Python tensorflow.python.ops.math_ops.not_equal() Examples

def _PowGrad(op, grad):
  """Returns grad * (y*x^(y-1), z*log(x))."""
  x = op.inputs[0]
  y = op.inputs[1]
  z = op.outputs[0]
  sx = array_ops.shape(x)
  sy = array_ops.shape(y)
  rx, ry = gen_array_ops._broadcast_gradient_args(sx, sy)
  x = math_ops.conj(x)
  y = math_ops.conj(y)
  z = math_ops.conj(z)
  gx = array_ops.reshape(
      math_ops.reduce_sum(grad * y * math_ops.pow(x, y - 1), rx), sx)
  # Avoid false singularity at x = 0
  if x.dtype.is_complex:
    # real(x) < 0 is fine for the complex case
    log_x = array_ops.where(
        math_ops.not_equal(x, 0), math_ops.log(x), array_ops.zeros_like(x))
  else:
    # There's no sensible real value to return if x < 0, so return 0
    log_x = array_ops.where(x > 0, math_ops.log(x), array_ops.zeros_like(x))
  gy = array_ops.reshape(math_ops.reduce_sum(grad * z * log_x, ry), sy)
  return gx, gy 

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 _PowGrad(op, grad):
  """Returns grad * (y*x^(y-1), z*log(x))."""
  x = op.inputs[0]
  y = op.inputs[1]
  z = op.outputs[0]
  sx = array_ops.shape(x)
  sy = array_ops.shape(y)
  rx, ry = gen_array_ops._broadcast_gradient_args(sx, sy)
  x = math_ops.conj(x)
  y = math_ops.conj(y)
  z = math_ops.conj(z)
  gx = array_ops.reshape(
      math_ops.reduce_sum(grad * y * math_ops.pow(x, y - 1), rx), sx)
  # Avoid false singularity at x = 0
  if x.dtype.is_complex:
    # real(x) < 0 is fine for the complex case
    log_x = array_ops.where(
        math_ops.not_equal(x, 0), math_ops.log(x), array_ops.zeros_like(x))
  else:
    # There's no sensible real value to return if x < 0, so return 0
    log_x = array_ops.where(x > 0, math_ops.log(x), array_ops.zeros_like(x))
  gy = array_ops.reshape(math_ops.reduce_sum(grad * z * log_x, ry), sy)
  return gx, gy 

def _PowGrad(op, grad):
  """Returns grad * (y*x^(y-1), z*log(x))."""
  x = op.inputs[0]
  y = op.inputs[1]
  z = op.outputs[0]
  sx = array_ops.shape(x)
  sy = array_ops.shape(y)
  rx, ry = gen_array_ops._broadcast_gradient_args(sx, sy)
  x = math_ops.conj(x)
  y = math_ops.conj(y)
  z = math_ops.conj(z)
  gx = array_ops.reshape(
      math_ops.reduce_sum(grad * y * math_ops.pow(x, y - 1), rx), sx)
  # Avoid false singularity at x = 0
  if x.dtype.is_complex:
    # real(x) < 0 is fine for the complex case
    log_x = array_ops.where(
        math_ops.not_equal(x, 0), math_ops.log(x), array_ops.zeros_like(x))
  else:
    # There's no sensible real value to return if x < 0, so return 0
    log_x = array_ops.where(x > 0, math_ops.log(x), array_ops.zeros_like(x))
  gy = array_ops.reshape(math_ops.reduce_sum(grad * z * log_x, ry), sy)
  return gx, gy 

def _PowGrad(op, grad):
  """Returns grad * (y*x^(y-1), z*log(x))."""
  x = op.inputs[0]
  y = op.inputs[1]
  z = op.outputs[0]
  sx = array_ops.shape(x)
  sy = array_ops.shape(y)
  rx, ry = gen_array_ops._broadcast_gradient_args(sx, sy)
  x = math_ops.conj(x)
  y = math_ops.conj(y)
  z = math_ops.conj(z)
  gx = array_ops.reshape(
      math_ops.reduce_sum(grad * y * math_ops.pow(x, y - 1), rx), sx)
  # Avoid false singularity at x = 0
  if x.dtype.is_complex:
    # real(x) < 0 is fine for the complex case
    log_x = array_ops.where(
        math_ops.not_equal(x, 0), math_ops.log(x), array_ops.zeros_like(x))
  else:
    # There's no sensible real value to return if x < 0, so return 0
    log_x = array_ops.where(x > 0, math_ops.log(x), array_ops.zeros_like(x))
  gy = array_ops.reshape(math_ops.reduce_sum(grad * z * log_x, ry), sy)
  return gx, gy 

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 dense_to_sparse_tensor(dense_tensor, ignore_value=None):
  """Converts dense `Tensor` to `SparseTensor`, dropping `ignore_value` cells.

