matrix in batch-ready form."""
    # Pad the matrix so that matmul works in the case of a matrix and vector
    # input. Keep track if the matrix was padded, to distinguish between a
    # rank 3 tensor and a padded rank 2 tensor.
    # TODO(srvasude): Remove side-effects from functions. Its currently unbroken
    # but error-prone since the function call order may change in the future.
    self._rank_two_event_ndims_one = math_ops.logical_and(
        math_ops.equal(array_ops.rank(matrix), min_rank),
        math_ops.equal(event_ndims, 1))
    left = array_ops.where(self._rank_two_event_ndims_one, 1, 0)
    pad = array_ops.concat(
        [array_ops.ones(
            [left], dtype=dtypes.int32), array_ops.shape(matrix)],
        0)
    return array_ops.reshape(matrix, pad) 

# 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 ones [as 别名]
def _sample_n(self, n, seed=None):
    sample_shape = array_ops.concat([[n], array_ops.shape(self.logits)], 0)
    logits = self.logits * array_ops.ones(sample_shape)
    logits_2d = array_ops.reshape(logits, [-1, self.event_size])
    # Uniform variates must be sampled from the open-interval `(0, 1)` rather
    # than `[0, 1)`. To do so, we use `np.finfo(self.dtype.as_numpy_dtype).tiny`
    # because it is the smallest, positive, "normal" number. A "normal" number
    # is such that the mantissa has an implicit leading 1. Normal, positive
    # numbers x, y have the reasonable property that, `x + y >= max(x, y)`. In
    # this case, a subnormal number (i.e., np.nextafter) can cause us to sample
    # 0.
    uniform = random_ops.random_uniform(
        shape=array_ops.shape(logits_2d),
        minval=np.finfo(self.dtype.as_numpy_dtype).tiny,
        maxval=1.,
        dtype=self.dtype,
        seed=seed)
    gumbel = -math_ops.log(-math_ops.log(uniform))
    noisy_logits = math_ops.div(gumbel + logits_2d, self._temperature_2d)
    samples = nn_ops.log_softmax(noisy_logits)
    ret = array_ops.reshape(samples, sample_shape)
    return ret 

# 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 ones [as 别名]
def _add(self, op1, op2, operator_name, hints):
    # Will build a LinearOperatorScaledIdentity.

    if _type(op1) == _SCALED_IDENTITY:
      multiplier_1 = op1.multiplier
    else:
      multiplier_1 = array_ops.ones(op1.batch_shape_tensor(), dtype=op1.dtype)

    if _type(op2) == _SCALED_IDENTITY:
      multiplier_2 = op2.multiplier
    else:
      multiplier_2 = array_ops.ones(op2.batch_shape_tensor(), dtype=op2.dtype)

    return linear_operator_identity.LinearOperatorScaledIdentity(
        num_rows=op1.range_dimension_tensor(),
        multiplier=multiplier_1 + multiplier_2,
        is_non_singular=hints.is_non_singular,
        is_self_adjoint=hints.is_self_adjoint,
        is_positive_definite=hints.is_positive_definite,
        name=operator_name) 

# 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 ones [as 别名]
def _mode(self):
    mode = (self.a - 1.)/ (self.a_b_sum - 2.)
    if self.allow_nan_stats:
      nan = np.array(np.nan, dtype=self.dtype.as_numpy_dtype())
      return array_ops.where(
          math_ops.logical_and(
              math_ops.greater(self.a, 1.),
              math_ops.greater(self.b, 1.)),
          mode,
          array_ops.fill(self.batch_shape(), nan, name="nan"))
    else:
      return control_flow_ops.with_dependencies([
          check_ops.assert_less(
              array_ops.ones((), dtype=self.dtype), self.a,
              message="Mode not defined for components of a <= 1."),
          check_ops.assert_less(
              array_ops.ones((), dtype=self.dtype), self.b,
              message="Mode not defined for components of b <= 1."),
      ], mode) 

# 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 ones [as 别名]
def _sample_n(self, n, seed=None):
    # The sampling method comes from the fact that if:
    #   X ~ Normal(0, 1)
    #   Z ~ Chi2(df)
    #   Y = X / sqrt(Z / df)
    # then:
    #   Y ~ StudentT(df).
    shape = array_ops.concat([[n], self.batch_shape()], 0)
    normal_sample = random_ops.random_normal(shape, dtype=self.dtype, seed=seed)
    df = self.df * array_ops.ones(self.batch_shape(), dtype=self.dtype)
    gamma_sample = random_ops.random_gamma(
        [n],
        0.5 * df,
        beta=0.5,
        dtype=self.dtype,
        seed=distribution_util.gen_new_seed(seed, salt="student_t"))
    samples = normal_sample / math_ops.sqrt(gamma_sample / df)
    return samples * self.sigma + self.mu  # Abs(sigma) not wanted. 

