Python tensorflow.python.ops.check_ops.assert_equal() Examples

def _check_chol(self, chol):
    """Verify that `chol` is proper."""
    chol = ops.convert_to_tensor(chol, name="chol")
    if not self.verify_pd:
      return chol

    shape = array_ops.shape(chol)
    rank = array_ops.rank(chol)

    is_matrix = check_ops.assert_rank_at_least(chol, 2)
    is_square = check_ops.assert_equal(
        array_ops.gather(shape, rank - 2), array_ops.gather(shape, rank - 1))

    deps = [is_matrix, is_square]
    diag = array_ops.matrix_diag_part(chol)
    deps.append(check_ops.assert_positive(diag))

    return control_flow_ops.with_dependencies(deps, chol) 

def _check_shape(self, shape):
    """Check that the init arg `shape` defines a valid operator."""
    shape = ops.convert_to_tensor(shape, name="shape")
    if not self._verify_pd:
      return shape

    # Further checks are equivalent to verification that this is positive
    # definite.  Why?  Because the further checks simply check that this is a
    # square matrix, and combining the fact that this is square (and thus maps
    # a vector space R^k onto itself), with the behavior of .matmul(), this must
    # be the identity operator.
    rank = array_ops.size(shape)
    assert_matrix = check_ops.assert_less_equal(2, rank)
    with ops.control_dependencies([assert_matrix]):
      last_dim = array_ops.gather(shape, rank - 1)
      second_to_last_dim = array_ops.gather(shape, rank - 2)
      assert_square = check_ops.assert_equal(last_dim, second_to_last_dim)
      return control_flow_ops.with_dependencies([assert_matrix, assert_square],
                                                shape) 

def assert_integer_form(
    x, data=None, summarize=None, message=None, name="assert_integer_form"):
  """Assert that x has integer components (or floats equal to integers).

  Args:
    x: Numeric `Tensor`
    data: The tensors to print out if the condition is `False`. Defaults to
      error message and first few entries of `x` and `y`.
    summarize: Print this many entries of each tensor.
    message: A string to prefix to the default message.
    name: A name for this operation (optional).

  Returns:
    Op raising `InvalidArgumentError` if round(x) != x.
  """

  message = message or "x has non-integer components"
  x = ops.convert_to_tensor(x, name="x")
  casted_x = math_ops.to_int64(x)
  return check_ops.assert_equal(
      x, math_ops.cast(math_ops.round(casted_x), x.dtype),
      data=data, summarize=summarize, message=message, name=name) 

def assert_integer_form(
    x, data=None, summarize=None, message=None, name="assert_integer_form"):
  """Assert that x has integer components (or floats equal to integers).

  Args:
    x: Floating-point `Tensor`
    data: The tensors to print out if the condition is `False`. Defaults to
      error message and first few entries of `x` and `y`.
    summarize: Print this many entries of each tensor.
    message: A string to prefix to the default message.
    name: A name for this operation (optional).

  Returns:
    Op raising `InvalidArgumentError` if round(x) != x.
  """

  message = message or "x has non-integer components"
  x = ops.convert_to_tensor(x, name="x")
  casted_x = math_ops.to_int64(x)
  return check_ops.assert_equal(
      x, math_ops.cast(math_ops.round(casted_x), x.dtype),
      data=data, summarize=summarize, message=message, name=name) 

def _check_shape(self, shape):
    """Check that the init arg `shape` defines a valid operator."""
    shape = ops.convert_to_tensor(shape, name="shape")
    if not self._verify_pd:
      return shape

    # Further checks are equivalent to verification that this is positive
    # definite.  Why?  Because the further checks simply check that this is a
    # square matrix, and combining the fact that this is square (and thus maps
    # a vector space R^k onto itself), with the behavior of .matmul(), this must
    # be the identity operator.
    rank = array_ops.size(shape)
    assert_matrix = check_ops.assert_less_equal(2, rank)
    with ops.control_dependencies([assert_matrix]):
      last_dim = array_ops.gather(shape, rank - 1)
      second_to_last_dim = array_ops.gather(shape, rank - 2)
      assert_square = check_ops.assert_equal(last_dim, second_to_last_dim)
      return control_flow_ops.with_dependencies([assert_matrix, assert_square],
                                                shape) 