  Args:
    dense_tensor: A `Tensor`.
    ignore_value: Entries in `dense_tensor` equal to this value will be
      absent from the return `SparseTensor`. If `None`, default value of
      `dense_tensor` dtype will be used (e.g. '' for `str`, 0 for `int`).

  Returns:
    A `SparseTensor` with the same shape as `dense_tensor`.

  Raises:
    ValueError: when `dense_tensor`'s rank is `None`.
  """
  with ops.name_scope("DenseToSparseTensor"):
    dense_tensor = ops.convert_to_tensor(dense_tensor)
    ignore_value = _ignore_value_tensor(dense_tensor.dtype, ignore_value)
    indices = array_ops.where(
        math_ops.not_equal(dense_tensor, ignore_value), name="indices")
    return sparse_tensor.SparseTensor(
        indices=indices,
        values=array_ops.gather_nd(dense_tensor, indices, name="values"),
        dense_shape=array_ops.shape(
            dense_tensor, out_type=dtypes.int64, name="dense_shape")) 

def _PowGrad(op, grad):
  """Returns grad * (y*x^(y-1), z*log(x))."""
  x = op.inputs[0]
  y = op.inputs[1]
  z = op.outputs[0]
  sx = array_ops.shape(x)
  sy = array_ops.shape(y)
  rx, ry = gen_array_ops._broadcast_gradient_args(sx, sy)
  x = math_ops.conj(x)
  y = math_ops.conj(y)
  z = math_ops.conj(z)
  gx = array_ops.reshape(
      math_ops.reduce_sum(grad * y * math_ops.pow(x, y - 1), rx), sx)
  # Avoid false singularity at x = 0
  if x.dtype.is_complex:
    # real(x) < 0 is fine for the complex case
    log_x = math_ops.select(
        math_ops.not_equal(x, 0), math_ops.log(x), array_ops.zeros_like(x))
  else:
    # There's no sensible real value to return if x < 0, so return 0
    log_x = math_ops.select(x > 0, math_ops.log(x), array_ops.zeros_like(x))
  gy = array_ops.reshape(
      math_ops.reduce_sum(grad * z * log_x, ry), sy)
  return gx, gy 

def setUp(self):
    super(CoreBinaryOpsTest, self).setUp()

    self.x_probs_broadcast_tensor = array_ops.reshape(
        self.x_probs_lt.tensor, [self.x_size, 1, self.probs_size])

    self.channel_probs_broadcast_tensor = array_ops.reshape(
        self.channel_probs_lt.tensor, [1, self.channel_size, self.probs_size])

    # == and != are not element-wise for tf.Tensor, so they shouldn't be
    # elementwise for LabeledTensor, either.
    self.ops = [
        ('add', operator.add, math_ops.add, core.add),
        ('sub', operator.sub, math_ops.subtract, core.sub),
        ('mul', operator.mul, math_ops.multiply, core.mul),
        ('div', operator.truediv, math_ops.div, core.div),
        ('mod', operator.mod, math_ops.mod, core.mod),
        ('pow', operator.pow, math_ops.pow, core.pow_function),
        ('equal', None, math_ops.equal, core.equal),
        ('less', operator.lt, math_ops.less, core.less),
        ('less_equal', operator.le, math_ops.less_equal, core.less_equal),
        ('not_equal', None, math_ops.not_equal, core.not_equal),
        ('greater', operator.gt, math_ops.greater, core.greater),
        ('greater_equal', operator.ge, math_ops.greater_equal,
         core.greater_equal),
    ]
    self.test_lt_1 = self.x_probs_lt
    self.test_lt_2 = self.channel_probs_lt
    self.test_lt_1_broadcast = self.x_probs_broadcast_tensor
    self.test_lt_2_broadcast = self.channel_probs_broadcast_tensor
    self.broadcast_axes = [self.a0, self.a1, self.a3] 

def _check_multiple_of(value, multiple_of):
  """Checks that value `value` is a non-zero multiple of `multiple_of`.

  Args:
    value: an int32 scalar Tensor.
    multiple_of: an int or int32 scalar Tensor.

