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

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)) 

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. 

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) 

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) 

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) 

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 

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) 

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) 

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. 

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) 

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 

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) 

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) 

def test_invalid(self):
    scalar_lt = core.LabeledTensor(array_ops.ones(()), [])
    x_lt = core.LabeledTensor(array_ops.ones((2,)), ['x'])
    x2_lt = core.LabeledTensor(array_ops.ones((3,)), ['x'])
    y_lt = core.LabeledTensor(array_ops.ones((3,)), ['y'])
    xy_lt = core.LabeledTensor(array_ops.ones((2, 3)), ['x', 'y'])
    xyz_lt = core.LabeledTensor(array_ops.ones((2, 3, 1)), ['x', 'y', 'z'])

    with self.assertRaisesRegexp(ValueError, 'inputs with at least rank'):
      ops.matmul(x_lt, scalar_lt)

    with self.assertRaises(NotImplementedError):
      ops.matmul(x_lt, xyz_lt)

    with self.assertRaisesRegexp(ValueError, 'exactly one axis in common'):
      ops.matmul(x_lt, y_lt)

    with self.assertRaises(NotImplementedError):
      ops.matmul(xy_lt, xy_lt)

    with self.assertRaisesRegexp(ValueError, 'does not match'):
      ops.matmul(x_lt, x2_lt) 

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) 

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) 

def test_name(self):
    x_lt = core.LabeledTensor(array_ops.ones((3,)), ['x'])
    matmul_lt = ops.matmul(x_lt, x_lt)
    self.assertIn('lt_matmul', matmul_lt.name) 

def test_invalid(self):
    axis_order = ['w', 'x', 'y', 'z']
    lt = core.LabeledTensor(array_ops.ones((1, 1, 1, 1)), axis_order)
    with self.assertRaises(core.AxisOrderError):
      core.check_axis_order(lt)
    with self.assertRaises(core.AxisOrderError):
      core.check_axis_order(lt, axis_order[:-1])
    with self.assertRaises(core.AxisOrderError):
      core.check_axis_order(lt, axis_order[::-1]) 

def __init__(self,
               lam,
               validate_args=False,
               allow_nan_stats=True,
               name="Exponential"):
    """Construct Exponential distribution with parameter `lam`.

    Args:
      lam: Floating point tensor, the rate of the distribution(s).
        `lam` must contain only positive values.
      validate_args: `Boolean`, default `False`.  Whether to assert that
        `lam > 0`, and that `x > 0` in the methods `prob(x)` and `log_prob(x)`.
        If `validate_args` is `False` and the inputs are invalid, correct
        behavior is not guaranteed.
      allow_nan_stats: `Boolean`, default `True`.  If `False`, raise an
        exception if a statistic (e.g. mean/mode/etc...) is undefined for any
        batch member. If `True`, batch members with valid parameters leading to
        undefined statistics will return NaN for this statistic.
      name: The name to prepend to all ops created by this distribution.
    """
    parameters = locals()
    parameters.pop("self")
    # Even though all statistics of are defined for valid inputs, this is not
    # true in the parent class "Gamma."  Therefore, passing
    # allow_nan_stats=True
    # through to the parent class results in unnecessary asserts.
    with ops.name_scope(name, values=[lam]) as ns:
      self._lam = ops.convert_to_tensor(lam, name="lam")
    super(Exponential, self).__init__(
        alpha=array_ops.ones((), dtype=self._lam.dtype),
        beta=self._lam,
        allow_nan_stats=allow_nan_stats,
        validate_args=validate_args,
        name=ns)
    # While the Gamma distribution is not re-parameterizable, the
    # exponential distribution is.
    self._is_reparameterized = True
    self._parameters = parameters
    self._graph_parents += [self._lam] 

def _mean(self):
    mean = self.beta / (self.alpha - 1.)
    if self.allow_nan_stats:
      nan = np.array(np.nan, dtype=self.dtype.as_numpy_dtype())
      return array_ops.where(
          self.alpha > 1., mean,
          array_ops.fill(self.batch_shape(), nan, name="nan"))
    else:
      return control_flow_ops.with_dependencies([
          check_ops.assert_less(
              array_ops.ones((), self.dtype), self.alpha,
              message="mean not defined for components of self.alpha <= 1"),
      ], mean) 