def _check_logits(logits, expected_logits_dimension):
  """Check logits type and shape."""
  with ops.name_scope(None, 'logits', (logits,)) as scope:
    logits = math_ops.to_float(logits)
    logits_shape = array_ops.shape(logits)
    assert_rank = check_ops.assert_rank(
        logits, 2, data=[logits_shape],
        message='logits shape must be [batch_size, logits_dimension]')
    with ops.control_dependencies([assert_rank]):
      static_shape = logits.shape
      if static_shape is not None:
        dim1 = static_shape[1]
        if (dim1 is not None) and (dim1 != expected_logits_dimension):
          raise ValueError(
              'logits shape must be [batch_size, logits_dimension], got %s.' %
              (static_shape,))
      assert_dimension = check_ops.assert_equal(
          expected_logits_dimension, logits_shape[1], data=[logits_shape],
          message='logits shape must be [batch_size, logits_dimension]')
      with ops.control_dependencies([assert_dimension]):
        return array_ops.identity(logits, name=scope) 

def _check_labels(labels, expected_labels_dimension):
  """Check labels type and shape."""
  with ops.name_scope(None, 'labels', (labels,)) as scope:
    labels = sparse_tensor.convert_to_tensor_or_sparse_tensor(labels)
    if isinstance(labels, sparse_tensor.SparseTensor):
      raise ValueError('SparseTensor labels are not supported.')
    labels_shape = array_ops.shape(labels)
    err_msg = 'labels shape must be [batch_size, {}]'.format(
        expected_labels_dimension)
    assert_rank = check_ops.assert_rank(labels, 2, message=err_msg)
    with ops.control_dependencies([assert_rank]):
      static_shape = labels.shape
      if static_shape is not None:
        dim1 = static_shape[1]
        if (dim1 is not None) and (dim1 != expected_labels_dimension):
          raise ValueError(
              'Mismatched label shape. '
              'Classifier configured with n_classes=%s.  Received %s. '
              'Suggested Fix: check your n_classes argument to the estimator '
              'and/or the shape of your label.' %
              (expected_labels_dimension, dim1))
      assert_dimension = check_ops.assert_equal(
          expected_labels_dimension, labels_shape[1], message=err_msg)
      with ops.control_dependencies([assert_dimension]):
        return array_ops.identity(labels, name=scope) 

def assert_zero_imag_part(x, message=None, name="assert_zero_imag_part"):
  """Returns `Op` that asserts Tensor `x` has no non-zero imaginary parts.

  Args:
    x:  Numeric `Tensor`, real, integer, or complex.
    message:  A string message to prepend to failure message.
    name:  A name to give this `Op`.

  Returns:
    An `Op` that asserts `x` has no entries with modulus zero.
  """
  with ops.name_scope(name, values=[x]):
    x = ops.convert_to_tensor(x, name="x")
    dtype = x.dtype.base_dtype

    if dtype.is_floating:
      return control_flow_ops.no_op()

    zero = ops.convert_to_tensor(0, dtype=dtype.real_dtype)
    return check_ops.assert_equal(zero, math_ops.imag(x), message=message) 

def assert_integer_form(
    x, data=None, summarize=None, message=None, name="assert_integer_form"):
  """Assert that x has integer components (or floats equal to integers).

  Args:
    x: Numeric `Tensor`
    data: The tensors to print out if the condition is `False`. Defaults to
      error message and first few entries of `x` and `y`.
    summarize: Print this many entries of each tensor.
    message: A string to prefix to the default message.
    name: A name for this operation (optional).

  Returns:
    Op raising `InvalidArgumentError` if round(x) != x.
  """

  message = message or "x has non-integer components"
  x = ops.convert_to_tensor(x, name="x")
  casted_x = math_ops.to_int64(x)
  return check_ops.assert_equal(
      x, math_ops.cast(math_ops.round(casted_x), x.dtype),
      data=data, summarize=summarize, message=message, name=name) 

def _check_chol(self, chol):
    """Verify that `chol` is proper."""
    chol = ops.convert_to_tensor(chol, name="chol")
    if not self.verify_pd:
      return chol

    shape = array_ops.shape(chol)
    rank = array_ops.rank(chol)

    is_matrix = check_ops.assert_rank_at_least(chol, 2)
    is_square = check_ops.assert_equal(
        array_ops.gather(shape, rank - 2), array_ops.gather(shape, rank - 1))

    deps = [is_matrix, is_square]
    diag = array_ops.matrix_diag_part(chol)
    deps.append(check_ops.assert_positive(diag))

    return control_flow_ops.with_dependencies(deps, chol) 

def assert_compatible_matrix_dimensions(operator, x):
  """Assert that an argument to solve/matmul has proper domain dimension.

