tf.raw_ops.ScatterNdUpdate   Applies sparse updates to individual values or slices within a given  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ScatterNdUpdate
tf.raw_ops.ScatterNdUpdate(
    ref, indices, updates, use_locking=True, name=None
)
 variable according to indices. ref is a Tensor with rank P and indices is a Tensor of rank Q. indices must be integer tensor, containing indices into ref. It must be shape \([d_0, ..., d_{Q-2}, K]\) where 0 < K <= P. The innermost dimension of indices (with length K) corresponds to indices into elements (if K = P) or slices (if K < P) along the Kth dimension of ref. updates is Tensor of rank Q-1+P-K with shape:  $$[d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]].$$  For example, say we want to update 4 scattered elements to a rank-1 tensor to 8 elements. In Python, that update would look like this: ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8])
indices = tf.constant([[4], [3], [1] ,[7]])
updates = tf.constant([9, 10, 11, 12])
update = tf.scatter_nd_update(ref, indices, updates)
with tf.Session() as sess:
  print sess.run(update)
 The resulting update to ref would look like this: [1, 11, 3, 10, 9, 6, 7, 12]
 See tf.scatter_nd for more details about how to make updates to slices. See also tf.scatter_update and tf.batch_scatter_update.

 Args
  ref   A mutable Tensor. A mutable Tensor. Should be from a Variable node.
  indices   A Tensor. Must be one of the following types: int32, int64. A Tensor. Must be one of the following types: int32, int64. A tensor of indices into ref.
  updates   A Tensor. Must have the same type as ref. A Tensor. Must have the same type as ref. A tensor of updated values to add to ref.
  use_locking   An optional bool. Defaults to True. An optional bool. Defaults to True. If True, the assignment will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
  name   A name for the operation (optional).

 Returns   A mutable Tensor. Has the same type as ref.


tf.raw_ops.ScatterSub Subtracts sparse updates to a variable reference.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ScatterSub
tf.raw_ops.ScatterSub(
    ref, indices, updates, use_locking=False, name=None
)
 # Scalar indices
ref[indices, ...] -= updates[...]

# Vector indices (for each i)
ref[indices[i], ...] -= updates[i, ...]

# High rank indices (for each i, ..., j)
ref[indices[i, ..., j], ...] -= updates[i, ..., j, ...]
 This operation outputs ref after the update is done. This makes it easier to chain operations that need to use the reset value. Duplicate entries are handled correctly: if multiple indices reference the same location, their (negated) contributions add. Requires updates.shape = indices.shape + ref.shape[1:] or updates.shape = [].

 Args
  ref   A mutable Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. Should be from a Variable node.
  indices   A Tensor. Must be one of the following types: int32, int64. A tensor of indices into the first dimension of ref.
  updates   A Tensor. Must have the same type as ref. A tensor of updated values to subtract from ref.
  use_locking   An optional bool. Defaults to False. If True, the subtraction will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
  name   A name for the operation (optional).

 Returns   A mutable Tensor. Has the same type as ref.


tf.raw_ops.ScatterUpdate Applies sparse updates to a variable reference.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ScatterUpdate
tf.raw_ops.ScatterUpdate(
    ref, indices, updates, use_locking=True, name=None
)
 This operation computes # Scalar indices
ref[indices, ...] = updates[...]

# Vector indices (for each i)
ref[indices[i], ...] = updates[i, ...]

# High rank indices (for each i, ..., j)
ref[indices[i, ..., j], ...] = updates[i, ..., j, ...]
 This operation outputs ref after the update is done. This makes it easier to chain operations that need to use the reset value. If values in ref is to be updated more than once, because there are duplicate entries in indices, the order at which the updates happen for each value is undefined. Requires updates.shape = indices.shape + ref.shape[1:] or updates.shape = [].    See also tf.batch_scatter_update and tf.scatter_nd_update.

 Args
  ref   A mutable Tensor. Should be from a Variable node.
  indices   A Tensor. Must be one of the following types: int32, int64. A tensor of indices into the first dimension of ref.
  updates   A Tensor. Must have the same type as ref. A tensor of updated values to store in ref.
  use_locking   An optional bool. Defaults to True. If True, the assignment will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
  name   A name for the operation (optional).

 Returns   A mutable Tensor. Has the same type as ref.


tf.raw_ops.SdcaFprint Computes fingerprints of the input strings.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.SdcaFprint
tf.raw_ops.SdcaFprint(
    input, name=None
)

 Args
  input   A Tensor of type string. vector of strings to compute fingerprints on.
  name   A name for the operation (optional).

 Returns   A Tensor of type int64.


tf.raw_ops.SdcaOptimizer   Distributed version of Stochastic Dual Coordinate Ascent (SDCA) optimizer for  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.SdcaOptimizer
tf.raw_ops.SdcaOptimizer(
    sparse_example_indices, sparse_feature_indices, sparse_feature_values,
    dense_features, example_weights, example_labels, sparse_indices, sparse_weights,
    dense_weights, example_state_data, loss_type, l1, l2, num_loss_partitions,
    num_inner_iterations, adaptative=True, name=None
)
 linear models with L1 + L2 regularization. As global optimization objective is strongly-convex, the optimizer optimizes the dual objective at each step. The optimizer applies each update one example at a time. Examples are sampled uniformly, and the optimizer is learning rate free and enjoys linear convergence rate. Proximal Stochastic Dual Coordinate Ascent. Shai Shalev-Shwartz, Tong Zhang. 2012  $$Loss Objective = \sum f_{i} (wx_{i}) + (l2 / 2) * |w|^2 + l1 * |w|$$  Adding vs. Averaging in Distributed Primal-Dual Optimization. Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, Peter Richtarik, Martin Takac. 2015 Stochastic Dual Coordinate Ascent with Adaptive Probabilities. Dominik Csiba, Zheng Qu, Peter Richtarik. 2015

 Args
  sparse_example_indices   A list of Tensor objects with type int64. a list of vectors which contain example indices.
  sparse_feature_indices   A list with the same length as sparse_example_indices of Tensor objects with type int64. a list of vectors which contain feature indices.
  sparse_feature_values   A list of Tensor objects with type float32. a list of vectors which contains feature value associated with each feature group.
  dense_features   A list of Tensor objects with type float32. a list of matrices which contains the dense feature values.
  example_weights   A Tensor of type float32. a vector which contains the weight associated with each example.
  example_labels   A Tensor of type float32. a vector which contains the label/target associated with each example.
  sparse_indices   A list with the same length as sparse_example_indices of Tensor objects with type int64. a list of vectors where each value is the indices which has corresponding weights in sparse_weights. This field maybe omitted for the dense approach.
  sparse_weights   A list with the same length as sparse_example_indices of Tensor objects with type float32. a list of vectors where each value is the weight associated with a sparse feature group.
  dense_weights   A list with the same length as dense_features of Tensor objects with type float32. a list of vectors where the values are the weights associated with a dense feature group.
  example_state_data   A Tensor of type float32. a list of vectors containing the example state data.
  loss_type   A string from: "logistic_loss", "squared_loss", "hinge_loss", "smooth_hinge_loss", "poisson_loss". Type of the primal loss. Currently SdcaSolver supports logistic, squared and hinge losses.
  l1   A float. Symmetric l1 regularization strength.
  l2   A float. Symmetric l2 regularization strength.
  num_loss_partitions   An int that is >= 1. Number of partitions of the global loss function.
  num_inner_iterations   An int that is >= 1. Number of iterations per mini-batch.
  adaptative   An optional bool. Defaults to True. Whether to use Adaptive SDCA for the inner loop.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (out_example_state_data, out_delta_sparse_weights, out_delta_dense_weights).     out_example_state_data   A Tensor of type float32.
  out_delta_sparse_weights   A list with the same length as sparse_example_indices of Tensor objects with type float32.
  out_delta_dense_weights   A list with the same length as dense_features of Tensor objects with type float32.


tf.raw_ops.SdcaOptimizerV2   Distributed version of Stochastic Dual Coordinate Ascent (SDCA) optimizer for  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.SdcaOptimizerV2
tf.raw_ops.SdcaOptimizerV2(
    sparse_example_indices, sparse_feature_indices, sparse_feature_values,
    dense_features, example_weights, example_labels, sparse_indices, sparse_weights,
    dense_weights, example_state_data, loss_type, l1, l2, num_loss_partitions,
    num_inner_iterations, adaptive=True, name=None
)
 linear models with L1 + L2 regularization. As global optimization objective is strongly-convex, the optimizer optimizes the dual objective at each step. The optimizer applies each update one example at a time. Examples are sampled uniformly, and the optimizer is learning rate free and enjoys linear convergence rate. Proximal Stochastic Dual Coordinate Ascent. Shai Shalev-Shwartz, Tong Zhang. 2012  $$Loss Objective = \sum f_{i} (wx_{i}) + (l2 / 2) * |w|^2 + l1 * |w|$$  Adding vs. Averaging in Distributed Primal-Dual Optimization. Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, Peter Richtarik, Martin Takac. 2015 Stochastic Dual Coordinate Ascent with Adaptive Probabilities. Dominik Csiba, Zheng Qu, Peter Richtarik. 2015

