The `network.Network.SGD` class in Python is a wrapper for the stochastic gradient descent optimizer. It is commonly used for training neural networks by iteratively updating the network's weights based on the calculated gradients of the loss function with respect to those weights. This class provides methods to set and retrieve the learning rate, momentum, weight decay, and other parameters used in the optimization process. By using the `network.Network.SGD` class, users can efficiently optimize their neural network models by specifying the appropriate settings for the stochastic gradient descent algorithm.
Python Network.SGD - 30 examples found. These are the top rated real world Python examples of network.Network.SGD extracted from open source projects. You can rate examples to help us improve the quality of examples.