Update '*var' according to the adagrad scheme.
tf.raw_ops.ResourceApplyAdagrad( var, accum, lr, grad, use_locking=False, update_slots=True, name=None )
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 . Scaling 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. |
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/raw_ops/ResourceApplyAdagrad