 Args
  sparse_example_indices   A list of Tensor objects with type int64. a list of vectors which contain example indices.
  sparse_feature_indices   A list with the same length as sparse_example_indices of Tensor objects with type int64. a list of vectors which contain feature indices.
  sparse_feature_values   A list of Tensor objects with type float32. a list of vectors which contains feature value associated with each feature group.
  dense_features   A list of Tensor objects with type float32. a list of matrices which contains the dense feature values.
  example_weights   A Tensor of type float32. a vector which contains the weight associated with each example.
  example_labels   A Tensor of type float32. a vector which contains the label/target associated with each example.
  sparse_indices   A list with the same length as sparse_example_indices of Tensor objects with type int64. a list of vectors where each value is the indices which has corresponding weights in sparse_weights. This field maybe omitted for the dense approach.
  sparse_weights   A list with the same length as sparse_example_indices of Tensor objects with type float32. a list of vectors where each value is the weight associated with a sparse feature group.
  dense_weights   A list with the same length as dense_features of Tensor objects with type float32. a list of vectors where the values are the weights associated with a dense feature group.
  example_state_data   A Tensor of type float32. a list of vectors containing the example state data.
  loss_type   A string from: "logistic_loss", "squared_loss", "hinge_loss", "smooth_hinge_loss", "poisson_loss". Type of the primal loss. Currently SdcaSolver supports logistic, squared and hinge losses.
  l1   A float. Symmetric l1 regularization strength.
  l2   A float. Symmetric l2 regularization strength.
  num_loss_partitions   An int that is >= 1. Number of partitions of the global loss function.
  num_inner_iterations   An int that is >= 1. Number of iterations per mini-batch.
  adaptive   An optional bool. Defaults to True. Whether to use Adaptive SDCA for the inner loop.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (out_example_state_data, out_delta_sparse_weights, out_delta_dense_weights).     out_example_state_data   A Tensor of type float32.
  out_delta_sparse_weights   A list with the same length as sparse_example_indices of Tensor objects with type float32.
  out_delta_dense_weights   A list with the same length as dense_features of Tensor objects with type float32.


tf.raw_ops.SdcaShrinkL1 Applies L1 regularization shrink step on the parameters.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.SdcaShrinkL1
tf.raw_ops.SdcaShrinkL1(
    weights, l1, l2, name=None
)

 Args
  weights   A list of Tensor objects with type mutable float32. a list of vectors where each value is the weight associated with a feature group.
  l1   A float. Symmetric l1 regularization strength.
  l2   A float. Symmetric l2 regularization strength. Should be a positive float.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.SegmentMax   Computes the maximum along segments of a tensor.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.SegmentMax
tf.raw_ops.SegmentMax(
    data, segment_ids, name=None
)
 Read the section on segmentation for an explanation of segments. Computes a tensor such that \(output_i = \max_j(data_j)\) where max is over j such that segment_ids[j] == i. If the max is empty for a given segment ID i, output[i] = 0.    For example: c = tf.constant([[1,2,3,4], [4, 3, 2, 1], [5,6,7,8]])
tf.segment_max(c, tf.constant([0, 0, 1]))
# ==> [[4, 3, 3, 4],
#      [5, 6, 7, 8]]

 Args
  data   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64.
  segment_ids   A Tensor. Must be one of the following types: int32, int64. A 1-D tensor whose size is equal to the size of data's first dimension. Values should be sorted and can be repeated.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as data.


tf.raw_ops.SegmentMean   Computes the mean along segments of a tensor.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.SegmentMean
tf.raw_ops.SegmentMean(
    data, segment_ids, name=None
)
 Read the section on segmentation for an explanation of segments. Computes a tensor such that \(output_i = \frac{\sum_j data_j}{N}\) where mean is over j such that segment_ids[j] == i and N is the total number of values summed. If the mean is empty for a given segment ID i, output[i] = 0.    For example: c = tf.constant([[1.0,2,3,4], [4, 3, 2, 1], [5,6,7,8]])
tf.segment_mean(c, tf.constant([0, 0, 1]))
# ==> [[2.5, 2.5, 2.5, 2.5],
#      [5, 6, 7, 8]]

 Args
  data   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64.
  segment_ids   A Tensor. Must be one of the following types: int32, int64. A 1-D tensor whose size is equal to the size of data's first dimension. Values should be sorted and can be repeated.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as data.


tf.raw_ops.SegmentMin   Computes the minimum along segments of a tensor.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.SegmentMin
tf.raw_ops.SegmentMin(
    data, segment_ids, name=None
)
 Read the section on segmentation for an explanation of segments. Computes a tensor such that \(output_i = \min_j(data_j)\) where min is over j such that segment_ids[j] == i. If the min is empty for a given segment ID i, output[i] = 0.    For example: c = tf.constant([[1,2,3,4], [4, 3, 2, 1], [5,6,7,8]])
tf.segment_min(c, tf.constant([0, 0, 1]))
# ==> [[1, 2, 2, 1],
#      [5, 6, 7, 8]]

 Args
  data   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64.
  segment_ids   A Tensor. Must be one of the following types: int32, int64. A 1-D tensor whose size is equal to the size of data's first dimension. Values should be sorted and can be repeated.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as data.


tf.raw_ops.SegmentProd   Computes the product along segments of a tensor.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.SegmentProd
tf.raw_ops.SegmentProd(
    data, segment_ids, name=None
)
 Read the section on segmentation for an explanation of segments. Computes a tensor such that \(output_i = \prod_j data_j\) where the product is over j such that segment_ids[j] == i. If the product is empty for a given segment ID i, output[i] = 1.    For example: c = tf.constant([[1,2,3,4], [4, 3, 2, 1], [5,6,7,8]])
tf.segment_prod(c, tf.constant([0, 0, 1]))
# ==> [[4, 6, 6, 4],
#      [5, 6, 7, 8]]

 Args
  data   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64.
  segment_ids   A Tensor. Must be one of the following types: int32, int64. A 1-D tensor whose size is equal to the size of data's first dimension. Values should be sorted and can be repeated.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as data.


tf.raw_ops.SegmentSum   Computes the sum along segments of a tensor.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.SegmentSum
tf.raw_ops.SegmentSum(
    data, segment_ids, name=None
)
 Read the section on segmentation for an explanation of segments. Computes a tensor such that \(output_i = \sum_j data_j\) where sum is over j such that segment_ids[j] == i. If the sum is empty for a given segment ID i, output[i] = 0.    For example: c = tf.constant([[1,2,3,4], [4, 3, 2, 1], [5,6,7,8]])
tf.segment_sum(c, tf.constant([0, 0, 1]))
# ==> [[5, 5, 5, 5],
#      [5, 6, 7, 8]]

 Args
  data   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64.
  segment_ids   A Tensor. Must be one of the following types: int32, int64. A 1-D tensor whose size is equal to the size of data's first dimension. Values should be sorted and can be repeated.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as data.


tf.raw_ops.Select Selects elements from x or y, depending on condition.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Select
tf.raw_ops.Select(
    condition, x, y, name=None
)
 The x, and y tensors must all have the same shape, and the output will also have that shape. The condition tensor must be a scalar if x and y are scalars. If x and y are vectors or higher rank, then condition must be either a scalar, a vector with size matching the first dimension of x, or must have the same shape as x. The condition tensor acts as a mask that chooses, based on the value at each element, whether the corresponding element / row in the output should be taken from x (if true) or y (if false). If condition is a vector and x and y are higher rank matrices, then it chooses which row (outer dimension) to copy from x and y. If condition has the same shape as x and y, then it chooses which element to copy from x and y. For example: # 'condition' tensor is [[True,  False]
#                        [False, True]]
# 't' is [[1, 2],
#         [3, 4]]
# 'e' is [[5, 6],
#         [7, 8]]
select(condition, t, e)  # => [[1, 6], [7, 4]]

# 'condition' tensor is [True, False]
# 't' is [[1, 2],
#         [3, 4]]
# 'e' is [[5, 6],
#         [7, 8]]
select(condition, t, e) ==> [[1, 2],
                             [7, 8]]

 Args
  condition   A Tensor of type bool.
  x   A Tensor which may have the same shape as condition. If condition is rank 1, x may have higher rank, but its first dimension must match the size of condition.
  y   A Tensor with the same type and shape as x.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as t.


tf.raw_ops.SelectV2  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.SelectV2
tf.raw_ops.SelectV2(
    condition, t, e, name=None
)

 Args
  condition   A Tensor of type bool.
  t   A Tensor.
  e   A Tensor. Must have the same type as t.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as t.


tf.raw_ops.SelfAdjointEig Computes the Eigen Decomposition of a batch of square self-adjoint matrices.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.SelfAdjointEig
tf.raw_ops.SelfAdjointEig(
    input, name=None
)
 The input is a tensor of shape [..., M, M] whose inner-most 2 dimensions form square matrices, with the same constraints as the single matrix SelfAdjointEig. The result is a [..., M+1, M] matrix with [..., 0,:] containing the eigenvalues, and subsequent [...,1:, :] containing the eigenvectors. The eigenvalues are sorted in non-decreasing order.

 Args
  input   A Tensor. Must be one of the following types: float64, float32, half. Shape is [..., M, M].
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as input.


tf.raw_ops.SelfAdjointEigV2 Computes the eigen decomposition of one or more square self-adjoint matrices.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.SelfAdjointEigV2
tf.raw_ops.SelfAdjointEigV2(
    input, compute_v=True, name=None
)
 Computes the eigenvalues and (optionally) eigenvectors of each inner matrix in input such that input[..., :, :] = v[..., :, :] * diag(e[..., :]). The eigenvalues are sorted in non-decreasing order. # a is a tensor.
# e is a tensor of eigenvalues.
# v is a tensor of eigenvectors.
e, v = self_adjoint_eig(a)
e = self_adjoint_eig(a, compute_v=False)

 Args
  input   A Tensor. Must be one of the following types: float64, float32, half, complex64, complex128. Tensor input of shape [N, N].
  compute_v   An optional bool. Defaults to True. If True then eigenvectors will be computed and returned in v. Otherwise, only the eigenvalues will be computed.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (e, v).     e   A Tensor. Has the same type as input.
  v   A Tensor. Has the same type as input.


tf.raw_ops.Selu Computes scaled exponential linear: scale * alpha * (exp(features) - 1)  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Selu
tf.raw_ops.Selu(
    features, name=None
)
 if < 0, scale * features otherwise. To be used together with initializer = tf.variance_scaling_initializer(factor=1.0, mode='FAN_IN'). For correct dropout, use tf.contrib.nn.alpha_dropout. See Self-Normalizing Neural Networks

 Args
  features   A Tensor. Must be one of the following types: half, bfloat16, float32, float64.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as features.


tf.raw_ops.SeluGrad Computes gradients for the scaled exponential linear (Selu) operation.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.SeluGrad
tf.raw_ops.SeluGrad(
    gradients, outputs, name=None
)