  If `operator.shape[-2:] = [M, N]`, and `x.shape[-2:] = [Q, R]`, then
  `operator.matmul(x)` is defined only if `N = Q`.  This `Op` returns an
  `Assert` that "fires" if this is not the case.  Static checks are already
  done by the base class `LinearOperator`.

  Args:
    operator:  `LinearOperator`.
    x:  `Tensor`.

  Returns:
    `Assert` `Op`.
  """
  # Static checks are done in the base class.  Only tensor asserts here.
  assert_same_dd = check_ops.assert_equal(
      array_ops.shape(x)[-2],
      operator.domain_dimension_tensor(),
      message=("Incompatible matrix dimensions.  "
               "shape[-2] of argument to be the same as this operator"))

  return assert_same_dd 

def assert_zero_imag_part(x, message=None, name="assert_zero_imag_part"):
  """Returns `Op` that asserts Tensor `x` has no non-zero imaginary parts.

  Args:
    x:  Numeric `Tensor`, real, integer, or complex.
    message:  A string message to prepend to failure message.
    name:  A name to give this `Op`.

  Returns:
    An `Op` that asserts `x` has no entries with modulus zero.
  """
  with ops.name_scope(name, values=[x]):
    x = ops.convert_to_tensor(x, name="x")
    dtype = x.dtype.base_dtype

    if dtype.is_floating:
      return control_flow_ops.no_op()

    zero = ops.convert_to_tensor(0, dtype=dtype.real_dtype)
    return check_ops.assert_equal(zero, math_ops.imag(x), message=message) 

def _check_shape(self, shape):
    """Check that the init arg `shape` defines a valid operator."""
    shape = ops.convert_to_tensor(shape, name="shape")
    if not self._verify_pd:
      return shape

    # Further checks are equivalent to verification that this is positive
    # definite.  Why?  Because the further checks simply check that this is a
    # square matrix, and combining the fact that this is square (and thus maps
    # a vector space R^k onto itself), with the behavior of .matmul(), this must
    # be the identity operator.
    rank = array_ops.size(shape)
    assert_matrix = check_ops.assert_less_equal(2, rank)
    with ops.control_dependencies([assert_matrix]):
      last_dim = array_ops.gather(shape, rank - 1)
      second_to_last_dim = array_ops.gather(shape, rank - 2)
      assert_square = check_ops.assert_equal(last_dim, second_to_last_dim)
      return control_flow_ops.with_dependencies([assert_matrix, assert_square],
                                                shape) 

def assert_integer_form(
    x, data=None, summarize=None, message=None, name="assert_integer_form"):
  """Assert that x has integer components (or floats equal to integers).

  Args:
    x: Numeric `Tensor`
    data: The tensors to print out if the condition is `False`. Defaults to
      error message and first few entries of `x` and `y`.
    summarize: Print this many entries of each tensor.
    message: A string to prefix to the default message.
    name: A name for this operation (optional).

  Returns:
    Op raising `InvalidArgumentError` if round(x) != x.
  """

  message = message or "x has non-integer components"
  x = ops.convert_to_tensor(x, name="x")
  casted_x = math_ops.to_int64(x)
  return check_ops.assert_equal(
      x, math_ops.cast(math_ops.round(casted_x), x.dtype),
      data=data, summarize=summarize, message=message, name=name) 

def _check_shape(self, shape):
    """Check that the init arg `shape` defines a valid operator."""
    shape = ops.convert_to_tensor(shape, name="shape")
    if not self._verify_pd:
      return shape

