weight decay

Weight attenuation is a common method to fit the problem.\ (l_2\)Norm RegularizationIn deep learning, we often use the L2 norm regularization, which is to add L2 norm penalty on the basis of the original loss function of the model, so as to get the

Weight Update In front of the reverse propagation we calculate the weight of each layer W and offset B of the partial derivative, the last step is to the weight and bias of the update. In the introduction of the previous BP algorithm, we give the

Introducing libraries, defining various parameters From __future__ import Absolute_import to __future__ Import division from __future__ import print_function import OS im Port re import sys import tarfile from six.moves import urllib import

Migration learning, with off-the-shelf network, run their own data: to retain the network in addition to the output layer of the weight of other layers, change the existing network output layer output class number. Train your network based on

TensorFlow Neural Network Optimization Strategy Learning, tensorflow Network Optimization During the optimization of the neural network model, we will encounter many problems, such as how to set the learning rate. We can quickly approach the optimal

http://blog.csdn.net/lien0906/article/details/47399823 excerpt from this blogIn August 15, the Adam method was added to the Caffe. Stochastic Gradient descent (SGD) Parameters for SGD When using a learning method with random gradient descent

Artificial neural Network (ANN), or neural network, is a mathematical model or a computational model for simulating the structure and function of biological neural networks. Neural networks are computed by a large number of artificial neuron

Reprint Please specify source: http://www.cnblogs.com/ymingjingr/p/4271742.htmlDirectory machine Learning Cornerstone Note When you can use machine learning (1) Machine learning Cornerstone Note 2--When you can use machine learning (2) Machine

Today learned the next TensorFlow official website on the CIFAR10 section, found some API has not seen before, here to tidy up a bit.CIFAR10 Tutorial Address 1. The first is the initialization of some parameters FLAGS = Tf.app.flags.FLAGS # Basic

In machine learning or pattern recognition, there will be overfitting, and when the network gradually overfitting, the network weights gradually become larger, therefore, in order to avoid the occurrence of overfitting, the error function will be