 Args
  gradients   A Tensor. Must be one of the following types: half, bfloat16, float32, float64. The backpropagated gradients to the corresponding Selu operation.
  outputs   A Tensor. Must have the same type as gradients. The outputs of the corresponding Selu operation.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as gradients.


tf.raw_ops.Send Sends the named tensor from send_device to recv_device.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Send
tf.raw_ops.Send(
    tensor, tensor_name, send_device, send_device_incarnation, recv_device,
    client_terminated=False, name=None
)

 Args
  tensor   A Tensor. The tensor to send.
  tensor_name   A string. The name of the tensor to send.
  send_device   A string. The name of the device sending the tensor.
  send_device_incarnation   An int. The current incarnation of send_device.
  recv_device   A string. The name of the device receiving the tensor.
  client_terminated   An optional bool. Defaults to False. If set to true, this indicates that the node was added to the graph as a result of a client-side feed or fetch of Tensor data, in which case the corresponding send or recv is expected to be managed locally by the caller.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.SendTPUEmbeddingGradients Performs gradient updates of embedding tables.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.SendTPUEmbeddingGradients
tf.raw_ops.SendTPUEmbeddingGradients(
    inputs, learning_rates, config, name=None
)

 Args
  inputs   A list of at least 1 Tensor objects with type float32. A TensorList of gradients with which to update embedding tables. This argument has the same length and shapes as the return value of RecvTPUEmbeddingActivations, but contains gradients of the model's loss with respect to the embedding activations. The embedding tables are updated from these gradients via the optimizer specified in the TPU embedding configuration given to tpu.initialize_system.
  learning_rates   A list of Tensor objects with type float32. A TensorList of float32 scalars, one for each dynamic learning rate tag: see the comments in //third_party/tensorflow/core/protobuf/tpu/optimization_parameters.proto. Multiple tables can share the same dynamic learning rate tag as specified in the configuration. If the learning rates for all tables are constant, this list should be empty.
  config   A string. Serialized TPUEmbeddingConfiguration proto.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.SerializeIterator Converts the given resource_handle representing an iterator to a variant tensor.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.SerializeIterator
tf.raw_ops.SerializeIterator(
    resource_handle, external_state_policy=0, name=None
)

 Args
  resource_handle   A Tensor of type resource. A handle to an iterator resource.
  external_state_policy   An optional int. Defaults to 0.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.SerializeManySparse Serialize an N-minibatch SparseTensor into an [N, 3] Tensor object.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.SerializeManySparse
tf.raw_ops.SerializeManySparse(
    sparse_indices, sparse_values, sparse_shape, out_type=tf.dtypes.string,
    name=None
)
 The SparseTensor must have rank R greater than 1, and the first dimension is treated as the minibatch dimension. Elements of the SparseTensor must be sorted in increasing order of this first dimension. The serialized SparseTensor objects going into each row of serialized_sparse will have rank R-1. The minibatch size N is extracted from sparse_shape[0].

 Args
  sparse_indices   A Tensor of type int64. 2-D. The indices of the minibatch SparseTensor.
  sparse_values   A Tensor. 1-D. The values of the minibatch SparseTensor.
  sparse_shape   A Tensor of type int64. 1-D. The shape of the minibatch SparseTensor.
  out_type   An optional tf.DType from: tf.string, tf.variant. Defaults to tf.string. The dtype to use for serialization; the supported types are string (default) and variant.
  name   A name for the operation (optional).

 Returns   A Tensor of type out_type.


tf.raw_ops.SerializeSparse Serialize a SparseTensor into a [3] Tensor object.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.SerializeSparse
tf.raw_ops.SerializeSparse(
    sparse_indices, sparse_values, sparse_shape, out_type=tf.dtypes.string,
    name=None
)

 Args
  sparse_indices   A Tensor of type int64. 2-D. The indices of the SparseTensor.
  sparse_values   A Tensor. 1-D. The values of the SparseTensor.
  sparse_shape   A Tensor of type int64. 1-D. The shape of the SparseTensor.
  out_type   An optional tf.DType from: tf.string, tf.variant. Defaults to tf.string. The dtype to use for serialization; the supported types are string (default) and variant.
  name   A name for the operation (optional).

 Returns   A Tensor of type out_type.


tf.raw_ops.SerializeTensor Transforms a Tensor into a serialized TensorProto proto.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.SerializeTensor
tf.raw_ops.SerializeTensor(
    tensor, name=None
)

 Args
  tensor   A Tensor. A Tensor of type T.
  name   A name for the operation (optional).

 Returns   A Tensor of type string.


tf.raw_ops.SetSize Number of unique elements along last dimension of input set.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.SetSize
tf.raw_ops.SetSize(
    set_indices, set_values, set_shape, validate_indices=True, name=None
)
 Input set is a SparseTensor represented by set_indices, set_values, and set_shape. The last dimension contains values in a set, duplicates are allowed but ignored. If validate_indices is True, this op validates the order and range of set indices.

 Args
  set_indices   A Tensor of type int64. 2D Tensor, indices of a SparseTensor.
  set_values   A Tensor. Must be one of the following types: int8, int16, int32, int64, uint8, uint16, string. 1D Tensor, values of a SparseTensor.
  set_shape   A Tensor of type int64. 1D Tensor, shape of a SparseTensor.
  validate_indices   An optional bool. Defaults to True.
  name   A name for the operation (optional).

 Returns   A Tensor of type int32.


tf.raw_ops.SetStatsAggregatorDataset  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.SetStatsAggregatorDataset
tf.raw_ops.SetStatsAggregatorDataset(
    input_dataset, stats_aggregator, tag, counter_prefix, output_types,
    output_shapes, name=None
)

 Args
  input_dataset   A Tensor of type variant.
  stats_aggregator   A Tensor of type resource.
  tag   A Tensor of type string.
  counter_prefix   A Tensor of type string.
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.Shape Returns the shape of a tensor.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Shape
tf.raw_ops.Shape(
    input, out_type=tf.dtypes.int32, name=None
)
 This operation returns a 1-D integer tensor representing the shape of input. For example: # 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]
shape(t) ==> [2, 2, 3]

 Args
  input   A Tensor.
  out_type   An optional tf.DType from: tf.int32, tf.int64. Defaults to tf.int32.
  name   A name for the operation (optional).

 Returns   A Tensor of type out_type.


tf.raw_ops.ShapeN Returns shape of tensors.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ShapeN
tf.raw_ops.ShapeN(
    input, out_type=tf.dtypes.int32, name=None
)
 This operation returns N 1-D integer tensors representing shape of input[i]s.

 Args
  input   A list of at least 1 Tensor objects with the same type.
  out_type   An optional tf.DType from: tf.int32, tf.int64. Defaults to tf.int32.
  name   A name for the operation (optional).

 Returns   A list with the same length as input of Tensor objects with type out_type.


tf.raw_ops.ShardDataset Creates a Dataset that includes only 1/num_shards of this dataset.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ShardDataset
tf.raw_ops.ShardDataset(
    input_dataset, num_shards, index, output_types, output_shapes,
    require_non_empty=False, name=None
)

 Args
  input_dataset   A Tensor of type variant.
  num_shards   A Tensor of type int64. An integer representing the number of shards operating in parallel.
  index   A Tensor of type int64. An integer representing the current worker index.
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  require_non_empty   An optional bool. Defaults to False.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.ShardedFilename Generate a sharded filename. The filename is printf formatted as  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ShardedFilename
tf.raw_ops.ShardedFilename(
    basename, shard, num_shards, name=None
)
 %s-%05d-of-%05d, basename, shard, num_shards.

 Args
  basename   A Tensor of type string.
  shard   A Tensor of type int32.
  num_shards   A Tensor of type int32.
  name   A name for the operation (optional).

 Returns   A Tensor of type string.


tf.raw_ops.ShardedFilespec Generate a glob pattern matching all sharded file names.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ShardedFilespec
tf.raw_ops.ShardedFilespec(
    basename, num_shards, name=None
)

 Args
  basename   A Tensor of type string.
  num_shards   A Tensor of type int32.
  name   A name for the operation (optional).

 Returns   A Tensor of type string.


tf.raw_ops.ShuffleAndRepeatDataset Creates a dataset that shuffles and repeats elements from input_dataset  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ShuffleAndRepeatDataset
tf.raw_ops.ShuffleAndRepeatDataset(
    input_dataset, buffer_size, seed, seed2, count, output_types, output_shapes,
    reshuffle_each_iteration=True, name=None
)
 pseudorandomly.

