04第一次训练与手写数字

机器学习/04第一次训练与手写数字/推理.py

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+from torchvision import transforms
+from torch import nn
+import torch
+from PIL import Image
+
+
+class MnistModel(nn.Module):
+    def __init__(self):
+        super(MnistModel, self).__init__()
+        self.fc1 = nn.Linear(1 * 28 * 28, 100)  # 最终为什么是 10，因为手写数字识别最终是 10分类的，分类任务中有多少，就分几类。 0-9
+        self.relu = nn.ReLU()
+        self.fc2 = nn.Linear(100, 10)
+
+    def forward(self, image):
+        image_viwed = image.view(-1, 1 * 28 * 28)  # 此处需要拍平
+        out_1 = self.fc1(image_viwed)
+        fc1 = self.relu(out_1)
+        out_2 = self.fc2(fc1)
+        return out_2
+
+
+model = MnistModel()
+model.load_state_dict(torch.load("./models/model.pkl"))
+
+my_transforms = transforms.Compose(
+    [
+        transforms.ToTensor(),
+        transforms.Normalize(mean=(0.1307,), std=(0.3081,))
+    ]
+)
+
+image = Image.open('./img/test.jpg')
+
+my_transforms = transforms.Compose(
+    [
+        transforms.Grayscale(1),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=(0.1307,), std=(0.3081,))
+    ]
+)
+image = my_transforms(image)
+
+model.eval()
+with torch.no_grad():
+    output = model(image)
+    result = output.max(dim=1).indices
+    print(result)

机器学习/04第一次训练与手写数字/测试.py

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+from torchvision.datasets import MNIST
+from torchvision import transforms
+from torch.utils.data import DataLoader
+from torch import nn
+from tqdm import tqdm
+import torch
+import numpy as np
+import os
+
+
+class MnistModel(nn.Module):
+    def __init__(self):
+        super(MnistModel, self).__init__()
+        self.fc1 = nn.Linear(1 * 28 * 28, 100)  # 最终为什么是 10，因为手写数字识别最终是 10分类的，分类任务中有多少，就分几类。 0-9
+        self.relu = nn.ReLU()
+        self.fc2 = nn.Linear(100, 10)
+
+    def forward(self, image):
+        image_viwed = image.view(-1, 1 * 28 * 28)  # 此处需要拍平
+        out_1 = self.fc1(image_viwed)
+        fc1 = self.relu(out_1)
+        out_2 = self.fc2(fc1)
+        return out_2
+
+
+def test_success():
+    # 实例化模型
+    total_loss = []
+    model = MnistModel()
+    if os.path.exists("./models/model.pkl"):
+        model.load_state_dict(torch.load("./models/model.pkl"))
+    loss_function = nn.CrossEntropyLoss()
+    my_transforms = transforms.Compose(
+        [
+            transforms.ToTensor(),
+            transforms.Normalize(mean=(0.1307,), std=(0.3081,))
+        ]
+    )
+    mnist_train = MNIST(root="../MNIST_data", train=False, download=True, transform=my_transforms)
+    dataloader = DataLoader(mnist_train, batch_size=8, shuffle=True)
+    dataloader = tqdm(dataloader, total=len(dataloader))
+
+    succeed = []
+
+    model.eval()
+    with torch.no_grad():
+        for images, labels in dataloader:
+            # 获取结果
+            output = model(images)
+            result = output.max(dim=1).indices
+            # print(labels)
+            # print(result)
+            succeed.append(result.eq(labels).float().mean().item())
+            # 通过结果计算损失
+            loss = loss_function(output, labels)
+            total_loss.append(loss.item())
+    print(np.mean(total_loss))
+    return np.mean(succeed)

机器学习/04第一次训练与手写数字/训练.py

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+from torch import save, load
+from torchvision.datasets import MNIST
+from torchvision import transforms
+from torch.utils.data import DataLoader
+from torch import nn
+from torch import optim
+from tqdm import tqdm
+import 测试
+import os
+import numpy as np
+
+
+class MnistModel(nn.Module):
+    def __init__(self):
+        super(MnistModel, self).__init__()
+        self.fc1 = nn.Linear(1 * 28 * 28, 100)  # 最终为什么是 10，因为手写数字识别最终是 10分类的，分类任务中有多少，就分几类。 0-9
+        self.relu = nn.ReLU()
+        self.fc2 = nn.Linear(100, 10)
+
+    def forward(self, image):
+        image_viwed = image.view(-1, 1 * 28 * 28)  # 此处需要拍平
+        out_1 = self.fc1(image_viwed)
+        fc1 = self.relu(out_1)
+        out_2 = self.fc2(fc1)
+        return out_2
+
+
+# 实例化模型
+model = MnistModel()
+optimizer = optim.Adam(model.parameters())
+
+# 加载已经训练好的模型和优化器继续进行训练
+if os.path.exists('./models/model.pkl'):
+    model.load_state_dict(load("./models/model.pkl"))
+    optimizer.load_state_dict(load("./models/optimizer.pkl"))
+
+loss_function = nn.CrossEntropyLoss()
+my_transforms = transforms.Compose(
+    [
+        transforms.ToTensor(),
+        transforms.Normalize(mean=(0.1307,), std=(0.3081,))
+    ]
+)
+mnist_train = MNIST(root="../MNIST_data", train=True, download=True, transform=my_transforms)
+
+
+def train(epoch):
+    total_loss = []
+    dataloader = DataLoader(mnist_train, batch_size=8, shuffle=True)
+    dataloader = tqdm(dataloader, total=len(dataloader))
+    model.train()
+    for images, labels in dataloader:
+        # 梯度置0
+        optimizer.zero_grad()
+        # 前向传播
+        output = model(images)
+        # 通过结果计算损失
+        loss = loss_function(output, labels)
+        total_loss.append(loss.item())
+        # 反向传播
+        loss.backward()
+        # 优化器更新
+        optimizer.step()
+
+    save(model.state_dict(), './models/model.pkl')
+    save(optimizer.state_dict(), './models/optimizer.pkl')
+    # 打印一下训练成功率
+    print('第{}个epoch，成功率, 损失为{}'.format(epoch, np.mean(total_loss)), 测试.test_success())
+
+
+for i in range(10):
+    train(i)

机器学习/04第一次训练与手写数字/进度条.py

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+import time
+from tqdm import tqdm
+
+
+c = tqdm(range(0, 1000), total=1000)
+for i in c:
+    time.sleep(0.1)

机器学习/README.md

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+pytorch文档
+
+https://pytorch.org/docs/stable/index.html