diff --git "a/\346\242\201\345\251\247/Week 2/Week2CrossEntropy.py" "b/\346\242\201\345\251\247/Week 2/Week2CrossEntropy.py"
new file mode 100644
index 00000000..c34a6c97
--- /dev/null
+++ "b/\346\242\201\345\251\247/Week 2/Week2CrossEntropy.py"	
@@ -0,0 +1,141 @@
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import numpy as np
+import torch.nn.functional as F
+
+'''
+随机将1-9中的某一位替换为0，预测0的位置，按照多分类项目处理，分为9类
+'''
+
+class TorchModel(nn.Module):
+    def __init__(self, input_size):
+        super(TorchModel, self).__init__()
+        self.linear = nn.Linear(input_size, 9)
+        self.loss_fn = nn.CrossEntropyLoss()
+
+    def forward(self, x):
+        logits = self.linear(x)
+        return logits
+
+
+def build_sample():
+    vector = np.arange(1, 10)
+    random_index = np.random.randint(0, len(vector))
+    vector[random_index] = 0
+    return vector, random_index
+
+
+def build_dataset(total_sample_num):
+    X = []
+    Y = []
+    for i in range(total_sample_num):
+        x, y = build_sample()
+        X.append(x)
+        Y.append(y)
+    return torch.FloatTensor(X), torch.LongTensor(Y)
+
+# 使用训练好的模型做预测
+def predict(model_path, test_vecs):
+    # 加载模型
+    model = TorchModel(input_size=9)
+    model.load_state_dict(torch.load(model_path)) # 加载训练好的权重
+    print(model.state_dict())
+    model.eval()  # 测试模式
+
+    predictions = []
+
+    # 逐个处理向量
+    with torch.no_grad():  # 不计算梯度
+        for vec in test_vecs:
+            vec_tensor = torch.FloatTensor(vec).unsqueeze(0)  # 添加一个批次维度
+            logits = model(vec_tensor)
+            _, predicted_class = torch.max(logits, 1)
+            predictions.append(predicted_class.item())
+
+    return predictions
+
+# 测试代码
+# 用来测试每轮模型的准确率
+def evaluate(model):
+    model.eval()
+    test_sample_num = 100
+    x, y = build_dataset(test_sample_num)
+
+    correct = 0
+    total = y.size(0)  # y是一个一维张量，包含所有样本的真实标签
+    with torch.no_grad():
+        y_pred = model(x)  # 模型预测，输出是logits
+        # 将logits转换为概率分布
+        probs = F.softmax(y_pred, dim=1)
+        # 获取最大概率的索引作为预测类别
+        _, predicted = torch.max(probs, 1)
+        # 比较预测类别和真实标签
+        correct = (predicted == y).sum().item()
+
+    accuracy = 100 * correct / total
+    print("正确预测个数：%d, 总预测数：%d, 正确率：%.2f%%" % (correct, total, accuracy))
+    return accuracy
+
+
+def main():
+    # 配置参数
+    epochs_num = 20
+    batch_size = 20  # 每次训练样本个数
+    train_sample_num = 5000  # 训练样本总数
+    input_size = 9  # 输入向量维度
+    learning_rate = 0.001  # 学习率
+
+    # 建立模型
+    model = TorchModel(input_size)
+
+    # 选择优化器
+    optimizer = optim.Adam(model.parameters(), lr=learning_rate)
+
+    # 创建训练集
+    train_x, train_y = build_dataset(train_sample_num)
+
+    # 训练过程
+    for epoch in range(epochs_num):
+        model.train()
+        # 批次循环
+        for batch_index in range(train_sample_num // batch_size):
+            # 数据加载
+            x = train_x[batch_index * batch_size : (batch_index + 1) * batch_index]
+            y = train_y[batch_index * batch_size : (batch_index + 1) * batch_index]
+            logits = model(x)
+            loss = model.loss_fn(logits, y)
+            loss.backward()  # 梯度
+            optimizer.step()  # 更新权重
+            optimizer.zero_grad()  # 权重归零
+        print(f'Epoch [{epoch + 1}/{epochs_num}], Loss: {loss.item():.4f}')
+        acc = evaluate(model)  # 测试本轮模型结果
+        print(acc)
+
+
+    #保存模型
+    torch.save(model.state_dict(), "model.pt")
+
+# 生成测试数据
+def generate_prediction_data(num_samples):
+    prediction_data = []
+
+    # 为每个样本生成一个向量
+    for i in range(num_samples):
+        vector = np.arange(1, 10)
+        random_index = np.random.randint(0, len(vector))
+        vector[random_index] = 0  # 将随机位置的元素设置为0
+        prediction_data.append(vector)
+
+    return prediction_data
+
+
+if __name__ == "__main__":
+    main()
+    # 生成5个预测数据
+    prediction_vecs = generate_prediction_data(5)
+    print(prediction_vecs)
+    print('\n---------------------------\n')
+    # 调用predict_multiple函数并打印预测结果
+    predictions = predict("model.pt", prediction_vecs)
+    print(predictions)
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