 Args
  input_dataset   A Tensor of type variant.
  buffer_size   A Tensor of type int64. The number of output elements to buffer in an iterator over this dataset. Compare with the min_after_dequeue attr when creating a RandomShuffleQueue.
  seed   A Tensor of type int64. A scalar seed for the random number generator. If either seed or seed2 is set to be non-zero, the random number generator is seeded by the given seed. Otherwise, a random seed is used.
  seed2   A Tensor of type int64. A second scalar seed to avoid seed collision.
  count   A Tensor of type int64. A scalar representing the number of times the underlying dataset should be repeated. The default is -1, which results in infinite repetition.
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  reshuffle_each_iteration   An optional bool. Defaults to True.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.ShuffleAndRepeatDatasetV2  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ShuffleAndRepeatDatasetV2
tf.raw_ops.ShuffleAndRepeatDatasetV2(
    input_dataset, buffer_size, seed, seed2, count, seed_generator, output_types,
    output_shapes, reshuffle_each_iteration=True, name=None
)

 Args
  input_dataset   A Tensor of type variant.
  buffer_size   A Tensor of type int64.
  seed   A Tensor of type int64.
  seed2   A Tensor of type int64.
  count   A Tensor of type int64.
  seed_generator   A Tensor of type resource.
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  reshuffle_each_iteration   An optional bool. Defaults to True.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.ShuffleDataset Creates a dataset that shuffles elements from input_dataset pseudorandomly.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ShuffleDataset
tf.raw_ops.ShuffleDataset(
    input_dataset, buffer_size, seed, seed2, output_types, output_shapes,
    reshuffle_each_iteration=True, name=None
)

 Args
  input_dataset   A Tensor of type variant.
  buffer_size   A Tensor of type int64. The number of output elements to buffer in an iterator over this dataset. Compare with the min_after_dequeue attr when creating a RandomShuffleQueue.
  seed   A Tensor of type int64. A scalar seed for the random number generator. If either seed or seed2 is set to be non-zero, the random number generator is seeded by the given seed. Otherwise, a random seed is used.
  seed2   A Tensor of type int64. A second scalar seed to avoid seed collision.
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  reshuffle_each_iteration   An optional bool. Defaults to True. If true, each iterator over this dataset will be given a different pseudorandomly generated seed, based on a sequence seeded by the seed and seed2 inputs. If false, each iterator will be given the same seed, and repeated iteration over this dataset will yield the exact same sequence of results.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.ShuffleDatasetV2  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ShuffleDatasetV2
tf.raw_ops.ShuffleDatasetV2(
    input_dataset, buffer_size, seed_generator, output_types, output_shapes,
    name=None
)

 Args
  input_dataset   A Tensor of type variant.
  buffer_size   A Tensor of type int64.
  seed_generator   A Tensor of type resource.
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.ShuffleDatasetV3  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ShuffleDatasetV3
tf.raw_ops.ShuffleDatasetV3(
    input_dataset, buffer_size, seed, seed2, seed_generator, output_types,
    output_shapes, reshuffle_each_iteration=True, name=None
)

 Args
  input_dataset   A Tensor of type variant.
  buffer_size   A Tensor of type int64.
  seed   A Tensor of type int64.
  seed2   A Tensor of type int64.
  seed_generator   A Tensor of type resource.
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  reshuffle_each_iteration   An optional bool. Defaults to True.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.ShutdownDistributedTPU Shuts down a running distributed TPU system.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ShutdownDistributedTPU
tf.raw_ops.ShutdownDistributedTPU(
    name=None
)
 The op returns an error if no system is running.

 Args
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.Sigmoid Computes sigmoid of x element-wise.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Sigmoid
tf.raw_ops.Sigmoid(
    x, name=None
)
 Specifically, y = 1 / (1 + exp(-x)).

 Args
  x   A Tensor. Must be one of the following types: bfloat16, half, float32, float64, complex64, complex128.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as x.


tf.raw_ops.SigmoidGrad Computes the gradient of the sigmoid of x wrt its input.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.SigmoidGrad
tf.raw_ops.SigmoidGrad(
    y, dy, name=None
)
 Specifically, grad = dy * y * (1 - y), where y = sigmoid(x), and dy is the corresponding input gradient.

 Args
  y   A Tensor. Must be one of the following types: bfloat16, half, float32, float64, complex64, complex128.
  dy   A Tensor. Must have the same type as y.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as y.


tf.raw_ops.Sign Returns an element-wise indication of the sign of a number.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Sign
tf.raw_ops.Sign(
    x, name=None
)
 y = sign(x) = -1 if x < 0; 0 if x == 0; 1 if x > 0. For complex numbers, y = sign(x) = x / |x| if x != 0, otherwise y = 0. Example usage:
tf.math.sign([0., 2., -3.])
<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.,  1., -1.], dtype=float32)>

 Args
  x   A Tensor. Must be one of the following types: bfloat16, half, float32, float64, int32, int64, complex64, complex128.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as x.


tf.raw_ops.Sin Computes sine of x element-wise.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Sin
tf.raw_ops.Sin(
    x, name=None
)
 Given an input tensor, this function computes sine of every element in the tensor. Input range is (-inf, inf) and output range is [-1,1]. x = tf.constant([-float("inf"), -9, -0.5, 1, 1.2, 200, 10, float("inf")])
tf.math.sin(x) ==> [nan -0.4121185 -0.47942555 0.84147096 0.9320391 -0.87329733 -0.54402107 nan]

 Args
  x   A Tensor. Must be one of the following types: bfloat16, half, float32, float64, complex64, complex128.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as x.


tf.raw_ops.Sinh Computes hyperbolic sine of x element-wise.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Sinh
tf.raw_ops.Sinh(
    x, name=None
)
 Given an input tensor, this function computes hyperbolic sine of every element in the tensor. Input range is [-inf,inf] and output range is [-inf,inf]. x = tf.constant([-float("inf"), -9, -0.5, 1, 1.2, 2, 10, float("inf")])
tf.math.sinh(x) ==> [-inf -4.0515420e+03 -5.2109528e-01 1.1752012e+00 1.5094614e+00 3.6268604e+00 1.1013232e+04 inf]

 Args
  x   A Tensor. Must be one of the following types: bfloat16, half, float32, float64, complex64, complex128.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as x.


tf.raw_ops.Size Returns the size of a tensor.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Size
tf.raw_ops.Size(
    input, out_type=tf.dtypes.int32, name=None
)
 This operation returns an integer representing the number of elements in input. For example: # 't' is [[[1, 1,, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]]
size(t) ==> 12

 Args
  input   A Tensor.
  out_type   An optional tf.DType from: tf.int32, tf.int64. Defaults to tf.int32.
  name   A name for the operation (optional).

 Returns   A Tensor of type out_type.


tf.raw_ops.SkipDataset Creates a dataset that skips count elements from the input_dataset.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.SkipDataset
tf.raw_ops.SkipDataset(
    input_dataset, count, output_types, output_shapes, name=None
)

 Args
  input_dataset   A Tensor of type variant.
  count   A Tensor of type int64. A scalar representing the number of elements from the input_dataset that should be skipped. If count is -1, skips everything.
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.SleepDataset  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.SleepDataset
tf.raw_ops.SleepDataset(
    input_dataset, sleep_microseconds, output_types, output_shapes, name=None
)

 Args
  input_dataset   A Tensor of type variant.
  sleep_microseconds   A Tensor of type int64.
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.Slice Return a slice from 'input'.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Slice
tf.raw_ops.Slice(
    input, begin, size, name=None
)
 The output tensor is a tensor with dimensions described by 'size' whose values are extracted from 'input' starting at the offsets in 'begin'. Requirements: 0 <= begin[i] <= begin[i] + size[i] <= Di for i in [0, n)

 Args
  input   A Tensor.
  begin   A Tensor. Must be one of the following types: int32, int64. begin[i] specifies the offset into the 'i'th dimension of 'input' to slice from.
  size   A Tensor. Must have the same type as begin. size[i] specifies the number of elements of the 'i'th dimension of 'input' to slice. If size[i] is -1, all remaining elements in dimension i are included in the slice (i.e. this is equivalent to setting size[i] = input.dim_size(i) - begin[i]).
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as input.


tf.raw_ops.SlidingWindowDataset Creates a dataset that passes a sliding window over input_dataset.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.SlidingWindowDataset
tf.raw_ops.SlidingWindowDataset(
    input_dataset, window_size, window_shift, window_stride, output_types,
    output_shapes, name=None
)

 Args
  input_dataset   A Tensor of type variant.
  window_size   A Tensor of type int64. A scalar representing the number of elements in the sliding window.
  window_shift   A Tensor of type int64. A scalar representing the steps moving the sliding window forward in one iteration. It must be positive.
  window_stride   A Tensor of type int64. A scalar representing the stride of the input elements of the sliding window. It must be positive.
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.Snapshot Returns a copy of the input tensor.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Snapshot
tf.raw_ops.Snapshot(
    input, name=None
)

 Args
  input   A Tensor.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as input.


tf.raw_ops.SnapshotDataset Creates a dataset that will write to / read from a snapshot.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.SnapshotDataset
tf.raw_ops.SnapshotDataset(
    input_dataset, path, output_types, output_shapes, compression='',
    reader_path_prefix='', writer_path_prefix='',
    shard_size_bytes=10737418240, pending_snapshot_expiry_seconds=86400,
    num_reader_threads=1, reader_buffer_size=1, num_writer_threads=1,
    writer_buffer_size=1, shuffle_on_read=False, seed=0, seed2=0,
    mode='auto', snapshot_name='', name=None
)
 This dataset attempts to determine whether a valid snapshot exists at the snapshot_path, and reads from the snapshot in lieu of using input_dataset. If not, it will run the preprocessing pipeline as usual, and write out a snapshot of the data processed for future use.

 Args
  input_dataset   A Tensor of type variant. A variant tensor representing the input dataset.
  path   A Tensor of type string. The path we should write snapshots to / read snapshots from.
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  compression   An optional string. Defaults to "".
  reader_path_prefix   An optional string. Defaults to "".
  writer_path_prefix   An optional string. Defaults to "".
  shard_size_bytes   An optional int. Defaults to 10737418240.
  pending_snapshot_expiry_seconds   An optional int. Defaults to 86400.
  num_reader_threads   An optional int. Defaults to 1.
  reader_buffer_size   An optional int. Defaults to 1.
  num_writer_threads   An optional int. Defaults to 1.
  writer_buffer_size   An optional int. Defaults to 1.
  shuffle_on_read   An optional bool. Defaults to False.
  seed   An optional int. Defaults to 0.
  seed2   An optional int. Defaults to 0.
  mode   An optional string. Defaults to "auto".
  snapshot_name   An optional string. Defaults to "".
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.SnapshotDatasetV2 Creates a dataset that will write to / read from a snapshot.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.SnapshotDatasetV2
tf.raw_ops.SnapshotDatasetV2(
    input_dataset, path, reader_func_other_args, shard_func_other_args,
    output_types, output_shapes, reader_func, shard_func, compression='',
    reader_prefix='', writer_prefix='', name=None
)
 This dataset attempts to determine whether a valid snapshot exists at the snapshot_path, and reads from the snapshot in lieu of using input_dataset. If not, it will run the preprocessing pipeline as usual, and write out a snapshot of the data processed for future use.

