Update relevant entries in '*var' and '*accum' according to the momentum scheme.

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` . |

© 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/r2.4/api_docs/python/tf/raw_ops/SparseApplyMomentum