layout: post
title: 深度学习
subtitle: 梯度下降算法实现
description: 梯度下降算法实现
date: 2022-10-25
categories: deeplearning
tags: code pytorch
comments: true
摘录自CS231N
def sgd(w, dw, config=None):
"""
Performs vanilla stochastic gradient descent.
config format:
- learning_rate: Scalar learning rate.
"""
if config is None:
config = {}
config.setdefault("learning_rate", 1e-2)
w -= config["learning_rate"] * dw
return w, config
def sgd_momentum(w, dw, config=None):
"""
Performs stochastic gradient descent with momentum.
config format:
- learning_rate: Scalar learning rate.
- momentum: Scalar between 0 and 1 giving the momentum value.
Setting momentum = 0 reduces to sgd.
- velocity: A numpy array of the same shape as w and dw used to store a
moving average of the gradients.
"""
if config is None:
config = {}
config.setdefault("learning_rate", 1e-2)
config.setdefault("momentum", 0.9)
v = config.get("velocity", np.zeros_like(w))
next_w = None
###########################################################################
# TODO: Implement the momentum update formula. Store the updated value in #
# the next_w variable. You should also use and update the velocity v. #
###########################################################################
# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
pass
v = config['momentum'] * v + (1 - config['momentum']) * dw
next_w = w - config['learning_rate'] * v
# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
###########################################################################
# END OF YOUR CODE #
###########################################################################
config["velocity"] = v
return next_w, config
def rmsprop(w, dw, config=None):
"""
Uses the RMSProp update rule, which uses a moving average of squared
gradient values to set adaptive per-parameter learning rates.
config format:
- learning_rate: Scalar learning rate.
- decay_rate: Scalar between 0 and 1 giving the decay rate for the squared
gradient cache.
- epsilon: Small scalar used for smoothing to avoid dividing by zero.
- cache: Moving average of second moments of gradients.
"""
if config is None:
config = {}
config.setdefault("learning_rate", 1e-2)
config.setdefault("decay_rate", 0.99)
config.setdefault("epsilon", 1e-8)
config.setdefault("cache", np.zeros_like(w))
next_w = None
###########################################################################
# TODO: Implement the RMSprop update formula, storing the next value of w #
# in the next_w variable. Don't forget to update cache value stored in #
# config['cache']. #
###########################################################################
# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
pass
config['cache'] = config['decay_rate'] * config['cache'] + (1 - config['decay_rate']) * dw ** 2
next_w = w - config['learning_rate'] * dw / (np.sqrt(config['cache']) + config['epsilon'])
# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
###########################################################################
# END OF YOUR CODE #
###########################################################################
return next_w, config
def adam(w, dw, config=None):
"""
Uses the Adam update rule, which incorporates moving averages of both the
gradient and its square and a bias correction term.
config format:
- learning_rate: Scalar learning rate.
- beta1: Decay rate for moving average of first moment of gradient.
- beta2: Decay rate for moving average of second moment of gradient.
- epsilon: Small scalar used for smoothing to avoid dividing by zero.
- m: Moving average of gradient.
- v: Moving average of squared gradient.
- t: Iteration number.
"""
if config is None:
config = {}
config.setdefault("learning_rate", 1e-3)
config.setdefault("beta1", 0.9)
config.setdefault("beta2", 0.999)
config.setdefault("epsilon", 1e-8)
config.setdefault("m", np.zeros_like(w))
config.setdefault("v", np.zeros_like(w))
config.setdefault("t", 0)
next_w = None
###########################################################################
# TODO: Implement the Adam update formula, storing the next value of w in #
# the next_w variable. Don't forget to update the m, v, and t variables #
# stored in config. #
# #
# NOTE: In order to match the reference output, please modify t _before_ #
# using it in any calculations. #
###########################################################################
# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
pass
config['t'] += 1
config['m'] = config['beta1'] * config['m'] + (1 - config['beta1']) * dw
config['v'] = config['beta2'] * config['v'] + (1 - config['beta2']) * dw ** 2
next_w = w - config['learning_rate'] * config['m'] / (np.sqrt(config['v']) + config['epsilon'])
# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
###########################################################################
# END OF YOUR CODE #
###########################################################################
return next_w, config