 Args
  input_dataset   A Tensor of type variant. A variant tensor representing the input dataset.
  path   A Tensor of type string. The path we should write snapshots to / read snapshots from.
  reader_func_other_args   A list of Tensor objects.
  shard_func_other_args   A list of Tensor objects.
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  reader_func   A function decorated with @Defun. Optional. A function to control how to read data from snapshot shards.
  shard_func   A function decorated with @Defun. Optional. A function to control how to shard data when writing a snapshot.
  compression   An optional string. Defaults to "". The type of compression to be applied to the saved snapshot files.
  reader_prefix   An optional string. Defaults to "".
  writer_prefix   An optional string. Defaults to "".
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.SobolSample Generates points from the Sobol sequence.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.SobolSample
tf.raw_ops.SobolSample(
    dim, num_results, skip, dtype=tf.dtypes.float32, name=None
)
 Creates a Sobol sequence with num_results samples. Each sample has dimension dim. Skips the first skip samples.

 Args
  dim   A Tensor of type int32. Positive scalar Tensor representing each sample's dimension.
  num_results   A Tensor of type int32. Positive scalar Tensor of dtype int32. The number of Sobol points to return in the output.
  skip   A Tensor of type int32. Positive scalar Tensor of dtype int32. The number of initial points of the Sobol sequence to skip.
  dtype   An optional tf.DType from: tf.float32, tf.float64. Defaults to tf.float32. The type of the sample. One of: float32 or float64.
  name   A name for the operation (optional).

 Returns   A Tensor of type dtype.


tf.raw_ops.Softmax   Computes softmax activations.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Softmax
tf.raw_ops.Softmax(
    logits, name=None
)
 For each batch i and class j we have  $$softmax[i, j] = exp(logits[i, j]) / sum_j(exp(logits[i, j]))$$

 Args
  logits   A Tensor. Must be one of the following types: half, bfloat16, float32, float64. 2-D with shape [batch_size, num_classes].
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as logits.


tf.raw_ops.SoftmaxCrossEntropyWithLogits Computes softmax cross entropy cost and gradients to backpropagate.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.SoftmaxCrossEntropyWithLogits
tf.raw_ops.SoftmaxCrossEntropyWithLogits(
    features, labels, name=None
)
 Inputs are the logits, not probabilities.

 Args
  features   A Tensor. Must be one of the following types: half, bfloat16, float32, float64. batch_size x num_classes matrix
  labels   A Tensor. Must have the same type as features. batch_size x num_classes matrix The caller must ensure that each batch of labels represents a valid probability distribution.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (loss, backprop).     loss   A Tensor. Has the same type as features.
  backprop   A Tensor. Has the same type as features.


tf.raw_ops.Softplus Computes softplus: log(exp(features) + 1).  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Softplus
tf.raw_ops.Softplus(
    features, name=None
)

 Args
  features   A Tensor. Must be one of the following types: half, bfloat16, float32, float64.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as features.


tf.raw_ops.SoftplusGrad Computes softplus gradients for a softplus operation.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.SoftplusGrad
tf.raw_ops.SoftplusGrad(
    gradients, features, name=None
)

 Args
  gradients   A Tensor. Must be one of the following types: half, bfloat16, float32, float64. The backpropagated gradients to the corresponding softplus operation.
  features   A Tensor. Must have the same type as gradients. The features passed as input to the corresponding softplus operation.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as gradients.


tf.raw_ops.Softsign Computes softsign: features / (abs(features) + 1).  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Softsign
tf.raw_ops.Softsign(
    features, name=None
)

 Args
  features   A Tensor. Must be one of the following types: half, bfloat16, float32, float64.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as features.


tf.raw_ops.SoftsignGrad Computes softsign gradients for a softsign operation.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.SoftsignGrad
tf.raw_ops.SoftsignGrad(
    gradients, features, name=None
)

 Args
  gradients   A Tensor. Must be one of the following types: half, bfloat16, float32, float64. The backpropagated gradients to the corresponding softsign operation.
  features   A Tensor. Must have the same type as gradients. The features passed as input to the corresponding softsign operation.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as gradients.


tf.raw_ops.SpaceToBatch SpaceToBatch for 4-D tensors of type T.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.SpaceToBatch
tf.raw_ops.SpaceToBatch(
    input, paddings, block_size, name=None
)
 This is a legacy version of the more general SpaceToBatchND. Zero-pads and then rearranges (permutes) blocks of spatial data into batch. More specifically, this op outputs a copy of the input tensor where values from the height and width dimensions are moved to the batch dimension. After the zero-padding, both height and width of the input must be divisible by the block size.

 Args
  input   A Tensor. 4-D with shape [batch, height, width, depth].
  paddings   A Tensor. Must be one of the following types: int32, int64. 2-D tensor of non-negative integers with shape [2, 2]. It specifies the padding of the input with zeros across the spatial dimensions as follows: paddings = [[pad_top, pad_bottom], [pad_left, pad_right]] The effective spatial dimensions of the zero-padded input tensor will be: height_pad = pad_top + height + pad_bottom width_pad = pad_left + width + pad_right The attr block_size must be greater than one. It indicates the block size.  Non-overlapping blocks of size block_size x block size in the height and width dimensions are rearranged into the batch dimension at each location. The batch of the output tensor is batch * block_size * block_size. Both height_pad and width_pad must be divisible by block_size.  The shape of the output will be: [batchblock_sizeblock_size, height_pad/block_size, width_pad/block_size, depth] Some examples: (1) For the following input of shape [1, 2, 2, 1] and block_size of 2: x = [[[[1], [2]], [[3], [4]]]]
 The output tensor has shape [4, 1, 1, 1] and value: [[[[1]]], [[[2]]], [[[3]]], [[[4]]]]
 (2) For the following input of shape [1, 2, 2, 3] and block_size of 2: x = [[[[1, 2, 3], [4, 5, 6]],
[[7, 8, 9], [10, 11, 12]]]]
 The output tensor has shape [4, 1, 1, 3] and value: [[[[1, 2, 3]]], [[[4, 5, 6]]], [[[7, 8, 9]]], [[[10, 11, 12]]]]
 (3) For the following input of shape [1, 4, 4, 1] and block_size of 2: x = [[[[1],   [2],  [3],  [4]],
[[5],   [6],  [7],  [8]],
[[9],  [10], [11],  [12]],
[[13], [14], [15],  [16]]]]
 The output tensor has shape [4, 2, 2, 1] and value: x = [[[[1], [3]], [[9], [11]]],
[[[2], [4]], [[10], [12]]],
[[[5], [7]], [[13], [15]]],
[[[6], [8]], [[14], [16]]]]
 (4) For the following input of shape [2, 2, 4, 1] and block_size of 2: x = [[[[1],   [2],  [3],  [4]],
[[5],   [6],  [7],  [8]]],
[[[9],  [10], [11],  [12]],
[[13], [14], [15],  [16]]]]
 The output tensor has shape [8, 1, 2, 1] and value: x = [[[[1], [3]]], [[[9], [11]]], [[[2], [4]]], [[[10], [12]]],
[[[5], [7]]], [[[13], [15]]], [[[6], [8]]], [[[14], [16]]]]
 Among others, this operation is useful for reducing atrous convolution into regular convolution.

  block_size   An int that is >= 2.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as input.


tf.raw_ops.SpaceToBatchND SpaceToBatch for N-D tensors of type T.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.SpaceToBatchND
tf.raw_ops.SpaceToBatchND(
    input, block_shape, paddings, name=None
)
 This operation divides "spatial" dimensions [1, ..., M] of the input into a grid of blocks of shape block_shape, and interleaves these blocks with the "batch" dimension (0) such that in the output, the spatial dimensions [1, ..., M] correspond to the position within the grid, and the batch dimension combines both the position within a spatial block and the original batch position. Prior to division into blocks, the spatial dimensions of the input are optionally zero padded according to paddings. See below for a precise description.

 Args
  input   A Tensor. N-D with shape input_shape = [batch] + spatial_shape + remaining_shape, where spatial_shape has M dimensions.
  block_shape   A Tensor. Must be one of the following types: int32, int64. 1-D with shape [M], all values must be >= 1.
  paddings   A Tensor. Must be one of the following types: int32, int64. 2-D with shape [M, 2], all values must be >= 0. paddings[i] = [pad_start, pad_end] specifies the padding for input dimension i + 1, which corresponds to spatial dimension i. It is required that block_shape[i] divides input_shape[i + 1] + pad_start + pad_end. This operation is equivalent to the following steps:  Zero-pad the start and end of dimensions [1, ..., M] of the input according to paddings to produce padded of shape padded_shape. Reshape padded to reshaped_padded of shape:  [batch] + [padded_shape[1] / block_shape[0], block_shape[0], ..., padded_shape[M] / block_shape[M-1], block_shape[M-1]] + remaining_shape  Permute dimensions of reshaped_padded to produce permuted_reshaped_padded of shape:  block_shape + [batch] + [padded_shape[1] / block_shape[0], ..., padded_shape[M] / block_shape[M-1]] + remaining_shape  Reshape permuted_reshaped_padded to flatten block_shape into the batch dimension, producing an output tensor of shape:  [batch * prod(block_shape)] + [padded_shape[1] / block_shape[0], ..., padded_shape[M] / block_shape[M-1]] + remaining_shape Some examples: (1) For the following input of shape [1, 2, 2, 1], block_shape = [2, 2], and paddings = [[0, 0], [0, 0]]: x = [[[[1], [2]], [[3], [4]]]]
 The output tensor has shape [4, 1, 1, 1] and value: [[[[1]]], [[[2]]], [[[3]]], [[[4]]]]
 (2) For the following input of shape [1, 2, 2, 3], block_shape = [2, 2], and paddings = [[0, 0], [0, 0]]: x = [[[[1, 2, 3], [4, 5, 6]],
[[7, 8, 9], [10, 11, 12]]]]
 The output tensor has shape [4, 1, 1, 3] and value: [[[[1, 2, 3]]], [[[4, 5, 6]]], [[[7, 8, 9]]], [[[10, 11, 12]]]]
 (3) For the following input of shape [1, 4, 4, 1], block_shape = [2, 2], and paddings = [[0, 0], [0, 0]]: x = [[[[1],   [2],  [3],  [4]],
[[5],   [6],  [7],  [8]],
[[9],  [10], [11],  [12]],
[[13], [14], [15],  [16]]]]
 The output tensor has shape [4, 2, 2, 1] and value: x = [[[[1], [3]], [[9], [11]]],
[[[2], [4]], [[10], [12]]],
[[[5], [7]], [[13], [15]]],
[[[6], [8]], [[14], [16]]]]
 (4) For the following input of shape [2, 2, 4, 1], block_shape = [2, 2], and paddings = [[0, 0], [2, 0]]: x = [[[[1],   [2],  [3],  [4]],
[[5],   [6],  [7],  [8]]],
[[[9],  [10], [11],  [12]],
[[13], [14], [15],  [16]]]]
 The output tensor has shape [8, 1, 3, 1] and value: x = [[[[0], [1], [3]]], [[[0], [9], [11]]],
[[[0], [2], [4]]], [[[0], [10], [12]]],
[[[0], [5], [7]]], [[[0], [13], [15]]],
[[[0], [6], [8]]], [[[0], [14], [16]]]]
 Among others, this operation is useful for reducing atrous convolution into regular convolution.

