neulab/docprompting-conala huggingface dataset:
tf.raw_ops.ResourceSparseApplyAdagrad 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.ResourceSparseApplyAdagrad  
tf.raw_ops.ResourceSparseApplyAdagrad(
    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 Tensor of type resource. Should be from a Variable().  
  accum   A Tensor of type resource. Should be from a Variable().  
  lr   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. Learning rate. Must be a scalar.  
  grad   A Tensor. Must have the same type as lr. 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   The created Operation.
tf.raw_ops.ResourceSparseApplyAdagradDA 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.ResourceSparseApplyAdagradDA  
tf.raw_ops.ResourceSparseApplyAdagradDA(
    var, gradient_accumulator, gradient_squared_accumulator, grad, indices, lr, l1,
    l2, global_step, use_locking=False, name=None
)

 


 Args
  var   A Tensor of type resource. Should be from a Variable().  
  gradient_accumulator   A Tensor of type resource. Should be from a Variable().  
  gradient_squared_accumulator   A Tensor of type resource. Should be from a Variable().  
  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. 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 grad. Learning rate. Must be a scalar.  
  l1   A Tensor. Must have the same type as grad. L1 regularization. Must be a scalar.  
  l2   A Tensor. Must have the same type as grad. 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   The created Operation.
tf.raw_ops.ResourceSparseApplyAdagradV2 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.ResourceSparseApplyAdagradV2  
tf.raw_ops.ResourceSparseApplyAdagradV2(
    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 Tensor of type resource. Should be from a Variable().  
  accum   A Tensor of type resource. Should be from a Variable().  
  lr   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. Learning rate. Must be a scalar.  
  epsilon   A Tensor. Must have the same type as lr. Constant factor. Must be a scalar.  
  grad   A Tensor. Must have the same type as lr. 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   The created Operation.
tf.raw_ops.ResourceSparseApplyCenteredRMSProp 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.ResourceSparseApplyCenteredRMSProp  
tf.raw_ops.ResourceSparseApplyCenteredRMSProp(
    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 <- rho * ms{t-1} + (1-rho) * grad * grad mom <- momentum * mom{t-1} + lr * grad / sqrt(ms + epsilon) var <- var - mom
 


 Args
  var   A Tensor of type resource. Should be from a Variable().  
  mg   A Tensor of type resource. Should be from a Variable().  
  ms   A Tensor of type resource. Should be from a Variable().  
  mom   A Tensor of type resource. Should be from a Variable().  
  lr   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. Scaling factor. Must be a scalar.  
  rho   A Tensor. Must have the same type as lr. Decay rate. Must be a scalar.  
  momentum   A Tensor. Must have the same type as lr.  
  epsilon   A Tensor. Must have the same type as lr. Ridge term. Must be a scalar.  
  grad   A Tensor. Must have the same type as lr. 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   The created Operation.
tf.raw_ops.ResourceSparseApplyFtrl 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.ResourceSparseApplyFtrl  
tf.raw_ops.ResourceSparseApplyFtrl(
    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 Tensor of type resource. Should be from a Variable().  
  accum   A Tensor of type resource. Should be from a Variable().  
  linear   A Tensor of type resource. Should be from a Variable().  
  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. 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 grad. Scaling factor. Must be a scalar.  
  l1   A Tensor. Must have the same type as grad. L1 regularization. Must be a scalar.  
  l2   A Tensor. Must have the same type as grad. L2 regularization. Must be a scalar.  
  lr_power   A Tensor. Must have the same type as grad. 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   The created Operation.
tf.raw_ops.ResourceSparseApplyFtrlV2 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.ResourceSparseApplyFtrlV2  
tf.raw_ops.ResourceSparseApplyFtrlV2(
    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_with_shrinkage * grad_with_shrinkage 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 Tensor of type resource. Should be from a Variable().  
  accum   A Tensor of type resource. Should be from a Variable().  
  linear   A Tensor of type resource. Should be from a Variable().  
  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. 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 grad. Scaling factor. Must be a scalar.  
  l1   A Tensor. Must have the same type as grad. L1 regularization. Must be a scalar.  
  l2   A Tensor. Must have the same type as grad. L2 shrinkage regularization. Must be a scalar.  
  l2_shrinkage   A Tensor. Must have the same type as grad.  
  lr_power   A Tensor. Must have the same type as grad. 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   The created Operation.
tf.raw_ops.ResourceSparseApplyKerasMomentum 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.ResourceSparseApplyKerasMomentum  
tf.raw_ops.ResourceSparseApplyKerasMomentum(
    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 - lr * grad var += accum
 


