手写数字集keras和pytorch的代码示例-巴涅波赫夫blog

Keras

from tensorflow.keras import layers
from tensorflow.keras import models
from tensorflow.keras.datasets import mnist
from tensorflow.keras.utils import to_categorical

# 创建卷积神经网络
model = models.Sequential()
model.add(layers.Conv2D(32, (3,3), activation='relu', input_shape=(28, 28, 1)))
model.add(layers.MaxPooling2D((2,2)))
model.add(layers.Conv2D(64, (3,3), activation='relu'))
model.add(layers.MaxPooling2D((2,2)))
model.add(layers.Conv2D(64, (3,3), activation='relu'))
model.summary()# 查看模型架构

# 在卷积神经网络上添加分类器
model.add(layers.Flatten())# 3D张量展平为1D
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10, activation='softmax'))
model.summary()

# 训练手写数字集
(xtrain,ytrain),(xtest,ytest) = mnist.load_data()

Xtrain = xtrain.reshape((60000,28,28,1))
Xtrain = Xtrain.astype('float32')/255

Xtest = xtest.reshape((10000,28,28,1))
Xtest = Xtest.astype('float32')/255

Ytrain = to_categorical(ytrain)
Ytest = to_categorical(ytest)

model.compile(optimizer='rmsprop',
              loss='categorical_crossentropy',
              metrics=&#91;'accuracy'])

model.fit(Xtrain,Ytrain,epochs=15,batch_size=64)

test_loss,test_acc = model.evaluate(Xtest,Ytest)

from tensorflow.keras import layers

from tensorflow.keras import models

from tensorflow.keras.datasets import mnist

from tensorflow.keras.utils import to_categorical

# 创建卷积神经网络

model = models.Sequential()

model.add(layers.Conv2D(32, (3,3), activation='relu', input_shape=(28, 28, 1)))

model.add(layers.MaxPooling2D((2,2)))

model.add(layers.Conv2D(64, (3,3), activation='relu'))

model.add(layers.MaxPooling2D((2,2)))

model.add(layers.Conv2D(64, (3,3), activation='relu'))

model.summary()# 查看模型架构

# 在卷积神经网络上添加分类器

model.add(layers.Flatten())# 3D张量展平为1D

model.add(layers.Dense(64, activation='relu'))

model.add(layers.Dense(10, activation='softmax'))

model.summary()

# 训练手写数字集

(xtrain,ytrain),(xtest,ytest) = mnist.load_data()

Xtrain = xtrain.reshape((60000,28,28,1))

Xtrain = Xtrain.astype('float32')/255

Xtest = xtest.reshape((10000,28,28,1))

Xtest = Xtest.astype('float32')/255

Ytrain = to_categorical(ytrain)

Ytest = to_categorical(ytest)

model.compile(optimizer='rmsprop',

loss='categorical_crossentropy',

metrics=['accuracy'])

model.fit(Xtrain,Ytrain,epochs=15,batch_size=64)

test_loss,test_acc = model.evaluate(Xtest,Ytest)

Pytorch

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms

BATCH_SIZE = 512 # 大概需要2G的显存
EPOCHS = 20 # 总共训练批次
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu") 

# 下载训练集
train_loader = torch.utils.data.DataLoader(
    datasets.MNIST('data', train = True, download = True,
              transform = transforms.Compose(&#91;
                  transforms.ToTensor(),
                  transforms.Normalize((0.1037,), (0.3081,))
              ])),
batch_size = BATCH_SIZE, shuffle = True)

# 测试集
test_loader = torch.utils.data.DataLoader(
datasets.MNIST('data', train = False, transform = transforms.Compose(&#91;
    transforms.ToTensor(),
    transforms.Normalize((0.1037,), (0.3081,))
])),
batch_size = BATCH_SIZE, shuffle = True)

# 定义模型
class ConvNet(nn.Module):
    def __init__(self):
        super().__init__()
        #1*1*28*28
        self.conv1 = nn.Conv2d(1, 10, 5) 
        self.conv2 = nn.Conv2d(10, 20, 3) 
        self.fc1 = nn.Linear(20 * 10 * 10, 500)
        self.fc2 = nn.Linear(500, 10)
        
    def forward(self, x):
        in_size = x.size(0)
        out= self.conv1(x) # 1* 10 * 24 *24
        out = F.relu(out)
        out = F.max_pool2d(out, 2, 2) # 1* 10 * 12 * 12
        out = self.conv2(out) # 1* 20 * 10 * 10
        out = F.relu(out)
        out = out.view(in_size, -1) # 1 * 2000
        out = self.fc1(out) # 1 * 500
        out = F.relu(out)
        out = self.fc2(out) # 1 * 10
        out = F.log_softmax(out, dim = 1)
        return out

