动手实现深度学习（8）：基于计算图构建的神经网络

传送门： https://www.cnblogs.com/greentomlee/p/12314064.html

github: Leezhen2014: https://github.com/Leezhen2014/python_deep_learning

5.5 Softmax-with-Loss 层

SoftMax函数会将函数正则话以后在输出，比如在手写体识别 中，输出层一般是softmax。考虑到里面也使用了交叉熵(cross entroy error)，所以命名为softmax-with-loss。

  1 class SoftmaxWithLoss:
  2     def __init__(self):
  3         self.loss = None
  4         self.y = None  # softmax的输出
  5         self.t = None  # 标签函数
  6 
  7     def forward(self, x, t):
  8         self.t = t
  9         self.y = softmax(x)
 10         self.loss = cross_entropy_error(self.y, self.t)
 11 
 12         return self.loss
 13 
 14     def backward(self, dout=1):
 15         batch_size = self.t.shape[0]
 16         if self.t.size == self.y.size:  # 处理one-hot
 17             dx = (self.y - self.t) / batch_size
 18         else:
 19             dx = self.y.copy()
 20             dx[np.arange(batch_size), self.t] -= 1
 21             dx = dx / batch_size
 22 
 23         return dx

 

5.6 基于计算图构建的神经网络

前几篇已经介绍了基于计算图的各种基本操作，这次将这些操作 组装一下：

 

  1 # -*- coding: utf-8 -*-
  2 # @File  : two_layer_net_v2.py
  3 # @Author: lizhen
  4 # @Date  : 2020/1/28
  5 # @Desc  : 这个是使用计算图的网络
  6 import numpy as np
  7 from src.common.layers import *
  8 from src.common.gradient import numerical_gradient
  9 from collections import OrderedDict
 10 
 11 
 12 class TwoLayerNet:
 13 
 14     def __init__(self, input_size, hidden_size, output_size, weight_init_std = 0.01):
 15         # 初始化成员
 16         self.params = {}
 17         self.params['W1'] = weight_init_std * np.random.randn(input_size, hidden_size)
 18         self.params['b1'] = np.zeros(hidden_size)
 19         self.params['W2'] = weight_init_std * np.random.randn(hidden_size, output_size)
 20         self.params['b2'] = np.zeros(output_size)
 21 
 22         # 网络层
 23         self.layers = OrderedDict()
 24         self.layers['Affine1'] = Affine(self.params['W1'], self.params['b1'])
 25         self.layers['Relu1'] = Relu()
 26         self.layers['Affine2'] = Affine(self.params['W2'], self.params['b2'])
 27 
 28         self.lastLayer = SoftmaxWithLoss()
 29 
 30     def predict(self, x):
 31         for layer in self.layers.values():
 32             x = layer.forward(x)
 33 
 34         return x
 35 
 36     def loss(self, x, t):
 37         y = self.predict(x)
 38         return self.lastLayer.forward(y, t)
 39 
 40     def accuracy(self, x, t):
 41         y = self.predict(x)
 42         y = np.argmax(y, axis=1)
 43         if t.ndim != 1 : t = np.argmax(t, axis=1)
 44 
 45         accuracy = np.sum(y == t) / float(x.shape[0])
 46         return accuracy
 47 
 48 
 49     def numerical_gradient(self, x, t):
 50         loss_W = lambda W: self.loss(x, t)
 51 
 52         grads = {}
 53         grads['W1'] = numerical_gradient(loss_W, self.params['W1'])
 54         grads['b1'] = numerical_gradient(loss_W, self.params['b1'])
 55         grads['W2'] = numerical_gradient(loss_W, self.params['W2'])
 56         grads['b2'] = numerical_gradient(loss_W, self.params['b2'])
 57 
 58         return grads
 59 
 60     def gradient(self, x, t):
 61         # forward
 62         dout=self.loss(x, t)
 63 
 64         # backward
 65         # dout = 1
 66         dout = self.lastLayer.backward(dout)
 67 
 68         layers = list(self.layers.values())
 69         layers.reverse()
 70         for layer in layers:
 71             dout = layer.backward(dout)
 72 
 73         # 更新
 74         grads = {}
 75         grads['W1'], grads['b1'] = self.layers['Affine1'].dW, self.layers['Affine1'].db
 76         grads['W2'], grads['b2'] = self.layers['Affine2'].dW, self.layers['Affine2'].db
 77 
 78         return grads

 

5.7 使用mnist数据集测试

本节的测试是为了测试简单层的书写是否符合逻辑。本次依旧是使用mnist数据集训练识别手写体。与第二篇所不同的是：本次使用的是基于计算图的逻辑实现。

 

  1 # coding: utf-8
  2 import sys, os
  3 sys.path.append(os.pardir)
  4 
  5 import numpy as np
  6 from src.datasets.mnist import load_mnist
  7 from src.test.two_layer_net_v2 import TwoLayerNet
  8 
  9 
 10 (x_train, t_train), (x_test, t_test) = load_mnist(normalize=True, one_hot_label=True)
 11 
 12 network = TwoLayerNet(input_size=784, hidden_size=50, output_size=10)
 13 
 14 iters_num = 10000
 15 train_size = x_train.shape[0]
 16 batch_size = 100
 17 learning_rate = 0.1
 18 
 19 train_loss_list = []
 20 train_acc_list = []
 21 test_acc_list = []
 22 
 23 iter_per_epoch = max(train_size / batch_size, 1)
 24 
 25 for i in range(iters_num):
 26     batch_mask = np.random.choice(train_size, batch_size)
 27     x_batch = x_train[batch_mask]
 28     t_batch = t_train[batch_mask]
 29 
 30     grad = network.gradient(x_batch, t_batch)
 31 
 32     # 更新
 33     for key in ('W1', 'b1', 'W2', 'b2'):
 34         network.params[key] -= learning_rate * grad[key]
 35 
 36     loss = network.loss(x_batch, t_batch)
 37     train_loss_list.append(loss)
 38 
 39     if i % iter_per_epoch == 0:
 40         train_acc = network.accuracy(x_train, t_train)
 41         test_acc = network.accuracy(x_test, t_test)
 42         train_acc_list.append(train_acc)
 43         test_acc_list.append(test_acc)
 44         print(train_acc, test_acc)

 

 

训练过程中的输出

train loss:0.933130708198716

train loss:0.8530083545338488

train loss:0.8060872314284586

=============== Final Test Accuracy ===============

test acc:0.9939

Saved Network Parameters!

posted @ 2022-09-12 18:27  修雨轩陈  阅读(149)  评论(0)  编辑  收藏  举报