    # Further checks are equivalent to verification that this is positive
    # definite.  Why?  Because the further checks simply check that this is a
    # square matrix, and combining the fact that this is square (and thus maps
    # a vector space R^k onto itself), with the behavior of .matmul(), this must
    # be the identity operator.
    rank = array_ops.size(shape)
    assert_matrix = check_ops.assert_less_equal(2, rank)
    with ops.control_dependencies([assert_matrix]):
      last_dim = array_ops.gather(shape, rank - 1)
      second_to_last_dim = array_ops.gather(shape, rank - 2)
      assert_square = check_ops.assert_equal(last_dim, second_to_last_dim)
      return control_flow_ops.with_dependencies([assert_matrix, assert_square],
                                                shape) 

def _batch_size_checks(self, batch_size, error_message):
        return [check_ops.assert_equal(batch_size, 
            attention_mechanism.batch_size,
            message=error_message)
                for attention_mechanism in self._attention_mechanisms] 

def assert_integer_form(
    x, data=None, summarize=None, message=None,
    int_dtype=None, name="assert_integer_form"):
  """Assert that x has integer components (or floats equal to integers).

  Args:
    x: Floating-point `Tensor`
    data: The tensors to print out if the condition is `False`. Defaults to
      error message and first few entries of `x` and `y`.
    summarize: Print this many entries of each tensor.
    message: A string to prefix to the default message.
    int_dtype: A `tf.dtype` used to cast the float to. The default (`None`)
      implies the smallest possible signed int will be used for casting.
    name: A name for this operation (optional).

  Returns:
    Op raising `InvalidArgumentError` if `cast(x, int_dtype) != x`.
  """
  with ops.name_scope(name, values=[x, data]):
    x = ops.convert_to_tensor(x, name="x")
    if x.dtype.is_integer:
      return control_flow_ops.no_op()
    message = message or "{} has non-integer components".format(x.op.name)
    if int_dtype is None:
      try:
        int_dtype = {
            dtypes.float16: dtypes.int16,
            dtypes.float32: dtypes.int32,
            dtypes.float64: dtypes.int64,
        }[x.dtype.base_dtype]
      except KeyError:
        raise TypeError("Unrecognized type {}".format(x.dtype.name))
    return check_ops.assert_equal(
        x, math_ops.cast(math_ops.cast(x, int_dtype), x.dtype),
        data=data, summarize=summarize, message=message, name=name) 

def _maybe_assert_valid_sample(self, counts):
    """Check counts for proper shape, values, then return tensor version."""
    if not self.validate_args:
      return counts
    counts = distribution_util.embed_check_nonnegative_integer_form(counts)
    return control_flow_ops.with_dependencies([
        check_ops.assert_equal(
            self.total_count, math_ops.reduce_sum(counts, -1),
            message="counts last-dimension must sum to `self.total_count`"),
    ], counts) 

def _batch_size_checks(self, batch_size, error_message):
		return [check_ops.assert_equal(batch_size,
		  self._attention_mechanism.batch_size,
		  message=error_message)] 

def _batch_size_checks(self, batch_size, error_message):
    return [check_ops.assert_equal(batch_size,
                                   attention_mechanism.batch_size,
                                   message=error_message)
            for attention_mechanism in self._attention_mechanisms] 

def _batch_size_checks(self, batch_size, error_message):
		return [check_ops.assert_equal(batch_size,
		  self._attention_mechanism.batch_size,
		  message=error_message)] 

def _batch_size_checks(self, batch_size, error_message):
		return [check_ops.assert_equal(batch_size,
		  self._attention_mechanism.batch_size,
		  message=error_message)] 

def assert_close(
    x, y, data=None, summarize=None, message=None, name="assert_close"):
  """Assert that that x and y are within machine epsilon of each other.

  Args:
    x: Numeric `Tensor`
    y: Numeric `Tensor`
    data: The tensors to print out if the condition is `False`. Defaults to
      error message and first few entries of `x` and `y`.
    summarize: Print this many entries of each tensor.
    message: A string to prefix to the default message.
    name: A name for this operation (optional).