  Returns:
    new_value: an int32 scalar Tensor matching `value`, but which includes an
      assertion that `value` is a multiple of `multiple_of`.
  """
  assert isinstance(value, ops.Tensor)
  with ops.control_dependencies([
      control_flow_ops.Assert(
          math_ops.logical_and(
              math_ops.equal(math_ops.mod(value, multiple_of), 0),
              math_ops.not_equal(value, 0)), [
                  string_ops.string_join([
                      "Tensor %s should be a multiple of: " % value.name,
                      string_ops.as_string(multiple_of), ", but saw value: ",
                      string_ops.as_string(value),
                      ". Consider setting pad=True."
                  ])
              ])
  ]):
    new_value = array_ops.identity(value, name="multiple_of_checked")
    return new_value 

def not_equal(x, y):
  """Element-wise inequality between two tensors.

  Arguments:
      x: Tensor or variable.
      y: Tensor or variable.

  Returns:
      A bool tensor.
  """
  return math_ops.not_equal(x, y) 

def test_forward_rel_ops():
    t1 = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
    t2 = np.array([[9, 8, 7], [6, 5, 4], [3, 2, 1]])
    _test_forward_rel_op([t1, t2], math_ops.less)
    _test_forward_rel_op([t1, t2], math_ops.greater)
    _test_forward_rel_op([t1, t2], math_ops.less_equal)
    _test_forward_rel_op([t1, t2], math_ops.greater_equal)
    _test_forward_rel_op([t1, t2], math_ops.equal)
    _test_forward_rel_op([t1, t2], math_ops.not_equal)

#######################################################################
# ExpandDims
# ---------- 

def _test_not_equal(data):
    """ One iteration of not_equal"""
    return _test_elemwise(math_ops.not_equal, data)
#######################################################################
# Squared_difference
# ------------------ 

def compute_mask(self, inputs, mask=None):
        if not self.mask_zero:
            return None

        return math_ops.not_equal(inputs, 0) 

def compute_mask(self, inputs, mask=None):
        if not self.mask_zero:
            return None

        return math_ops.not_equal(inputs, 0) 

def finalize(self, output, final_state, sequence_lengths):
    # Gather according to beam search result
    # now predicted_ids is [M, N/B]
    predicted_ids = beam_search.gather_tree(output.predicted_ids,
                                            output.beam_parent_ids)
    # TODO(Shancheng): pay attention
    beam_width = output.beam_parent_ids.get_shape().as_list()
    parent_ids = tf.concat([tf.zeros([1, beam_width[-1]], dtype=tf.int32),
                            output.beam_parent_ids[:-1, :]], 0)
    # now logits is [M, N/B, C]
    logits = beam_search.gather_tree(output.logits,
                                     parent_ids)
    # now attention scores is [M, N/B, L, H, W]
    attention_scores = beam_search.gather_tree(output.attention_scores,
                                               parent_ids)
    # orginal length is the length of ungathered logits
    sequence_lengths = math_ops.not_equal(predicted_ids, self.end_token)
    sequence_lengths = tf.to_int32(sequence_lengths)
    sequence_lengths = tf.reduce_sum(sequence_lengths, axis=0) + 1

    # choose the top score item
    predicted_ids = predicted_ids[:, 0:1]
    logits = logits[:, 0:1]
    attention_scores = attention_scores[:, 0:1]
    # mask out
    length = sequence_lengths[0]
    logits = logits[0:length, :]
    attention_scores = attention_scores[0:length, :]

    final_outputs = DecoderOutput(
        logits=self._padding(logits),
        predicted_ids=self._padding(predicted_ids),
        attention_scores=attention_scores)

    return final_outputs, final_state 

def _check_multiple_of(value, multiple_of):
  """Checks that value `value` is a non-zero multiple of `multiple_of`.

  Args:
    value: an int32 scalar Tensor.
    multiple_of: an int or int32 scalar Tensor.

  Returns:
    new_value: an int32 scalar Tensor matching `value`, but which includes an
      assertion that `value` is a multiple of `multiple_of`.
  """
  assert isinstance(value, ops.Tensor)
  with ops.control_dependencies([
      control_flow_ops.Assert(
          math_ops.logical_and(
              math_ops.equal(math_ops.mod(value, multiple_of), 0),
              math_ops.not_equal(value, 0)),
          [string_ops.string_join(
              ["Tensor %s should be a multiple of: " % value.name,
               string_ops.as_string(multiple_of),
               ", but saw value: ",
               string_ops.as_string(value),
               ". Consider setting pad=True."])])]):
    new_value = array_ops.identity(
        value, name="multiple_of_checked")
    return new_value 

def test_forward_rel_ops():
    t1 = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
    t2 = np.array([[9, 8, 7], [6, 5, 4], [3, 2, 1]])
    _test_forward_rel_op([t1, t2], math_ops.less)
    _test_forward_rel_op([t1, t2], math_ops.greater)
    _test_forward_rel_op([t1, t2], math_ops.less_equal)
    _test_forward_rel_op([t1, t2], math_ops.greater_equal)
    _test_forward_rel_op([t1, t2], math_ops.equal)
    _test_forward_rel_op([t1, t2], math_ops.not_equal)