  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as input.


tf.raw_ops.SpaceToDepth SpaceToDepth for tensors of type T.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.SpaceToDepth
tf.raw_ops.SpaceToDepth(
    input, block_size, data_format='NHWC', name=None
)
 Rearranges blocks of spatial data, into depth. More specifically, this op outputs a copy of the input tensor where values from the height and width dimensions are moved to the depth dimension. The attr block_size indicates the input block size.  Non-overlapping blocks of size block_size x block size are rearranged into depth at each location. The depth of the output tensor is block_size * block_size * input_depth. The Y, X coordinates within each block of the input become the high order component of the output channel index. The input tensor's height and width must be divisible by block_size.  The data_format attr specifies the layout of the input and output tensors with the following options: "NHWC": [ batch, height, width, channels ] "NCHW": [ batch, channels, height, width ] "NCHW_VECT_C": qint8 [ batch, channels / 4, height, width, 4 ] It is useful to consider the operation as transforming a 6-D Tensor. e.g. for data_format = NHWC, Each element in the input tensor can be specified via 6 coordinates, ordered by decreasing memory layout significance as: n,oY,bY,oX,bX,iC (where n=batch index, oX, oY means X or Y coordinates within the output image, bX, bY means coordinates within the input block, iC means input channels). The output would be a transpose to the following layout: n,oY,oX,bY,bX,iC This operation is useful for resizing the activations between convolutions (but keeping all data), e.g. instead of pooling. It is also useful for training purely convolutional models. For example, given an input of shape [1, 2, 2, 1], data_format = "NHWC" and block_size = 2: x = [[[[1], [2]],
      [[3], [4]]]]
 This operation will output a tensor of shape [1, 1, 1, 4]: [[[[1, 2, 3, 4]]]]
 Here, the input has a batch of 1 and each batch element has shape [2, 2, 1], the corresponding output will have a single element (i.e. width and height are both 1) and will have a depth of 4 channels (1 * block_size * block_size). The output element shape is [1, 1, 4]. For an input tensor with larger depth, here of shape [1, 2, 2, 3], e.g. x = [[[[1, 2, 3], [4, 5, 6]],
      [[7, 8, 9], [10, 11, 12]]]]
 This operation, for block_size of 2, will return the following tensor of shape [1, 1, 1, 12] [[[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]]]]
 Similarly, for the following input of shape [1 4 4 1], and a block size of 2: x = [[[[1],   [2],  [5],  [6]],
      [[3],   [4],  [7],  [8]],
      [[9],  [10], [13],  [14]],
      [[11], [12], [15],  [16]]]]
 the operator will return the following tensor of shape [1 2 2 4]: x = [[[[1, 2, 3, 4],
       [5, 6, 7, 8]],
      [[9, 10, 11, 12],
       [13, 14, 15, 16]]]]

 Args
  input   A Tensor.
  block_size   An int that is >= 2. The size of the spatial block.
  data_format   An optional string from: "NHWC", "NCHW", "NCHW_VECT_C". Defaults to "NHWC".
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as input.


tf.raw_ops.SparseAccumulatorApplyGradient Applies a sparse gradient to a given accumulator.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.SparseAccumulatorApplyGradient
tf.raw_ops.SparseAccumulatorApplyGradient(
    handle, local_step, gradient_indices, gradient_values, gradient_shape,
    has_known_shape, name=None
)
 Does not add if local_step is smaller than the accumulator's global_step.

 Args
  handle   A Tensor of type mutable string. The handle to a accumulator.
  local_step   A Tensor of type int64. The local_step value at which the sparse gradient was computed.
  gradient_indices   A Tensor of type int64. Indices of the sparse gradient to be accumulated. Must be a vector.
  gradient_values   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. Values are the non-zero slices of the gradient, and must have the same first dimension as indices, i.e., the nnz represented by indices and values must be consistent.
  gradient_shape   A Tensor of type int64. Shape of the sparse gradient to be accumulated.
  has_known_shape   A bool. Boolean indicating whether gradient_shape is unknown, in which case the input is ignored during validation.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.SparseAccumulatorTakeGradient Extracts the average sparse gradient in a SparseConditionalAccumulator.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.SparseAccumulatorTakeGradient
tf.raw_ops.SparseAccumulatorTakeGradient(
    handle, num_required, dtype, name=None
)
 The op will blocks until sufficient (i.e., more than num_required) gradients have been accumulated. If the accumulator has already aggregated more than num_required gradients, it will return its average of the accumulated gradients. Also automatically increments the recorded global_step in the accumulator by 1, and resets the aggregate to 0.

 Args
  handle   A Tensor of type mutable string. The handle to a SparseConditionalAccumulator.
  num_required   A Tensor of type int32. Number of gradients required before we return an aggregate.
  dtype   A tf.DType from: tf.float32, tf.float64, tf.int32, tf.uint8, tf.int16, tf.int8, tf.complex64, tf.int64, tf.qint8, tf.quint8, tf.qint32, tf.bfloat16, tf.uint16, tf.complex128, tf.half, tf.uint32, tf.uint64. The data type of accumulated gradients. Needs to correspond to the type of the accumulator.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (indices, values, shape).     indices   A Tensor of type int64.
  values   A Tensor of type dtype.
  shape   A Tensor of type int64.


tf.raw_ops.SparseAdd Adds two SparseTensor objects to produce another SparseTensor.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.SparseAdd
tf.raw_ops.SparseAdd(
    a_indices, a_values, a_shape, b_indices, b_values, b_shape, thresh, name=None
)
 The input SparseTensor objects' indices are assumed ordered in standard lexicographic order. If this is not the case, before this step run SparseReorder to restore index ordering. By default, if two values sum to zero at some index, the output SparseTensor would still include that particular location in its index, storing a zero in the corresponding value slot. To override this, callers can specify thresh, indicating that if the sum has a magnitude strictly smaller than thresh, its corresponding value and index would then not be included. In particular, thresh == 0 (default) means everything is kept and actual thresholding happens only for a positive value. In the following shapes, nnz is the count after taking thresh into account.

 Args
  a_indices   A Tensor of type int64. 2-D. The indices of the first SparseTensor, size [nnz, ndims] Matrix.
  a_values   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. 1-D. The values of the first SparseTensor, size [nnz] Vector.
  a_shape   A Tensor of type int64. 1-D. The shape of the first SparseTensor, size [ndims] Vector.
  b_indices   A Tensor of type int64. 2-D. The indices of the second SparseTensor, size [nnz, ndims] Matrix.
  b_values   A Tensor. Must have the same type as a_values. 1-D. The values of the second SparseTensor, size [nnz] Vector.
  b_shape   A Tensor of type int64. 1-D. The shape of the second SparseTensor, size [ndims] Vector.
  thresh   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64. 0-D. The magnitude threshold that determines if an output value/index pair takes space.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (sum_indices, sum_values, sum_shape).     sum_indices   A Tensor of type int64.
  sum_values   A Tensor. Has the same type as a_values.
  sum_shape   A Tensor of type int64.


tf.raw_ops.SparseAddGrad The gradient operator for the SparseAdd op.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.SparseAddGrad
tf.raw_ops.SparseAddGrad(
    backprop_val_grad, a_indices, b_indices, sum_indices, name=None
)
 The SparseAdd op calculates A + B, where A, B, and the sum are all represented as SparseTensor objects. This op takes in the upstream gradient w.r.t. non-empty values of the sum, and outputs the gradients w.r.t. the non-empty values of A and B.

 Args
  backprop_val_grad   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. 1-D with shape [nnz(sum)]. The gradient with respect to the non-empty values of the sum.
  a_indices   A Tensor of type int64. 2-D. The indices of the SparseTensor A, size [nnz(A), ndims].
  b_indices   A Tensor of type int64. 2-D. The indices of the SparseTensor B, size [nnz(B), ndims].
  sum_indices   A Tensor of type int64. 2-D. The indices of the sum SparseTensor, size [nnz(sum), ndims].
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (a_val_grad, b_val_grad).     a_val_grad   A Tensor. Has the same type as backprop_val_grad.
  b_val_grad   A Tensor. Has the same type as backprop_val_grad.


tf.raw_ops.SparseApplyAdadelta var: Should be from a Variable().  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.SparseApplyAdadelta
tf.raw_ops.SparseApplyAdadelta(
    var, accum, accum_update, lr, rho, epsilon, grad, indices, use_locking=False,
    name=None
)

 Args
  var
  A mutable Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64.

  accum
  A mutable Tensor. Must have the same type as var. Should be from a Variable().

  accum_update

 A mutable Tensor. Must have the same type as var. Should be from a Variable().

  lr   A Tensor. Must have the same type as var. Learning rate. Must be a scalar.
  rho   A Tensor. Must have the same type as var. Decay factor. Must be a scalar.
  epsilon   A Tensor. Must have the same type as var. Constant factor. Must be a scalar.
  grad   A Tensor. Must have the same type as var. The gradient.
  indices   A Tensor. Must be one of the following types: int32, int64. A vector of indices into the first dimension of var and accum.
  use_locking   An optional bool. Defaults to False. If True, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
  name   A name for the operation (optional).

