neulab/docprompting-conala huggingface dataset:
tf.raw_ops.ResourceApplyAdagradDA Update '*var' according to the proximal adagrad scheme.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceApplyAdagradDA  
tf.raw_ops.ResourceApplyAdagradDA(
    var, gradient_accumulator, gradient_squared_accumulator, grad, 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.  
  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.  
  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.ResourceApplyAdagradV2 Update '*var' according to the adagrad scheme.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceApplyAdagradV2  
tf.raw_ops.ResourceApplyAdagradV2(
    var, accum, lr, epsilon, grad, use_locking=False, update_slots=True, name=None
)
 accum += grad * grad var -= lr * grad * (1 / (sqrt(accum) + epsilon))
 


 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. Scaling factor. 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.  
  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.ResourceApplyAdam   Update '*var' according to the Adam algorithm.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceApplyAdam  
tf.raw_ops.ResourceApplyAdam(
    var, m, v, beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad,
    use_locking=False, use_nesterov=False, name=None
)
  $$\text{lr}_t := \mathrm{learning_rate} * \sqrt{1 - \beta_2^t} / (1 - \beta_1^t)$$   $$m_t := \beta_1 * m_{t-1} + (1 - \beta_1) * g$$   $$v_t := \beta_2 * v_{t-1} + (1 - \beta_2) * g * g$$   $$\text{variable} := \text{variable} - \text{lr}_t * m_t / (\sqrt{v_t} + \epsilon)$$ 
 


 Args
  var   A Tensor of type resource. Should be from a Variable().  
  m   A Tensor of type resource. Should be from a Variable().  
  v   A Tensor of type resource. Should be from a Variable().  
  beta1_power   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. Must be a scalar.  
  beta2_power   A Tensor. Must have the same type as beta1_power. Must be a scalar.  
  lr   A Tensor. Must have the same type as beta1_power. Scaling factor. Must be a scalar.  
  beta1   A Tensor. Must have the same type as beta1_power. Momentum factor. Must be a scalar.  
  beta2   A Tensor. Must have the same type as beta1_power. Momentum factor. Must be a scalar.  
  epsilon   A Tensor. Must have the same type as beta1_power. Ridge term. Must be a scalar.  
  grad   A Tensor. Must have the same type as beta1_power. The gradient.  
  use_locking   An optional bool. Defaults to False. If True, updating of the var, m, and v 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, uses the nesterov update.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.ResourceApplyAdaMax Update '*var' according to the AdaMax algorithm.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceApplyAdaMax  
tf.raw_ops.ResourceApplyAdaMax(
    var, m, v, beta1_power, lr, beta1, beta2, epsilon, grad, use_locking=False,
    name=None
)
 mt <- beta1 * m{t-1} + (1 - beta1) * g vt <- max(beta2 * v{t-1}, abs(g)) variable <- variable - learning_rate / (1 - beta1^t) * m_t / (v_t + epsilon)
 


 Args
  var   A Tensor of type resource. Should be from a Variable().  
  m   A Tensor of type resource. Should be from a Variable().  
  v   A Tensor of type resource. Should be from a Variable().  
  beta1_power   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. Must be a scalar.  
  lr   A Tensor. Must have the same type as beta1_power. Scaling factor. Must be a scalar.  
  beta1   A Tensor. Must have the same type as beta1_power. Momentum factor. Must be a scalar.  
  beta2   A Tensor. Must have the same type as beta1_power. Momentum factor. Must be a scalar.  
  epsilon   A Tensor. Must have the same type as beta1_power. Ridge term. Must be a scalar.  
  grad   A Tensor. Must have the same type as beta1_power. The gradient.  
  use_locking   An optional bool. Defaults to False. If True, updating of the var, m, and v 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.ResourceApplyAdamWithAmsgrad   Update '*var' according to the Adam algorithm.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceApplyAdamWithAmsgrad  
tf.raw_ops.ResourceApplyAdamWithAmsgrad(
    var, m, v, vhat, beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad,
    use_locking=False, name=None
)
  $$\text{lr}_t := \mathrm{learning_rate} * \sqrt{1 - \beta_2^t} / (1 - \beta_1^t)$$   $$m_t := \beta_1 * m_{t-1} + (1 - \beta_1) * g$$   $$v_t := \beta_2 * v_{t-1} + (1 - \beta_2) * g * g$$   $$\hat{v}_t := max{\hat{v}_{t-1}, v_t}$$   $$\text{variable} := \text{variable} - \text{lr}_t * m_t / (\sqrt{\hat{v}_t} + \epsilon)$$ 
 