#######################################################################
# Main
# ---- 

def setUp(self):
    super(CoreBinaryOpsTest, self).setUp()

    self.x_probs_broadcast_tensor = array_ops.reshape(
        self.x_probs_lt.tensor, [self.x_size, 1, self.probs_size])

    self.channel_probs_broadcast_tensor = array_ops.reshape(
        self.channel_probs_lt.tensor, [1, self.channel_size, self.probs_size])

    # == and != are not element-wise for tf.Tensor, so they shouldn't be
    # elementwise for LabeledTensor, either.
    self.ops = [
        ('add', operator.add, math_ops.add, core.add),
        ('sub', operator.sub, math_ops.subtract, core.sub),
        ('mul', operator.mul, math_ops.multiply, core.mul),
        ('div', operator.truediv, math_ops.div, core.div),
        ('mod', operator.mod, math_ops.mod, core.mod),
        ('pow', operator.pow, math_ops.pow, core.pow_function),
        ('equal', None, math_ops.equal, core.equal),
        ('less', operator.lt, math_ops.less, core.less),
        ('less_equal', operator.le, math_ops.less_equal, core.less_equal),
        ('not_equal', None, math_ops.not_equal, core.not_equal),
        ('greater', operator.gt, math_ops.greater, core.greater),
        ('greater_equal', operator.ge, math_ops.greater_equal,
         core.greater_equal),
    ]
    self.test_lt_1 = self.x_probs_lt
    self.test_lt_2 = self.channel_probs_lt
    self.test_lt_1_broadcast = self.x_probs_broadcast_tensor
    self.test_lt_2_broadcast = self.channel_probs_broadcast_tensor
    self.broadcast_axes = [self.a0, self.a1, self.a3] 

def _check_multiple_of(value, multiple_of):
  """Checks that value `value` is a non-zero multiple of `multiple_of`.

  Args:
    value: an int32 scalar Tensor.
    multiple_of: an int or int32 scalar Tensor.

  Returns:
    new_value: an int32 scalar Tensor matching `value`, but which includes an
      assertion that `value` is a multiple of `multiple_of`.
  """
  assert isinstance(value, ops.Tensor)
  with ops.control_dependencies([
      control_flow_ops.Assert(
          math_ops.logical_and(
              math_ops.equal(math_ops.mod(value, multiple_of), 0),
              math_ops.not_equal(value, 0)), [
                  string_ops.string_join([
                      "Tensor %s should be a multiple of: " % value.name,
                      string_ops.as_string(multiple_of), ", but saw value: ",
                      string_ops.as_string(value),
                      ". Consider setting pad=True."
                  ])
              ])
  ]):
    new_value = array_ops.identity(value, name="multiple_of_checked")
    return new_value 

def _check_multiple_of(value, multiple_of):
  """Checks that value `value` is a non-zero multiple of `multiple_of`.

  Args:
    value: an int32 scalar Tensor.
    multiple_of: an int or int32 scalar Tensor.

  Returns:
    new_value: an int32 scalar Tensor matching `value`, but which includes an
      assertion that `value` is a multiple of `multiple_of`.
  """
  assert isinstance(value, ops.Tensor)
  with ops.control_dependencies([
      control_flow_ops.Assert(
          math_ops.logical_and(
              math_ops.equal(math_ops.mod(value, multiple_of), 0),
              math_ops.not_equal(value, 0)), [
                  string_ops.string_join([
                      "Tensor %s should be a multiple of: " % value.name,
                      string_ops.as_string(multiple_of), ", but saw value: ",
                      string_ops.as_string(value),
                      ". Consider setting pad=True."
                  ])
              ])
  ]):
    new_value = array_ops.identity(value, name="multiple_of_checked")
    return new_value 

def not_equal(x, y):
  """Element-wise inequality between two tensors.

  Arguments:
      x: Tensor or variable.
      y: Tensor or variable.