  Returns:
    Op raising `InvalidArgumentError` if |x - y| > machine epsilon.
  """
  message = message or ""
  x = ops.convert_to_tensor(x, name="x")
  y = ops.convert_to_tensor(y, name="y")

  if data is None:
    data = [
        message,
        "Condition x ~= y did not hold element-wise: x = ", x.name, x, "y = ",
        y.name, y
    ]

  if x.dtype.is_integer:
    return check_ops.assert_equal(
        x, y, data=data, summarize=summarize, message=message, name=name)

  with ops.name_scope(name, "assert_close", [x, y, data]):
    tol = np.finfo(x.dtype.as_numpy_dtype).eps
    condition = math_ops.reduce_all(math_ops.less_equal(math_ops.abs(x-y), tol))
    return control_flow_ops.Assert(
        condition, data, summarize=summarize) 

def _kl_vmf_uniform(vmf, hyu, name=None):
    with ops.control_dependencies([check_ops.assert_equal(vmf.loc.shape[-1] - 1, hyu.dim)]):
        with ops.name_scope(name, "_kl_vmf_uniform", [vmf.scale]):
            return - vmf.entropy() + hyu.entropy() 

def _batch_size_checks(self, batch_size, error_message):
        return [check_ops.assert_equal(batch_size,
                                       attention_mechanism.batch_size,
                                       message=error_message)
                for attention_mechanism in self._attention_mechanisms] 

def _batch_size_checks(self, batch_size, error_message):
    return [check_ops.assert_equal(batch_size,
      self._attention_mechanism.batch_size,
      message=error_message)] 

def _forward(self, x):
    if self._static_event_ndims == 0:
      return math_ops.square(x)
    if self.validate_args:
      is_matrix = check_ops.assert_rank_at_least(x, 2)
      shape = array_ops.shape(x)
      is_square = check_ops.assert_equal(shape[-2], shape[-1])
      x = control_flow_ops.with_dependencies([is_matrix, is_square], x)
    # For safety, explicitly zero-out the upper triangular part.
    x = array_ops.matrix_band_part(x, -1, 0)
    return math_ops.batch_matmul(x, x, adj_y=True) 

def _assert_valid_mu(self, mu):
    """Return `mu` after validity checks and possibly with assertations."""
    cov = self._cov
    if mu.dtype != cov.dtype:
      raise TypeError(
          "mu and cov must have the same dtype.  Found mu.dtype = %s, "
          "cov.dtype = %s" % (mu.dtype, cov.dtype))

    # Try to validate with static checks.
    mu_shape = mu.get_shape()
    cov_shape = cov.get_shape()
    if mu_shape.is_fully_defined() and cov_shape.is_fully_defined():
      if mu_shape != cov_shape[:-1]:
        raise ValueError(
            "mu.shape and cov.shape[:-1] should match.  Found: mu.shape=%s, "
            "cov.shape=%s" % (mu_shape, cov_shape))
      else:
        return mu

    # Static checks could not be run, so possibly do dynamic checks.
    if not self.validate_args:
      return mu
    else:
      assert_same_rank = check_ops.assert_equal(
          array_ops.rank(mu) + 1,
          cov.rank(),
          data=["mu should have rank 1 less than cov.  Found: rank(mu) = ",
                array_ops.rank(mu), " rank(cov) = ", cov.rank()],
      )
      with ops.control_dependencies([assert_same_rank]):
        assert_same_shape = check_ops.assert_equal(
            array_ops.shape(mu),
            cov.vector_shape(),
            data=["mu.shape and cov.shape[:-1] should match.  "
                  "Found: shape(mu) = "
                  , array_ops.shape(mu), " shape(cov) = ", cov.shape()],
        )
        return control_flow_ops.with_dependencies([assert_same_shape], mu) 

def _maybe_assert_valid_sample(self, counts):
    """Check counts for proper shape, values, then return tensor version."""
    if not self.validate_args:
      return counts

    counts = distribution_util.embed_check_nonnegative_discrete(
        counts, check_integer=True)
    return control_flow_ops.with_dependencies([
        check_ops.assert_equal(
            self.total_count, math_ops.reduce_sum(counts, -1),
            message="counts must sum to `self.total_count`"),
    ], counts) 

def _batch_size_checks(self, batch_size, error_message):
    return [check_ops.assert_equal(batch_size,
                                   attention_mechanism.batch_size,
                                   message=error_message)
            for attention_mechanism in self._attention_mechanisms]