Optimizer
Defined in tensorflow/python/keras/_impl/keras/optimizers.py
.
Abstract optimizer base class.
Note: this is the parent class of all optimizers, not an actual optimizer that can be used for training models.
All Keras optimizers support the following keyword arguments:
clipnorm: float >= 0. Gradients will be clipped when their L2 norm exceeds this value. clipvalue: float >= 0. Gradients will be clipped when their absolute value exceeds this value.
__init__
__init__(**kwargs)
Initialize self. See help(type(self)) for accurate signature.
from_config
@classmethod from_config( cls, config )
get_config
get_config()
get_gradients
get_gradients( loss, params )
Returns gradients of loss
with respect to params
.
loss
: Loss tensor.params
: List of variables.List of gradient tensors.
ValueError
: In case any gradient cannot be computed (e.g. if gradient function not implemented).get_updates
get_updates( loss, params )
get_weights
get_weights()
Returns the current value of the weights of the optimizer.
A list of numpy arrays.
set_weights
set_weights(weights)
Sets the weights of the optimizer, from Numpy arrays.
Should only be called after computing the gradients (otherwise the optimizer has no weights).
weights
: a list of Numpy arrays. The number of arrays and their shape must match number of the dimensions of the weights of the optimizer (i.e. it should match the output of get_weights
).ValueError
: in case of incompatible weight shapes.
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/Optimizer