  Returns:
      A bool tensor.
  """
  return math_ops.not_equal(x, y) 

def dense_to_sparse_tensor(dense_tensor, ignore_value=None):
  """Converts a dense Tensor to a SparseTensor, dropping ignore_value cells.

  Args:
    dense_tensor: A `Tensor`.
    ignore_value: Entries in `dense_tensor` equal to this value will be
      absent from the return `SparseTensor`. If `None`, default value of
      dense_tensor's dtype will be used (e.g. '' for `str`, 0 for `int`).

  Returns:
    A `SparseTensor` with the same shape as `dense_tensor`.

  Raises:
    ValueError: when `dense_tensor`'s rank is `None`.
  """
  with ops.name_scope("DenseToSparseTensor"):
    dense_t = ops.convert_to_tensor(dense_tensor)
    if dense_t.get_shape().ndims is None:
      # TODO(b/32318825): Implement dense_to_sparse_tensor for undefined rank.
      raise ValueError("dense_tensor.get_shape() should be defined, got None.")
    if ignore_value is None:
      if dense_t.dtype == dtypes.string:
        # Exception due to TF strings are converted to numpy objects by default.
        ignore_value = ""
      else:
        ignore_value = dense_t.dtype.as_numpy_dtype()
    dense_shape = math_ops.cast(array_ops.shape(dense_t), dtypes.int64)
    indices = array_ops.where(
        math_ops.not_equal(dense_t, math_ops.cast(ignore_value, dense_t.dtype)))
    index_dims = len(dense_t.get_shape())
    # Flattens the tensor and indices for use with gather.
    flat_tensor = array_ops.reshape(dense_t, [-1])
    flat_indices = indices[:, index_dims - 1]
    # Computes the correct flattened indices for 2d (or higher) tensors.
    if index_dims > 1:
      higher_dims = indices[:, :index_dims - 1]
      shape_multipliers = array_ops.pack(
          _multiplier_helper(array_ops.unpack(dense_shape)[1:]))
      offsets = math_ops.reduce_sum(
          math_ops.mul(higher_dims, shape_multipliers), reduction_indices=[1])
      flat_indices = math_ops.add(flat_indices, offsets)
    values = array_ops.gather(flat_tensor, flat_indices)
    return sparse_tensor.SparseTensor(indices, values, dense_shape) 

def dense_to_sparse_tensor(dense_tensor, ignore_value=None):
  """Converts a dense Tensor to a SparseTensor, dropping ignore_value cells.

  Args:
    dense_tensor: A `Tensor`.
    ignore_value: Entries in `dense_tensor` equal to this value will be
      absent from the return `SparseTensor`. If `None`, default value of
      dense_tensor's dtype will be used (e.g. '' for `str`, 0 for `int`).

  Returns:
    A `SparseTensor` with the same shape as `dense_tensor`.

  Raises:
    ValueError: when `dense_tensor`'s rank is `None`.
  """
  with ops.name_scope("DenseToSparseTensor"):
    dense_t = ops.convert_to_tensor(dense_tensor)
    if dense_t.get_shape().ndims is None:
      # TODO(b/32318825): Implement dense_to_sparse_tensor for undefined rank.
      raise ValueError("dense_tensor.get_shape() should be defined, got None.")
    if ignore_value is None:
      if dense_t.dtype == dtypes.string:
        # Exception due to TF strings are converted to numpy objects by default.
        ignore_value = ""
      else:
        ignore_value = dense_t.dtype.as_numpy_dtype()
    dense_shape = math_ops.cast(array_ops.shape(dense_t), dtypes.int64)
    indices = array_ops.where(
        math_ops.not_equal(dense_t, math_ops.cast(ignore_value, dense_t.dtype)))
    index_dims = len(dense_t.get_shape())
    # Flattens the tensor and indices for use with gather.
    flat_tensor = array_ops.reshape(dense_t, [-1])
    flat_indices = indices[:, index_dims - 1]
    # Computes the correct flattened indices for 2d (or higher) tensors.
    if index_dims > 1:
      higher_dims = indices[:, :index_dims - 1]
      shape_multipliers = array_ops.stack(
          _multiplier_helper(array_ops.unstack(dense_shape)[1:]))
      offsets = math_ops.reduce_sum(
          math_ops.multiply(higher_dims, shape_multipliers),
          reduction_indices=[1])
      flat_indices = math_ops.add(flat_indices, offsets)
    values = array_ops.gather(flat_tensor, flat_indices)
    return sparse_tensor.SparseTensor(indices, values, dense_shape) 

def decode_image(contents, channels=None, name=None):
  """Convenience function for `decode_gif`, `decode_jpeg`, and `decode_png`.
  Detects whether an image is a GIF, JPEG, or PNG, and performs the appropriate 
  operation to convert the input bytes `string` into a `Tensor` of type `uint8`.