def _entropy(self):
    v = array_ops.ones(self.batch_shape(), dtype=self.dtype)[..., None]
    u = v * self.df[..., None]
    beta_arg = array_ops.concat([u, v], -1) / 2.
    return (math_ops.log(math_ops.abs(self.sigma)) +
            0.5 * math_ops.log(self.df) +
            special_math_ops.lbeta(beta_arg) +
            0.5 * (self.df + 1.) *
            (math_ops.digamma(0.5 * (self.df + 1.)) -
             math_ops.digamma(0.5 * self.df))) 

def _mode(self):
    mode = (self.alpha - 1.) / self.beta
    if self.allow_nan_stats:
      nan = np.array(np.nan, dtype=self.dtype.as_numpy_dtype())
      return array_ops.where(
          self.alpha >= 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((), self.dtype),
              self.alpha,
              message="mode not defined for components of alpha <= 1"),
          ], mode) 

def _sqrt_to_dense(self):
    diag = array_ops.ones(self.vector_shape(), dtype=self.dtype)
    dense = array_ops.matrix_diag(diag)
    dense.set_shape(self.get_shape())
    return math_ops.sqrt(self._scale) * dense 

def _to_dense(self):
    diag = array_ops.ones(self.vector_shape(), dtype=self.dtype)
    dense = array_ops.matrix_diag(diag)
    dense.set_shape(self.get_shape())
    return self._scale * dense 

def _batch_log_det(self):
    rank = array_ops.size(self._shape_arg)
    last_dim = math_ops.cast(
        array_ops.gather(self._shape_arg, rank - 1), dtype=self.dtype)
    log_det = (last_dim * math_ops.log(math_ops.abs(self._scale)) *
               array_ops.ones(self.batch_shape(), dtype=self.dtype))
    log_det.set_shape(self.get_batch_shape())
    return log_det 

def unit_norm(inputs, dim, epsilon=1e-7, scope=None):
  """Normalizes the given input across the specified dimension to unit length.

  Note that the rank of `input` must be known.

  Args:
    inputs: A `Tensor` of arbitrary size.
    dim: The dimension along which the input is normalized.
    epsilon: A small value to add to the inputs to avoid dividing by zero.
    scope: Optional scope for variable_scope.

  Returns:
    The normalized `Tensor`.

  Raises:
    ValueError: If dim is smaller than the number of dimensions in 'inputs'.
  """
  with variable_scope.variable_scope(scope, 'UnitNorm', [inputs]):
    if not inputs.get_shape():
      raise ValueError('The input rank must be known.')
    input_rank = len(inputs.get_shape().as_list())
    if dim < 0 or dim >= input_rank:
      raise ValueError(
          'dim must be positive but smaller than the input rank.')

    lengths = math_ops.sqrt(epsilon + math_ops.reduce_sum(
        math_ops.square(inputs), dim, True))
    multiples = []
    if dim > 0:
      multiples.append(array_ops.ones([dim], dtypes.int32))
    multiples.append(
        array_ops.strided_slice(array_ops.shape(inputs), [dim], [dim + 1]))
    if dim < (input_rank - 1):
      multiples.append(array_ops.ones([input_rank - 1 - dim], dtypes.int32))
    multiples = array_ops.concat(multiples, 0)
    return math_ops.div(inputs, array_ops.tile(lengths, multiples)) 

def _add_bias_column(feature_columns, columns_to_tensors, bias_variable,
                     columns_to_variables):
  """Adds a fake bias feature column filled with all 1s."""
  # TODO(b/31008490): Move definition to a common constants place.
  bias_column_name = "tf_virtual_bias_column"
  if any(col.name is bias_column_name for col in feature_columns):
    raise ValueError("%s is a reserved column name." % bias_column_name)
  if not feature_columns:
    raise ValueError("feature_columns can't be empty.")

