基于pytorch全连接神经网络手写体数据识别，准确率达到百分之97

最新推荐文章于 2025-07-17 16:25:53 发布

原创

最新推荐文章于 2025-07-17 16:25:53 发布 · 1k 阅读

1 ·

CC 4.0 BY-SA版权

文章标签：

#手写体 #神经网络

import torch
from torch import nn
import torch.optim as optimizer
from torch.autograd import Variable

import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn import datasets
import numpy as np

class simpleNet(nn.Module):
    def __init__(self, in_dim, n_hidden_1, n_hidden_2, out_dim):
        super(simpleNet, self).__init__()
        self.layer1 = nn.Sequential(nn.Linear(in_dim, n_hidden_1), nn.BatchNorm1d(n_hidden_1), nn.ReLU(True))
        self.layer2 = nn.Sequential(nn.Linear(n_hidden_1, n_hidden_2), nn.BatchNorm1d(n_hidden_2), nn.ReLU(True))
        self.layer3 = nn.Sequential(nn.Linear(n_hidden_2, out_dim))
    def forward(self, x):
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        return x



digits = datasets.load_digits()
plt.gray()
plt.matshow(digits.images[0])
plt.show()

print(digits.data.shape)
print(digits.target.shape)
X_train, X_test, y_train, y_test = train_test_split(digits.data, di