  Note: `decode_gif` returns a 4-D array `[num_frames, height, width, 3]`, as 
  opposed to `decode_jpeg` and `decode_png`, which return 3-D arrays 
  `[height, width, num_channels]`. Make sure to take this into account when 
  constructing your graph if you are intermixing GIF files with JPEG and/or PNG 
  files.

  Args:
    contents: 0-D `string`. The encoded image bytes.
    channels: An optional `int`. Defaults to `0`. Number of color channels for 
      the decoded image.
    name: A name for the operation (optional)
    
  Returns:
    `Tensor` with type `uint8` with shape `[height, width, num_channels]` for 
      JPEG and PNG images and shape `[num_frames, height, width, 3]` for GIF 
      images.
  """
  with ops.name_scope(name, 'decode_image') as scope:
    if channels not in (None, 0, 1, 3):
      raise ValueError('channels must be in (None, 0, 1, 3)')
    substr = string_ops.substr(contents, 0, 4)

    def _gif():
      # Create assert op to check that bytes are GIF decodable
      is_gif = math_ops.equal(substr, b'\x47\x49\x46\x38', name='is_gif')
      decode_msg = 'Unable to decode bytes as JPEG, PNG, or GIF'
      assert_decode = control_flow_ops.Assert(is_gif, [decode_msg])
      # Create assert to make sure that channels is not set to 1
      # Already checked above that channels is in (None, 0, 1, 3)
      gif_channels = 0 if channels is None else channels
      good_channels = math_ops.not_equal(gif_channels, 1, name='check_channels')
      channels_msg = 'Channels must be in (None, 0, 3) when decoding GIF images'
      assert_channels = control_flow_ops.Assert(good_channels, [channels_msg])
      with ops.control_dependencies([assert_decode, assert_channels]):
        return gen_image_ops.decode_gif(contents)

    def _png():
      return gen_image_ops.decode_png(contents, channels)

    def check_png():
      is_png = math_ops.equal(substr, b'\211PNG', name='is_png')
      return control_flow_ops.cond(is_png, _png, _gif, name='cond_png')

    def _jpeg():
      return gen_image_ops.decode_jpeg(contents, channels)

    is_jpeg = math_ops.equal(substr, b'\xff\xd8\xff\xe0', name='is_jpeg')
    return control_flow_ops.cond(is_jpeg, _jpeg, check_png, name='cond_jpeg') 

def lookup(self, keys, name=None):
    """Looks up `keys` in the table, outputs the corresponding values.

    It assigns out-of-vocabulary keys to buckets based in their hashes.

    Args:
      keys: Keys to look up. May be either a `SparseTensor` or dense `Tensor`.
      name: Optional name for the op.

    Returns:
      A `SparseTensor` if keys are sparse, otherwise a dense `Tensor`.

    Raises:
      TypeError: when `keys` doesn't match the table key data type.
    """
    if keys.dtype != self._key_dtype:
      raise TypeError("Signature mismatch. Keys must be dtype %s, got %s." %
                      (self._key_dtype, keys.dtype))
    values = keys
    if isinstance(keys, sparse_tensor.SparseTensor):
      values = keys.values
    if self._table and (self._table.key_dtype.base_dtype == dtypes.int64):
      values = math_ops.to_int64(values)

    if self._num_oov_buckets == 0:
      ids = self._table.lookup(values, name=name)
    else:
      # TODO(yleon): Consider moving this functionality to its own kernel.
      with ops.name_scope(name, "%s_Lookup" % self.name) as scope:
        str_to_hash_bucket = self._get_string_to_hash_bucket_fn(
            self._hasher_spec)
        buckets = str_to_hash_bucket(
            _as_string(values),
            num_buckets=self._num_oov_buckets,
            name="hash_bucket")
        if self._table:
          ids = self._table.lookup(values)
          buckets = math_ops.add(buckets, self._table.size())
          is_id_non_default = math_ops.not_equal(ids, self._table.default_value)
          ids = array_ops.where(is_id_non_default, ids, buckets, name=scope)
        else:
          ids = buckets
    if isinstance(keys, sparse_tensor.SparseTensor):
      return sparse_tensor.SparseTensor(keys.indices, ids, keys.dense_shape)
    return ids 

def lookup(self, keys, name=None):
    """Looks up `keys` in the table, outputs the corresponding values.