 Args
  var   A Tensor of type resource. Should be from a Variable().  
  accum   A Tensor of type resource. Should be from a Variable().  
  lr   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. Learning rate. Must be a scalar.  
  grad   A Tensor. Must have the same type as lr. 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 lr. 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 + momentum * accum, so in the end, the var you get is actually var + momentum * accum.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.ResourceSparseApplyMomentum 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.ResourceSparseApplyMomentum  
tf.raw_ops.ResourceSparseApplyMomentum(
    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 Tensor of type resource. Should be from a Variable().  
  accum   A Tensor of type resource. Should be from a Variable().  
  lr   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. Learning rate. Must be a scalar.  
  grad   A Tensor. Must have the same type as lr. 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 lr. 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   The created Operation.
tf.raw_ops.ResourceSparseApplyProximalAdagrad 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.ResourceSparseApplyProximalAdagrad  
tf.raw_ops.ResourceSparseApplyProximalAdagrad(
    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 Tensor of type resource. Should be from a Variable().  
  accum   A Tensor of type resource. Should be from a Variable().  
  lr   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. Learning rate. Must be a scalar.  
  l1   A Tensor. Must have the same type as lr. L1 regularization. Must be a scalar.  
  l2   A Tensor. Must have the same type as lr. L2 regularization. Must be a scalar.  
  grad   A Tensor. Must have the same type as lr. 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   The created Operation.
tf.raw_ops.ResourceSparseApplyProximalGradientDescent 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.ResourceSparseApplyProximalGradientDescent  
tf.raw_ops.ResourceSparseApplyProximalGradientDescent(
    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 Tensor of type resource. Should be from a Variable().  
  alpha   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. Scaling factor. Must be a scalar.  
  l1   A Tensor. Must have the same type as alpha. L1 regularization. Must be a scalar.  
  l2   A Tensor. Must have the same type as alpha. L2 regularization. Must be a scalar.  
  grad   A Tensor. Must have the same type as alpha. 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   The created Operation.
tf.raw_ops.ResourceSparseApplyRMSProp Update '*var' according to the RMSProp algorithm.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceSparseApplyRMSProp  
tf.raw_ops.ResourceSparseApplyRMSProp(
    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 <- rho * ms{t-1} + (1-rho) * grad * grad mom <- momentum * mom{t-1} + lr * grad / sqrt(ms + epsilon) var <- var - mom
 


 Args
  var   A Tensor of type resource. Should be from a Variable().  
  ms   A Tensor of type resource. Should be from a Variable().  
  mom   A Tensor of type resource. Should be from a Variable().  
  lr   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. Scaling factor. Must be a scalar.  
  rho   A Tensor. Must have the same type as lr. Decay rate. Must be a scalar.  
  momentum   A Tensor. Must have the same type as lr.  
  epsilon   A Tensor. Must have the same type as lr. Ridge term. Must be a scalar.  
  grad   A Tensor. Must have the same type as lr. 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   The created Operation.
tf.raw_ops.ResourceStridedSliceAssign Assign value to the sliced l-value reference of ref.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceStridedSliceAssign  
tf.raw_ops.ResourceStridedSliceAssign(
    ref, begin, end, strides, value, begin_mask=0, end_mask=0, ellipsis_mask=0,
    new_axis_mask=0, shrink_axis_mask=0, name=None
)
 The values of value are assigned to the positions in the variable ref that are selected by the slice parameters. The slice parameters begin,end,strides, etc. work exactly as inStridedSlice`. NOTE this op currently does not support broadcasting and so value's shape must be exactly the shape produced by the slice of ref.
 


 Args
  ref   A Tensor of type resource.  
  begin   A Tensor. Must be one of the following types: int32, int64.  
  end   A Tensor. Must have the same type as begin.  
  strides   A Tensor. Must have the same type as begin.  
  value   A Tensor.  
  begin_mask   An optional int. Defaults to 0.  
  end_mask   An optional int. Defaults to 0.  
  ellipsis_mask   An optional int. Defaults to 0.  
  new_axis_mask   An optional int. Defaults to 0.  
  shrink_axis_mask   An optional int. Defaults to 0.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.Restore Restores a tensor from checkpoint files.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Restore  
tf.raw_ops.Restore(
    file_pattern, tensor_name, dt, preferred_shard=-1, name=None
)
 Reads a tensor stored in one or several files. If there are several files (for instance because a tensor was saved as slices), file_pattern may contain wildcard symbols (* and ?) in the filename portion only, not in the directory portion. If a file_pattern matches several files, preferred_shard can be used to hint in which file the requested tensor is likely to be found. This op will first open the file at index preferred_shard in the list of matching files and try to restore tensors from that file. Only if some tensors or tensor slices are not found in that first file, then the Op opens all the files. Setting preferred_shard to match the value passed as the shard input of a matching Save Op may speed up Restore. This attribute only affects performance, not correctness. The default value -1 means files are processed in order. See also RestoreSlice.
 


 Args
  file_pattern   A Tensor of type string. Must have a single element. The pattern of the files from which we read the tensor.  
  tensor_name   A Tensor of type string. Must have a single element. The name of the tensor to be restored.  
  dt   A tf.DType. The type of the tensor to be restored.  
  preferred_shard   An optional int. Defaults to -1. Index of file to open first if multiple files match file_pattern.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type dt.
tf.raw_ops.RestoreSlice Restores a tensor from checkpoint files.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RestoreSlice  
tf.raw_ops.RestoreSlice(
    file_pattern, tensor_name, shape_and_slice, dt, preferred_shard=-1, name=None
)
 This is like Restore except that restored tensor can be listed as filling only a slice of a larger tensor. shape_and_slice specifies the shape of the larger tensor and the slice that the restored tensor covers. The shape_and_slice input has the same format as the elements of the shapes_and_slices input of the SaveSlices op.
 


 Args
  file_pattern   A Tensor of type string. Must have a single element. The pattern of the files from which we read the tensor.  
  tensor_name   A Tensor of type string. Must have a single element. The name of the tensor to be restored.  
  shape_and_slice   A Tensor of type string. Scalar. The shapes and slice specifications to use when restoring a tensors.  
  dt   A tf.DType. The type of the tensor to be restored.  
  preferred_shard   An optional int. Defaults to -1. Index of file to open first if multiple files match file_pattern. See the documentation for Restore.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type dt.