# 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 ones [as 别名]
def _process_matrix(self, matrix, min_rank, event_ndims):
    """Helper to __init__ which gets matrix in batch-ready form."""
    # Pad the matrix so that matmul works in the case of a matrix and vector
    # input.  Keep track if the matrix was padded, to distinguish between a
    # rank 3 tensor and a padded rank 2 tensor.
    # TODO(srvasude): Remove side-effects from functions. Its currently unbroken
    # but error-prone since the function call order may change in the future.
    self._rank_two_event_ndims_one = math_ops.logical_and(
        math_ops.equal(array_ops.rank(matrix), min_rank),
        math_ops.equal(event_ndims, 1))
    left = array_ops.where(self._rank_two_event_ndims_one, 1, 0)
    pad = array_ops.concat(
        [array_ops.ones(
            [left], dtype=dtypes.int32), array_ops.shape(matrix)],
        0)
    return array_ops.reshape(matrix, pad) 

# 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 ones [as 别名]
def _mode(self):
    mode = ((self.alpha - 1.) /
            (array_ops.expand_dims(self.alpha_sum, dim=-1) -
             math_ops.cast(self.event_shape()[0], self.dtype)))
    if self.allow_nan_stats:
      nan = np.array(np.nan, dtype=self.dtype.as_numpy_dtype())
      shape = array_ops.concat((self.batch_shape(), self.event_shape()), 0)
      return array_ops.where(
          math_ops.greater(self.alpha, 1.),
          mode,
          array_ops.fill(shape, nan, name="nan"))
    else:
      return control_flow_ops.with_dependencies([
          check_ops.assert_less(
              array_ops.ones((), dtype=self.dtype), self.alpha,
              message="mode not defined for components of alpha <= 1")
      ], mode) 

# 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 ones [as 别名]
def _sample_n(self, n, seed=None):
    sample_shape = array_ops.concat(([n], array_ops.shape(self.logits)), 0)
    logits = self.logits * array_ops.ones(sample_shape)
    if logits.get_shape().ndims == 2:
      logits_2d = logits
    else:
      logits_2d = array_ops.reshape(logits, [-1, self.num_classes])
    np_dtype = self.dtype.as_numpy_dtype()
    minval = np.nextafter(np_dtype(0), np_dtype(1))
    uniform = random_ops.random_uniform(shape=array_ops.shape(logits_2d),
                                        minval=minval,
                                        maxval=1,
                                        dtype=self.dtype,
                                        seed=seed)
    gumbel = - math_ops.log(- math_ops.log(uniform))
    noisy_logits = math_ops.div(gumbel + logits_2d, self.temperature)
    samples = nn_ops.log_softmax(noisy_logits)
    ret = array_ops.reshape(samples, sample_shape)
    return ret 

# 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 ones [as 别名]
def test_axis_order_scope(self):
    xz_lt = core.LabeledTensor(array_ops.ones((2, 3)), ['x', 'z'])
    yz_lt = core.LabeledTensor(array_ops.ones((4, 3)), ['y', 'z'])

    _, _, broadcast_axes = core.align(xz_lt, yz_lt)
    self.assertEqual(list(broadcast_axes.keys()), ['x', 'y', 'z'])

    _, _, broadcast_axes = core.align(yz_lt, xz_lt)
    self.assertEqual(list(broadcast_axes.keys()), ['y', 'x', 'z'])

    with core.axis_order_scope(['x', 'y', 'z']):
      _, _, broadcast_axes = core.align(yz_lt, xz_lt)
      self.assertEqual(list(broadcast_axes.keys()), ['x', 'y', 'z'])

    with core.axis_order_scope(['x', 'y']):
      with self.assertRaises(core.AxisOrderError):
        core.align(xz_lt, yz_lt)
      with self.assertRaises(core.AxisOrderError):
        core.align(yz_lt, xz_lt)