    It assigns out-of-vocabulary keys to buckets based in their hashes.

    Args:
      keys: Keys to look up. May be either a `SparseTensor` or dense `Tensor`.
      name: Optional name for the op.

    Returns:
      A `SparseTensor` if keys are sparse, otherwise a dense `Tensor`.

    Raises:
      TypeError: when `keys` doesn't match the table key data type.
    """
    if keys.dtype != self._key_dtype:
      raise TypeError("Signature mismatch. Keys must be dtype %s, got %s." %
                      (self._key_dtype, keys.dtype))
    values = keys
    if isinstance(keys, sparse_tensor.SparseTensor):
      values = keys.values
    if self._table and (self._table.key_dtype.base_dtype == dtypes.int64):
      values = math_ops.to_int64(values)

    if self._num_oov_buckets == 0:
      ids = self._table.lookup(values, name=name)
    else:
      # TODO(yleon): Consider moving this functionality to its own kernel.
      with ops.name_scope(name, "%s_Lookup" % self.name) as scope:
        str_to_hash_bucket = self._get_string_to_hash_bucket_fn(
            self._hasher_spec)
        buckets = str_to_hash_bucket(
            _as_string(values),
            num_buckets=self._num_oov_buckets,
            name="hash_bucket")
        if self._table:
          ids = self._table.lookup(values)
          buckets = math_ops.add(buckets, self._table.size())
          is_id_non_default = math_ops.not_equal(ids, self._table.default_value)
          ids = array_ops.where(is_id_non_default, ids, buckets, name=scope)
        else:
          ids = buckets
    if isinstance(keys, sparse_tensor.SparseTensor):
      return sparse_tensor.SparseTensor(keys.indices, ids, keys.dense_shape)
    return ids 

def _to_sparse_input(input_tensor, ignore_value=None):
  """Converts a `Tensor` to a `SparseTensor`, dropping ignore_value cells.

  If `input_tensor` is already a `SparseTensor`, just return it.

  Args:
    input_tensor: A string or integer `Tensor`.
    ignore_value: Entries in `dense_tensor` equal to this value will be
      absent from the resulting `SparseTensor`. If `None`, default value of
      `dense_tensor`'s dtype will be used ('' for `str`, -1 for `int`).

  Returns:
    A `SparseTensor` with the same shape as `input_tensor`.

  Raises:
    ValueError: when `input_tensor`'s rank is `None`.
  """
  input_tensor = sparse_tensor_lib.convert_to_tensor_or_sparse_tensor(
      input_tensor)
  if isinstance(input_tensor, sparse_tensor_lib.SparseTensor):
    return input_tensor
  with ops.name_scope(None, 'to_sparse_input', (input_tensor, ignore_value,)):
    input_rank = input_tensor.get_shape().ndims
    if input_rank is None:
      # TODO(b/32318825): Implement dense_to_sparse_tensor for undefined rank.
      raise ValueError('Undefined input_tensor shape.')
    if ignore_value is None:
      ignore_value = '' if input_tensor.dtype == dtypes.string else -1
    dense_shape = math_ops.cast(array_ops.shape(input_tensor), dtypes.int64)
    indices = array_ops.where(math_ops.not_equal(
        input_tensor, math_ops.cast(ignore_value, input_tensor.dtype)))
    # Flattens the tensor and indices for use with gather.
    flat_tensor = array_ops.reshape(input_tensor, [-1])
    flat_indices = indices[:, input_rank - 1]
    # Computes the correct flattened indices for 2d (or higher) tensors.
    if input_rank > 1:
      higher_dims = indices[:, :input_rank - 1]
      shape_offsets = array_ops.stack(
          _shape_offsets(array_ops.unstack(dense_shape)[1:]))
      offsets = math_ops.reduce_sum(
          math_ops.multiply(higher_dims, shape_offsets),
          reduction_indices=[1])
      flat_indices = math_ops.add(flat_indices, offsets)
    values = array_ops.gather(flat_tensor, flat_indices)
    return sparse_tensor_lib.SparseTensor(indices, values, dense_shape) 

def decode_image(contents, channels=None, name=None):
  """Convenience function for `decode_gif`, `decode_jpeg`, and `decode_png`.
  Detects whether an image is a GIF, JPEG, or PNG, and performs the appropriate 
  operation to convert the input bytes `string` into a `Tensor` of type `uint8`.

  Note: `decode_gif` returns a 4-D array `[num_frames, height, width, 3]`, as 
  opposed to `decode_jpeg` and `decode_png`, which return 3-D arrays 
  `[height, width, num_channels]`. Make sure to take this into account when 
  constructing your graph if you are intermixing GIF files with JPEG and/or PNG 
  files.

