Update '*var' according to the AdaMax algorithm.

tf.raw_ops.ApplyAdaMax( var, m, v, beta1_power, lr, beta1, beta2, epsilon, grad, use_locking=False, name=None )

m*t <- beta1 * m*{t-1} + (1 - beta1) * g v*t <- max(beta2 * v*{t-1}, abs(g)) variable <- variable - learning_rate / (1 - beta1^t) * m_t / (v_t + epsilon)

Args | |
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`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(). |

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

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

`beta1_power` | A `Tensor` . Must have the same type as `var` . Must be a scalar. |

`lr` | A `Tensor` . Must have the same type as `var` . Scaling factor. Must be a scalar. |

`beta1` | A `Tensor` . Must have the same type as `var` . Momentum factor. Must be a scalar. |

`beta2` | A `Tensor` . Must have the same type as `var` . Momentum factor. Must be a scalar. |

`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. |

`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 | |
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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/ApplyAdaMax