 Returns   A mutable Tensor. Has the same type as var.


tf.raw_ops.SparseApplyAdagrad   Update relevant entries in 'var' and 'accum' according to the adagrad scheme.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.SparseApplyAdagrad
tf.raw_ops.SparseApplyAdagrad(
    var, accum, lr, grad, indices, use_locking=False, update_slots=True, name=None
)
 That is for rows we have grad for, we update var and accum as follows:  $$accum += grad * grad$$   $$var -= lr * grad * (1 / sqrt(accum))$$

 Args
  var   A mutable Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. Should be from a Variable().
  accum   A mutable Tensor. Must have the same type as var. Should be from a Variable().
  lr   A Tensor. Must have the same type as var. Learning rate. Must be a scalar.
  grad   A Tensor. Must have the same type as var. The gradient.
  indices   A Tensor. Must be one of the following types: int32, int64. A vector of indices into the first dimension of var and accum.
  use_locking   An optional bool. Defaults to False. If True, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
  update_slots   An optional bool. Defaults to True.
  name   A name for the operation (optional).

 Returns   A mutable Tensor. Has the same type as var.


tf.raw_ops.SparseApplyAdagradDA Update entries in 'var' and 'accum' according to the proximal adagrad scheme.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.SparseApplyAdagradDA
tf.raw_ops.SparseApplyAdagradDA(
    var, gradient_accumulator, gradient_squared_accumulator, grad, indices, lr, l1,
    l2, global_step, use_locking=False, name=None
)

 Args
  var   A mutable Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. Should be from a Variable().
  gradient_accumulator   A mutable Tensor. Must have the same type as var. Should be from a Variable().
  gradient_squared_accumulator   A mutable Tensor. Must have the same type as var. Should be from a Variable().
  grad   A Tensor. Must have the same type as var. The gradient.
  indices   A Tensor. Must be one of the following types: int32, int64. A vector of indices into the first dimension of var and accum.
  lr   A Tensor. Must have the same type as var. Learning rate. Must be a scalar.
  l1   A Tensor. Must have the same type as var. L1 regularization. Must be a scalar.
  l2   A Tensor. Must have the same type as var. L2 regularization. Must be a scalar.
  global_step   A Tensor of type int64. Training step number. Must be a scalar.
  use_locking   An optional bool. Defaults to False. If True, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
  name   A name for the operation (optional).

 Returns   A mutable Tensor. Has the same type as var.


tf.raw_ops.SparseApplyAdagradV2   Update relevant entries in 'var' and 'accum' according to the adagrad scheme.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.SparseApplyAdagradV2
tf.raw_ops.SparseApplyAdagradV2(
    var, accum, lr, epsilon, grad, indices, use_locking=False, update_slots=True,
    name=None
)
 That is for rows we have grad for, we update var and accum as follows:  $$accum += grad * grad$$   $$var -= lr * grad * (1 / sqrt(accum))$$

 Args
  var   A mutable Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. Should be from a Variable().
  accum   A mutable Tensor. Must have the same type as var. Should be from a Variable().
  lr   A Tensor. Must have the same type as var. Learning rate. Must be a scalar.
  epsilon   A Tensor. Must have the same type as var. Constant factor. Must be a scalar.
  grad   A Tensor. Must have the same type as var. The gradient.
  indices   A Tensor. Must be one of the following types: int32, int64. A vector of indices into the first dimension of var and accum.
  use_locking   An optional bool. Defaults to False. If True, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
  update_slots   An optional bool. Defaults to True.
  name   A name for the operation (optional).

 Returns   A mutable Tensor. Has the same type as var.


tf.raw_ops.SparseApplyCenteredRMSProp   Update '*var' according to the centered RMSProp algorithm.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.SparseApplyCenteredRMSProp
tf.raw_ops.SparseApplyCenteredRMSProp(
    var, mg, ms, mom, lr, rho, momentum, epsilon, grad, indices, use_locking=False,
    name=None
)
 The centered RMSProp algorithm uses an estimate of the centered second moment (i.e., the variance) for normalization, as opposed to regular RMSProp, which uses the (uncentered) second moment. This often helps with training, but is slightly more expensive in terms of computation and memory. Note that in dense implementation of this algorithm, mg, ms, and mom will update even if the grad is zero, but in this sparse implementation, mg, ms, and mom will not update in iterations during which the grad is zero. mean_square = decay * mean_square + (1-decay) * gradient ** 2 mean_grad = decay * mean_grad + (1-decay) * gradient Delta = learning_rate * gradient / sqrt(mean_square + epsilon - mean_grad ** 2)  $$ms  $$mom  $$var

 Args
  var   A mutable Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. Should be from a Variable().
  mg   A mutable Tensor. Must have the same type as var. Should be from a Variable().
  ms   A mutable Tensor. Must have the same type as var. Should be from a Variable().
  mom   A mutable Tensor. Must have the same type as var. Should be from a Variable().
  lr   A Tensor. Must have the same type as var. Scaling factor. Must be a scalar.
  rho   A Tensor. Must have the same type as var. Decay rate. Must be a scalar.
  momentum   A Tensor. Must have the same type as var.
  epsilon   A Tensor. Must have the same type as var. Ridge term. Must be a scalar.
  grad   A Tensor. Must have the same type as var. The gradient.
  indices   A Tensor. Must be one of the following types: int32, int64. A vector of indices into the first dimension of var, ms and mom.
  use_locking   An optional bool. Defaults to False. If True, updating of the var, mg, ms, and mom tensors is protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
  name   A name for the operation (optional).

 Returns   A mutable Tensor. Has the same type as var.
     Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates. Some content is licensed under the numpy license. Last updated 2021-02-18 UTC.        Stay connected    Blog     GitHub     Twitter     YouTube      Support    Issue tracker     Release notes     Stack Overflow     Brand guidelines     Cite TensorFlow              Terms     Privacy    Sign up for the TensorFlow monthly newsletter  Subscribe       Language  English   中文 – 简体


tf.raw_ops.SparseApplyFtrl   Update relevant entries in '*var' according to the Ftrl-proximal scheme.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.SparseApplyFtrl
tf.raw_ops.SparseApplyFtrl(
    var, accum, linear, grad, indices, lr, l1, l2, lr_power, use_locking=False,
    multiply_linear_by_lr=False, name=None
)
 That is for rows we have grad for, we update var, accum and linear as follows:  $$accum_new = accum + grad * grad$$   $$linear += grad + (accum_{new}^{-lr_{power} } - accum^{-lr_{power} } / lr * var$$   $$quadratic = 1.0 / (accum_{new}^{lr_{power} } * lr) + 2 * l2$$   $$var = (sign(linear) * l1 - linear) / quadratic\ if\ |linear| > l1\ else\ 0.0$$   $$accum = accum_{new}$$

 Args
  var   A mutable Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. Should be from a Variable().
  accum   A mutable Tensor. Must have the same type as var. Should be from a Variable().
  linear   A mutable Tensor. Must have the same type as var. Should be from a Variable().
  grad   A Tensor. Must have the same type as var. The gradient.
  indices   A Tensor. Must be one of the following types: int32, int64. A vector of indices into the first dimension of var and accum.
  lr   A Tensor. Must have the same type as var. Scaling factor. Must be a scalar.
  l1   A Tensor. Must have the same type as var. L1 regularization. Must be a scalar.
  l2   A Tensor. Must have the same type as var. L2 regularization. Must be a scalar.
  lr_power   A Tensor. Must have the same type as var. Scaling factor. Must be a scalar.
  use_locking   An optional bool. Defaults to False. If True, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
  multiply_linear_by_lr   An optional bool. Defaults to False.
  name   A name for the operation (optional).

 Returns   A mutable Tensor. Has the same type as var.


tf.raw_ops.SparseApplyFtrlV2 Update relevant entries in '*var' according to the Ftrl-proximal scheme.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.SparseApplyFtrlV2
tf.raw_ops.SparseApplyFtrlV2(
    var, accum, linear, grad, indices, lr, l1, l2, l2_shrinkage, lr_power,
    use_locking=False, multiply_linear_by_lr=False, name=None
)
 That is for rows we have grad for, we update var, accum and linear as follows: grad_with_shrinkage = grad + 2 * l2_shrinkage * var accum_new = accum + grad * grad linear += grad_with_shrinkage - (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2 var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0 accum = accum_new

 Args
  var   A mutable Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. Should be from a Variable().
  accum   A mutable Tensor. Must have the same type as var. Should be from a Variable().
  linear   A mutable Tensor. Must have the same type as var. Should be from a Variable().
  grad   A Tensor. Must have the same type as var. The gradient.
  indices   A Tensor. Must be one of the following types: int32, int64. A vector of indices into the first dimension of var and accum.
  lr   A Tensor. Must have the same type as var. Scaling factor. Must be a scalar.
  l1   A Tensor. Must have the same type as var. L1 regularization. Must be a scalar.
  l2   A Tensor. Must have the same type as var. L2 shrinkage regularization. Must be a scalar.
  l2_shrinkage   A Tensor. Must have the same type as var.
  lr_power   A Tensor. Must have the same type as var. Scaling factor. Must be a scalar.
  use_locking   An optional bool. Defaults to False. If True, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
  multiply_linear_by_lr   An optional bool. Defaults to False.
  name   A name for the operation (optional).

