pytorch cnn 识别手写的字实现自建图片数据

yipeiwu_com5年前Python基础

本文主要介绍了pytorch cnn 识别手写的字实现自建图片数据,分享给大家,具体如下:

# library
# standard library
import os 
# third-party library
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.utils.data import Dataset, DataLoader
import torchvision
import matplotlib.pyplot as plt
from PIL import Image
import numpy as np
# torch.manual_seed(1)  # reproducible 
# Hyper Parameters
EPOCH = 1        # train the training data n times, to save time, we just train 1 epoch
BATCH_SIZE = 50
LR = 0.001       # learning rate 
 
root = "./mnist/raw/"
 
def default_loader(path):
  # return Image.open(path).convert('RGB')
  return Image.open(path)
 
class MyDataset(Dataset):
  def __init__(self, txt, transform=None, target_transform=None, loader=default_loader):
    fh = open(txt, 'r')
    imgs = []
    for line in fh:
      line = line.strip('\n')
      line = line.rstrip()
      words = line.split()
      imgs.append((words[0], int(words[1])))
    self.imgs = imgs
    self.transform = transform
    self.target_transform = target_transform
    self.loader = loader
    fh.close()
  def __getitem__(self, index):
    fn, label = self.imgs[index]
    img = self.loader(fn)
    img = Image.fromarray(np.array(img), mode='L')
    if self.transform is not None:
      img = self.transform(img)
    return img,label
  def __len__(self):
    return len(self.imgs)
 
train_data = MyDataset(txt= root + 'train.txt', transform = torchvision.transforms.ToTensor())
train_loader = DataLoader(dataset = train_data, batch_size=BATCH_SIZE, shuffle=True)
 
test_data = MyDataset(txt= root + 'test.txt', transform = torchvision.transforms.ToTensor())
test_loader = DataLoader(dataset = test_data, batch_size=BATCH_SIZE)
 
class CNN(nn.Module):
  def __init__(self):
    super(CNN, self).__init__()
    self.conv1 = nn.Sequential(     # input shape (1, 28, 28)
      nn.Conv2d(
        in_channels=1,       # input height
        out_channels=16,      # n_filters
        kernel_size=5,       # filter size
        stride=1,          # filter movement/step
        padding=2,         # if want same width and length of this image after con2d, padding=(kernel_size-1)/2 if stride=1
      ),               # output shape (16, 28, 28)
      nn.ReLU(),           # activation
      nn.MaxPool2d(kernel_size=2),  # choose max value in 2x2 area, output shape (16, 14, 14)
    )
    self.conv2 = nn.Sequential(     # input shape (16, 14, 14)
      nn.Conv2d(16, 32, 5, 1, 2),   # output shape (32, 14, 14)
      nn.ReLU(),           # activation
      nn.MaxPool2d(2),        # output shape (32, 7, 7)
    )
    self.out = nn.Linear(32 * 7 * 7, 10)  # fully connected layer, output 10 classes
 
  def forward(self, x):
    x = self.conv1(x)
    x = self.conv2(x)
    x = x.view(x.size(0), -1)      # flatten the output of conv2 to (batch_size, 32 * 7 * 7)
    output = self.out(x)
    return output, x  # return x for visualization 
cnn = CNN()
print(cnn) # net architecture
 
optimizer = torch.optim.Adam(cnn.parameters(), lr=LR)  # optimize all cnn parameters
loss_func = nn.CrossEntropyLoss()            # the target label is not one-hotted 
 
# training and testing
for epoch in range(EPOCH):
  for step, (x, y) in enumerate(train_loader):  # gives batch data, normalize x when iterate train_loader
    b_x = Variable(x)  # batch x
    b_y = Variable(y)  # batch y
 
    output = cnn(b_x)[0]        # cnn output
    loss = loss_func(output, b_y)  # cross entropy loss
    optimizer.zero_grad()      # clear gradients for this training step
    loss.backward()         # backpropagation, compute gradients
    optimizer.step()        # apply gradients
 
    if step % 50 == 0:
      cnn.eval()
      eval_loss = 0.
      eval_acc = 0.
      for i, (tx, ty) in enumerate(test_loader):
        t_x = Variable(tx)
        t_y = Variable(ty)
        output = cnn(t_x)[0]
        loss = loss_func(output, t_y)
        eval_loss += loss.data[0]
        pred = torch.max(output, 1)[1]
        num_correct = (pred == t_y).sum()
        eval_acc += float(num_correct.data[0])
      acc_rate = eval_acc / float(len(test_data))
      print('Test Loss: {:.6f}, Acc: {:.6f}'.format(eval_loss / (len(test_data)), acc_rate))

图片和label 见上一篇文章《pytorch 把MNIST数据集转换成图片和txt

结果如下:

以上就是本文的全部内容,希望对大家的学习有所帮助,也希望大家多多支持【听图阁-专注于Python设计】。

相关文章

Python使用cx_Oracle模块将oracle中数据导出到csv文件的方法

本文实例讲述了Python使用cx_Oracle模块将oracle中数据导出到csv文件的方法。分享给大家供大家参考。具体实现方法如下: # Export Oracle databa...

在Pandas中处理NaN值的方法

在Pandas中处理NaN值的方法

关于NaN值 -在能够使用大型数据集训练学习算法之前,我们通常需要先清理数据, 也就是说,我们需要通过某个方法检测并更正数据中的错误。 - 任何给定数据集可能会出现各种糟糕的数据,例如...

对python当中不在本路径的py文件的引用详解

众所周知,如果py文件不在当前路径,那么就不能import,因此,本文介绍如下两种有效的方法: 方法1: 修改环境变量,在~/.bashrc里面进行修改,然后source ~/.bash...

对Python 除法负数取商的取整方式详解

python除法负数商的取整方式与C++不同 python: 5 / -2 = -3 若想和C++行为相同,可以使用 int(operator.truediv(num1, num2...

python实现远程控制电脑

python远程控制电脑的具体代码,供大家参考,具体内容如下 python拥有大量的第三方库,且语法简单。今天老杨就用python实现远程控制电脑 所谓,谋定而后动,在实现任何一个需求之...