tf.compat.v1.tpu.experimental.StochasticGradientDescentParameters
Optimization parameters for stochastic gradient descent for TPU embeddings.
tf.compat.v1.tpu.experimental.StochasticGradientDescentParameters( learning_rate, clip_weight_min=None, clip_weight_max=None, weight_decay_factor=None, multiply_weight_decay_factor_by_learning_rate=None )
Pass this to tf.estimator.tpu.experimental.EmbeddingConfigSpec
via the optimization_parameters
argument to set the optimizer and its parameters. See the documentation for tf.estimator.tpu.experimental.EmbeddingConfigSpec
for more details.
estimator = tf.estimator.tpu.TPUEstimator( ... embedding_config_spec=tf.estimator.tpu.experimental.EmbeddingConfigSpec( ... optimization_parameters=( tf.tpu.experimental.StochasticGradientDescentParameters(0.1))))
Args | |
---|---|
learning_rate |
a floating point value. The learning rate. |
clip_weight_min |
the minimum value to clip by; None means -infinity. |
clip_weight_max |
the maximum value to clip by; None means +infinity. |
weight_decay_factor |
amount of weight decay to apply; None means that the weights are not decayed. |
multiply_weight_decay_factor_by_learning_rate |
if true, weight_decay_factor is multiplied by the current learning rate. |
© 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/compat/v1/tpu/experimental/StochasticGradientDescentParameters