  Args:
    contents: 0-D `string`. The encoded image bytes.
    channels: An optional `int`. Defaults to `0`. Number of color channels for 
      the decoded image.
    name: A name for the operation (optional)
    
  Returns:
    `Tensor` with type `uint8` with shape `[height, width, num_channels]` for 
      JPEG and PNG images and shape `[num_frames, height, width, 3]` for GIF 
      images.
  """
  with ops.name_scope(name, 'decode_image') as scope:
    if channels not in (None, 0, 1, 3):
      raise ValueError('channels must be in (None, 0, 1, 3)')
    substr = string_ops.substr(contents, 0, 4)

    def _gif():
      # Create assert op to check that bytes are GIF decodable
      is_gif = math_ops.equal(substr, b'\x47\x49\x46\x38', name='is_gif')
      decode_msg = 'Unable to decode bytes as JPEG, PNG, or GIF'
      assert_decode = control_flow_ops.Assert(is_gif, [decode_msg])
      # Create assert to make sure that channels is not set to 1
      # Already checked above that channels is in (None, 0, 1, 3)
      gif_channels = 0 if channels is None else channels
      good_channels = math_ops.not_equal(gif_channels, 1, name='check_channels')
      channels_msg = 'Channels must be in (None, 0, 3) when decoding GIF images'
      assert_channels = control_flow_ops.Assert(good_channels, [channels_msg])
      with ops.control_dependencies([assert_decode, assert_channels]):
        return gen_image_ops.decode_gif(contents)

    def _png():
      return gen_image_ops.decode_png(contents, channels)

    def check_png():
      is_png = math_ops.equal(substr, b'\211PNG', name='is_png')
      return control_flow_ops.cond(is_png, _png, _gif, name='cond_png')

    def _jpeg():
      return gen_image_ops.decode_jpeg(contents, channels)

    is_jpeg = math_ops.equal(substr, b'\xff\xd8\xff\xe0', name='is_jpeg')
    return control_flow_ops.cond(is_jpeg, _jpeg, check_png, name='cond_jpeg') 

def _to_sparse_input(input_tensor, ignore_value=None):
  """Converts a `Tensor` to a `SparseTensor`, dropping ignore_value cells.

  If `input_tensor` is already a `SparseTensor`, just return it.

  Args:
    input_tensor: A string or integer `Tensor`.
    ignore_value: Entries in `dense_tensor` equal to this value will be
      absent from the resulting `SparseTensor`. If `None`, default value of
      `dense_tensor`'s dtype will be used ('' for `str`, -1 for `int`).

  Returns:
    A `SparseTensor` with the same shape as `input_tensor`.

  Raises:
    ValueError: when `input_tensor`'s rank is `None`.
  """
  input_tensor = sparse_tensor_lib.convert_to_tensor_or_sparse_tensor(
      input_tensor)
  if isinstance(input_tensor, sparse_tensor_lib.SparseTensor):
    return input_tensor
  with ops.name_scope(None, 'to_sparse_input', (input_tensor, ignore_value,)):
    input_rank = input_tensor.get_shape().ndims
    if input_rank is None:
      # TODO(b/32318825): Implement dense_to_sparse_tensor for undefined rank.
      raise ValueError('Undefined input_tensor shape.')
    if ignore_value is None:
      ignore_value = '' if input_tensor.dtype == dtypes.string else -1
    dense_shape = math_ops.cast(array_ops.shape(input_tensor), dtypes.int64)
    indices = array_ops.where(math_ops.not_equal(
        input_tensor, math_ops.cast(ignore_value, input_tensor.dtype)))
    # Flattens the tensor and indices for use with gather.
    flat_tensor = array_ops.reshape(input_tensor, [-1])
    flat_indices = indices[:, input_rank - 1]
    # Computes the correct flattened indices for 2d (or higher) tensors.
    if input_rank > 1:
      higher_dims = indices[:, :input_rank - 1]
      shape_offsets = array_ops.stack(
          _shape_offsets(array_ops.unstack(dense_shape)[1:]))
      offsets = math_ops.reduce_sum(
          math_ops.multiply(higher_dims, shape_offsets),
          reduction_indices=[1])
      flat_indices = math_ops.add(flat_indices, offsets)
    values = array_ops.gather(flat_tensor, flat_indices)
    return sparse_tensor_lib.SparseTensor(indices, values, dense_shape)