 Args
  var   A Tensor of type resource. Should be from a Variable().  
  m   A Tensor of type resource. Should be from a Variable().  
  v   A Tensor of type resource. Should be from a Variable().  
  vhat   A Tensor of type resource. Should be from a Variable().  
  beta1_power   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. Must be a scalar.  
  beta2_power   A Tensor. Must have the same type as beta1_power. Must be a scalar.  
  lr   A Tensor. Must have the same type as beta1_power. Scaling factor. Must be a scalar.  
  beta1   A Tensor. Must have the same type as beta1_power. Momentum factor. Must be a scalar.  
  beta2   A Tensor. Must have the same type as beta1_power. Momentum factor. Must be a scalar.  
  epsilon   A Tensor. Must have the same type as beta1_power. Ridge term. Must be a scalar.  
  grad   A Tensor. Must have the same type as beta1_power. The gradient.  
  use_locking   An optional bool. Defaults to False. If True, updating of the var, m, and v 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.ResourceApplyAddSign Update '*var' according to the AddSign update.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceApplyAddSign  
tf.raw_ops.ResourceApplyAddSign(
    var, m, lr, alpha, sign_decay, beta, grad, use_locking=False, name=None
)
 mt <- beta1 * m{t-1} + (1 - beta1) * g update <- (alpha + sign_decay * sign(g) *sign(m)) * g variable <- variable - lr_t * update
 


 Args
  var   A Tensor of type resource. Should be from a Variable().  
  m   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.  
  alpha   A Tensor. Must have the same type as lr. Must be a scalar.  
  sign_decay   A Tensor. Must have the same type as lr. Must be a scalar.  
  beta   A Tensor. Must have the same type as lr. Must be a scalar.  
  grad   A Tensor. Must have the same type as lr. The gradient.  
  use_locking   An optional bool. Defaults to False. If True, updating of the var and m 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.ResourceApplyCenteredRMSProp 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.ResourceApplyCenteredRMSProp  
tf.raw_ops.ResourceApplyCenteredRMSProp(
    var, mg, ms, mom, lr, rho, momentum, epsilon, grad, 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) mg <- rho * mg{t-1} + (1-rho) * grad ms <- rho * ms{t-1} + (1-rho) * grad * grad mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms - mg * mg + 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. Momentum Scale. Must be a scalar.  
  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.  
  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.ResourceApplyFtrl Update '*var' according to the Ftrl-proximal scheme.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceApplyFtrl  
tf.raw_ops.ResourceApplyFtrl(
    var, accum, linear, grad, lr, l1, l2, lr_power, use_locking=False,
    multiply_linear_by_lr=False, name=None
)
 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.  
  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.ResourceApplyFtrlV2 Update '*var' according to the Ftrl-proximal scheme.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceApplyFtrlV2  
tf.raw_ops.ResourceApplyFtrlV2(
    var, accum, linear, grad, lr, l1, l2, l2_shrinkage, lr_power, use_locking=False,
    multiply_linear_by_lr=False, name=None
)
 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.  
  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.ResourceApplyGradientDescent Update '*var' by subtracting 'alpha' * 'delta' from it.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceApplyGradientDescent  
tf.raw_ops.ResourceApplyGradientDescent(
    var, alpha, delta, use_locking=False, name=None
)

 


 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.  
  delta   A Tensor. Must have the same type as alpha. The change.  
  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.ResourceApplyKerasMomentum Update '*var' according to the momentum scheme.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceApplyKerasMomentum  
tf.raw_ops.ResourceApplyKerasMomentum(
    var, accum, lr, grad, momentum, use_locking=False, use_nesterov=False, name=None
)
 Set use_nesterov = True if you want to use Nesterov momentum. 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. Scaling factor. Must be a scalar.  
  grad   A Tensor. Must have the same type as lr. The gradient.  
  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.ResourceApplyMomentum Update '*var' according to the momentum scheme.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceApplyMomentum  
tf.raw_ops.ResourceApplyMomentum(
    var, accum, lr, grad, momentum, use_locking=False, use_nesterov=False, name=None
)
 Set use_nesterov = True if you want to use Nesterov momentum. 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. Scaling factor. Must be a scalar.  
  grad   A Tensor. Must have the same type as lr. The gradient.  
  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.ResourceApplyPowerSign Update '*var' according to the AddSign update.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceApplyPowerSign  
tf.raw_ops.ResourceApplyPowerSign(
    var, m, lr, logbase, sign_decay, beta, grad, use_locking=False, name=None
)
 mt <- beta1 * m{t-1} + (1 - beta1) * g update <- exp(logbase * sign_decay * sign(g) * sign(m_t)) * g variable <- variable - lr_t * update
 


 Args
  var   A Tensor of type resource. Should be from a Variable().  
  m   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.  
  logbase   A Tensor. Must have the same type as lr. Must be a scalar.  
  sign_decay   A Tensor. Must have the same type as lr. Must be a scalar.  
  beta   A Tensor. Must have the same type as lr. Must be a scalar.  
  grad   A Tensor. Must have the same type as lr. The gradient.  
  use_locking   An optional bool. Defaults to False. If True, updating of the var and m 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.ResourceApplyProximalAdagrad Update 'var' and 'accum' according to FOBOS with Adagrad learning rate.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceApplyProximalAdagrad  
tf.raw_ops.ResourceApplyProximalAdagrad(
    var, accum, lr, l1, l2, grad, use_locking=False, name=None
)
 accum += grad * grad prox_v = var - 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. Scaling factor. 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.  
  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.ResourceApplyProximalGradientDescent 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.ResourceApplyProximalGradientDescent  
tf.raw_ops.ResourceApplyProximalGradientDescent(
    var, alpha, l1, l2, delta, use_locking=False, name=None
)
 prox_v = var - alpha * delta 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.  
  delta   A Tensor. Must have the same type as alpha. The change.  
  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.