# 生成模型和优化器
model = ConvNet().to(DEVICE)
optimizer = optim.Adam(model.parameters())

# 定义训练函数
def train(model, device, train_loader, optimizer, epoch):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = F.nll_loss(output, target)
        loss.backward()
        optimizer.step()
        if (batch_idx + 1) % 30 == 0:
            print('Train Epoch: {} &#91;{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                100. * batch_idx / len(train_loader), loss.item()))

# 定义测试函数
def test(model, device, test_loader):
    model.eval()
    test_loss =0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            output = model(data)
            test_loss += F.nll_loss(output, target, reduction = 'sum') # 将一批的损失相加
            pred = output.max(1, keepdim = True)&#91;1] # 找到概率最大的下标
            correct += pred.eq(target.view_as(pred)).sum().item()
    
    test_loss /= len(test_loader.dataset)
    print("\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%) \n".format(
        test_loss, correct, len(test_loader.dataset),
        100.* correct / len(test_loader.dataset)
            ))

# 最后开始训练和测试
for epoch in range(1, EPOCHS + 1):
    train(model,  DEVICE, train_loader, optimizer, epoch)
    test(model, DEVICE, test_loader)

import torch

import torch.nn as nn

import torch.nn.functional as F

import torch.optim as optim

from torchvision import datasets, transforms

BATCH_SIZE = 512 # 大概需要2G的显存

EPOCHS = 20 # 总共训练批次

DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# 下载训练集

train_loader = torch.utils.data.DataLoader(

datasets.MNIST('data', train = True, download = True,

transform = transforms.Compose([

transforms.ToTensor(),

transforms.Normalize((0.1037,), (0.3081,))

])),

batch_size = BATCH_SIZE, shuffle = True)

# 测试集

test_loader = torch.utils.data.DataLoader(

datasets.MNIST('data', train = False, transform = transforms.Compose([

transforms.ToTensor(),

transforms.Normalize((0.1037,), (0.3081,))

])),

batch_size = BATCH_SIZE, shuffle = True)

# 定义模型

class ConvNet(nn.Module):

def __init__(self):

super().__init__()

#1*1*28*28

self.conv1 = nn.Conv2d(1, 10, 5)

self.conv2 = nn.Conv2d(10, 20, 3)

self.fc1 = nn.Linear(20 * 10 * 10, 500)

self.fc2 = nn.Linear(500, 10)

def forward(self, x):

in_size = x.size(0)

out= self.conv1(x) # 1* 10 * 24 *24

out = F.relu(out)

out = F.max_pool2d(out, 2, 2) # 1* 10 * 12 * 12

out = self.conv2(out) # 1* 20 * 10 * 10

out = F.relu(out)

out = out.view(in_size, -1) # 1 * 2000

out = self.fc1(out) # 1 * 500

out = F.relu(out)

out = self.fc2(out) # 1 * 10

out = F.log_softmax(out, dim = 1)

return out

# 生成模型和优化器

model = ConvNet().to(DEVICE)

optimizer = optim.Adam(model.parameters())

# 定义训练函数

def train(model, device, train_loader, optimizer, epoch):

model.train()

for batch_idx, (data, target) in enumerate(train_loader):

data, target = data.to(device), target.to(device)

optimizer.zero_grad()

output = model(data)

loss = F.nll_loss(output, target)

loss.backward()

optimizer.step()

if (batch_idx + 1) % 30 == 0:

print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(

epoch, batch_idx * len(data), len(train_loader.dataset),

100. * batch_idx / len(train_loader), loss.item()))

# 定义测试函数

def test(model, device, test_loader):

model.eval()

test_loss =0

correct = 0

with torch.no_grad():

for data, target in test_loader:

data, target = data.to(device), target.to(device)

output = model(data)

test_loss += F.nll_loss(output, target, reduction = 'sum') # 将一批的损失相加

pred = output.max(1, keepdim = True)[1] # 找到概率最大的下标

correct += pred.eq(target.view_as(pred)).sum().item()

test_loss /= len(test_loader.dataset)

print("\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%) \n".format(

test_loss, correct, len(test_loader.dataset),

100.* correct / len(test_loader.dataset)

))

# 最后开始训练和测试

for epoch in range(1, EPOCHS + 1):

train(model, DEVICE, train_loader, optimizer, epoch)

test(model, DEVICE, test_loader)