 Returns   A mutable Tensor. Has the same type as var.


tf.raw_ops.SparseApplyMomentum   Update relevant entries in 'var' and 'accum' according to the momentum scheme.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.SparseApplyMomentum
tf.raw_ops.SparseApplyMomentum(
    var, accum, lr, grad, indices, momentum, use_locking=False, use_nesterov=False,
    name=None
)
 Set use_nesterov = True if you want to use Nesterov momentum. That is for rows we have grad for, we update var and accum as follows:  $$accum = accum * momentum + grad$$   $$var -= lr * accum$$

 Args
  var   A mutable Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. Should be from a Variable().
  accum   A mutable Tensor. Must have the same type as var. Should be from a Variable().
  lr   A Tensor. Must have the same type as var. Learning rate. Must be a scalar.
  grad   A Tensor. Must have the same type as var. The gradient.
  indices   A Tensor. Must be one of the following types: int32, int64. A vector of indices into the first dimension of var and accum.
  momentum   A Tensor. Must have the same type as var. Momentum. Must be a scalar.
  use_locking   An optional bool. Defaults to False. If True, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
  use_nesterov   An optional bool. Defaults to False. If True, the tensor passed to compute grad will be var - lr * momentum * accum, so in the end, the var you get is actually var - lr * momentum * accum.
  name   A name for the operation (optional).

 Returns   A mutable Tensor. Has the same type as var.


tf.raw_ops.SparseApplyProximalAdagrad   Sparse update entries in 'var' and 'accum' according to FOBOS algorithm.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.SparseApplyProximalAdagrad
tf.raw_ops.SparseApplyProximalAdagrad(
    var, accum, lr, l1, l2, grad, indices, use_locking=False, name=None
)
 That is for rows we have grad for, we update var and accum as follows:  $$accum += grad * grad$$   $$prox_v = var$$   $$prox_v -= lr * grad * (1 / sqrt(accum))$$   $$var = sign(prox_v)/(1+lr*l2) * max{|prox_v|-lr*l1,0}$$

 Args
  var   A mutable Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. Should be from a Variable().
  accum   A mutable Tensor. Must have the same type as var. Should be from a Variable().
  lr   A Tensor. Must have the same type as var. Learning rate. Must be a scalar.
  l1   A Tensor. Must have the same type as var. L1 regularization. Must be a scalar.
  l2   A Tensor. Must have the same type as var. L2 regularization. Must be a scalar.
  grad   A Tensor. Must have the same type as var. The gradient.
  indices   A Tensor. Must be one of the following types: int32, int64. A vector of indices into the first dimension of var and accum.
  use_locking   An optional bool. Defaults to False. If True, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
  name   A name for the operation (optional).

 Returns   A mutable Tensor. Has the same type as var.


tf.raw_ops.SparseApplyProximalGradientDescent   Sparse update '*var' as FOBOS algorithm with fixed learning rate.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.SparseApplyProximalGradientDescent
tf.raw_ops.SparseApplyProximalGradientDescent(
    var, alpha, l1, l2, grad, indices, use_locking=False, name=None
)
 That is for rows we have grad for, we update var as follows:  $$prox_v = var - alpha * grad$$   $$var = sign(prox_v)/(1+alpha*l2) * max{|prox_v|-alpha*l1,0}$$

 Args
  var   A mutable Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. Should be from a Variable().
  alpha   A Tensor. Must have the same type as var. Scaling factor. Must be a scalar.
  l1   A Tensor. Must have the same type as var. L1 regularization. Must be a scalar.
  l2   A Tensor. Must have the same type as var. L2 regularization. Must be a scalar.
  grad   A Tensor. Must have the same type as var. The gradient.
  indices   A Tensor. Must be one of the following types: int32, int64. A vector of indices into the first dimension of var and accum.
  use_locking   An optional bool. Defaults to False. If True, the subtraction will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
  name   A name for the operation (optional).

 Returns   A mutable Tensor. Has the same type as var.


tf.raw_ops.SparseApplyRMSProp   Update '*var' according to the RMSProp algorithm.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.SparseApplyRMSProp
tf.raw_ops.SparseApplyRMSProp(
    var, ms, mom, lr, rho, momentum, epsilon, grad, indices, use_locking=False,
    name=None
)
 Note that in dense implementation of this algorithm, ms and mom will update even if the grad is zero, but in this sparse implementation, ms and mom will not update in iterations during which the grad is zero. mean_square = decay * mean_square + (1-decay) * gradient ** 2 Delta = learning_rate * gradient / sqrt(mean_square + epsilon)  $$ms  $$mom  $$var

 Args
  var   A mutable Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. Should be from a Variable().
  ms   A mutable Tensor. Must have the same type as var. Should be from a Variable().
  mom   A mutable Tensor. Must have the same type as var. Should be from a Variable().
  lr   A Tensor. Must have the same type as var. Scaling factor. Must be a scalar.
  rho   A Tensor. Must have the same type as var. Decay rate. Must be a scalar.
  momentum   A Tensor. Must have the same type as var.
  epsilon   A Tensor. Must have the same type as var. Ridge term. Must be a scalar.
  grad   A Tensor. Must have the same type as var. The gradient.
  indices   A Tensor. Must be one of the following types: int32, int64. A vector of indices into the first dimension of var, ms and mom.
  use_locking   An optional bool. Defaults to False. If True, updating of the var, ms, and mom tensors is protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
  name   A name for the operation (optional).

 Returns   A mutable Tensor. Has the same type as var.
     Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates. Some content is licensed under the numpy license. Last updated 2021-02-18 UTC.        Stay connected    Blog     GitHub     Twitter     YouTube      Support    Issue tracker     Release notes     Stack Overflow     Brand guidelines     Cite TensorFlow              Terms     Privacy    Sign up for the TensorFlow monthly newsletter  Subscribe       Language  English   中文 – 简体


tf.raw_ops.SparseBincount Counts the number of occurrences of each value in an integer array.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.SparseBincount
tf.raw_ops.SparseBincount(
    indices, values, dense_shape, size, weights, binary_output=False, name=None
)
 Outputs a vector with length size and the same dtype as weights. If weights are empty, then index i stores the number of times the value i is counted in arr. If weights are non-empty, then index i stores the sum of the value in weights at each index where the corresponding value in arr is i. Values in arr outside of the range [0, size) are ignored.

 Args
  indices   A Tensor of type int64. 2D int64 Tensor.
  values   A Tensor. Must be one of the following types: int32, int64. 1D int Tensor.
  dense_shape   A Tensor of type int64. 1D int64 Tensor.
  size   A Tensor. Must have the same type as values. non-negative int scalar Tensor.
  weights   A Tensor. Must be one of the following types: int32, int64, float32, float64. is an int32, int64, float32, or float64 Tensor with the same shape as input, or a length-0 Tensor, in which case it acts as all weights equal to 1.
  binary_output   An optional bool. Defaults to False. bool; Whether the kernel should count the appearance or number of occurrences.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as weights.


tf.raw_ops.SparseConcat Concatenates a list of SparseTensor along the specified dimension.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.SparseConcat
tf.raw_ops.SparseConcat(
    indices, values, shapes, concat_dim, name=None
)
 Concatenation is with respect to the dense versions of these sparse tensors. It is assumed that each input is a SparseTensor whose elements are ordered along increasing dimension number. All inputs' shapes must match, except for the concat dimension. The indices, values, and shapes lists must have the same length. The output shape is identical to the inputs', except along the concat dimension, where it is the sum of the inputs' sizes along that dimension. The output elements will be resorted to preserve the sort order along increasing dimension number. This op runs in O(M log M) time, where M is the total number of non-empty values across all inputs. This is due to the need for an internal sort in order to concatenate efficiently across an arbitrary dimension. For example, if concat_dim = 1 and the inputs are sp_inputs[0]: shape = [2, 3]
[0, 2]: "a"
[1, 0]: "b"
[1, 1]: "c"

sp_inputs[1]: shape = [2, 4]
[0, 1]: "d"
[0, 2]: "e"
 then the output will be shape = [2, 7]
[0, 2]: "a"
[0, 4]: "d"
[0, 5]: "e"
[1, 0]: "b"
[1, 1]: "c"
 Graphically this is equivalent to doing [    a] concat [  d e  ] = [    a   d e  ]
[b c  ]        [       ]   [b c          ]

 Args
  indices   A list of at least 2 Tensor objects with type int64. 2-D. Indices of each input SparseTensor.
  values   A list with the same length as indices of Tensor objects with the same type. 1-D. Non-empty values of each SparseTensor.
  shapes   A list with the same length as indices of Tensor objects with type int64. 1-D. Shapes of each SparseTensor.
  concat_dim   An int. Dimension to concatenate along. Must be in range [-rank, rank), where rank is the number of dimensions in each input SparseTensor.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (output_indices, output_values, output_shape).     output_indices   A Tensor of type int64.
  output_values   A Tensor. Has the same type as values.
  output_shape   A Tensor of type int64.


tf.raw_ops.SparseConditionalAccumulator A conditional accumulator for aggregating sparse gradients.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.SparseConditionalAccumulator
tf.raw_ops.SparseConditionalAccumulator(
    dtype, shape, container='', shared_name='',
    reduction_type='MEAN', name=None
)
 The accumulator accepts gradients marked with local_step greater or equal to the most recent global_step known to the accumulator. The average can be extracted from the accumulator, provided sufficient gradients have been accumulated. Extracting the average automatically resets the aggregate to 0, and increments the global_step recorded by the accumulator.

 Args
  dtype   A tf.DType from: tf.float32, tf.float64, tf.int32, tf.uint8, tf.int16, tf.int8, tf.complex64, tf.int64, tf.qint8, tf.quint8, tf.qint32, tf.bfloat16, tf.uint16, tf.complex128, tf.half, tf.uint32, tf.uint64. The type of the value being accumulated.
  shape   A tf.TensorShape or list of ints. The shape of the values.
  container   An optional string. Defaults to "". If non-empty, this accumulator is placed in the given container. Otherwise, a default container is used.
  shared_name   An optional string. Defaults to "". If non-empty, this accumulator will be shared under the given name across multiple sessions.
  reduction_type   An optional string from: "MEAN", "SUM". Defaults to "MEAN".
  name   A name for the operation (optional).

 Returns   A Tensor of type mutable string.