  # Loop through input tensors until we can figure out batch_size.
  batch_size = None
  for column in columns_to_tensors.values():
    if isinstance(column, tuple):
      column = column[0]
    if isinstance(column, sparse_tensor.SparseTensor):
      shape = tensor_util.constant_value(column.dense_shape)
      if shape is not None:
        batch_size = shape[0]
        break
    else:
      batch_size = array_ops.shape(column)[0]
      break
  if batch_size is None:
    raise ValueError("Could not infer batch size from input features.")

  bias_column = layers.real_valued_column(bias_column_name)
  columns_to_tensors[bias_column] = array_ops.ones([batch_size, 1],
                                                   dtype=dtypes.float32)
  columns_to_variables[bias_column] = [bias_variable] 

def _init_clusters(self):
    """Initialization of clusters.

    Returns:
    Tuple with following elements:
      cluster_centers: a Tensor for storing cluster centers
      cluster_counts: a Tensor for storing counts of points assigned to this
        cluster. This is used by mini-batch training.
    """
    init = self._initial_clusters
    if init == RANDOM_INIT:
      clusters_init = self._init_clusters_random()
    elif init == KMEANS_PLUS_PLUS_INIT:
      # Points from only the first shard are used for initializing centers.
      # TODO(ands): Use all points.
      clusters_init = gen_clustering_ops.kmeans_plus_plus_initialization(
          self._inputs[0], self._num_clusters, self._random_seed,
          self._kmeans_plus_plus_num_retries)
    elif callable(init):
      clusters_init = init(self._inputs, self._num_clusters)
    elif not isinstance(init, str):
      clusters_init = init
    else:
      assert False, 'Unsupported init passed to Kmeans %s' % str(init)
    if self._distance_metric == COSINE_DISTANCE and clusters_init is not None:
      clusters_init = nn_impl.l2_normalize(clusters_init, dim=1)
    clusters_init = clusters_init if clusters_init is not None else []
    cluster_centers = variables.Variable(
        clusters_init, name='clusters', validate_shape=False)
    cluster_counts = (variables.Variable(
        array_ops.ones(
            [self._num_clusters], dtype=dtypes.int64)) if self._use_mini_batch
                      else None)
    return cluster_centers, cluster_counts 

def _sum_rows(x):
  """Returns a vector summing up each row of the matrix x."""
  # _sum_rows(x) is equivalent to math_ops.reduce_sum(x, 1) when x is
  # a matrix.  The gradient of _sum_rows(x) is more efficient than
  # reduce_sum(x, 1)'s gradient in today's implementation. Therefore,
  # we use _sum_rows(x) in the nce_loss() computation since the loss
  # is mostly used for training.
  cols = array_ops.shape(x)[1]
  ones_shape = array_ops.stack([cols, 1])
  ones = array_ops.ones(ones_shape, x.dtype)
  return array_ops.reshape(math_ops.matmul(x, ones), [-1]) 

def grayscale_to_rgb(images, name=None):
  """Converts one or more images from Grayscale to RGB.

  Outputs a tensor of the same `DType` and rank as `images`.  The size of the
  last dimension of the output is 3, containing the RGB value of the pixels.

  Args:
    images: The Grayscale tensor to convert. Last dimension must be size 1.
    name: A name for the operation (optional).

  Returns:
    The converted grayscale image(s).
  """
  with ops.name_scope(name, 'grayscale_to_rgb', [images]) as name:
    images = ops.convert_to_tensor(images, name='images')
    rank_1 = array_ops.expand_dims(array_ops.rank(images) - 1, 0)
    shape_list = (
        [array_ops.ones(rank_1,
                        dtype=dtypes.int32)] + [array_ops.expand_dims(3, 0)])
    multiples = array_ops.concat(shape_list, 0)
    rgb = array_ops.tile(images, multiples, name=name)
    rgb.set_shape(images.get_shape()[:-1].concatenate([3]))
    return rgb


# pylint: disable=invalid-name