修改一点小问题

.gitignore

@@ -1,3 +1,5 @@
 dataset/
 
 .vscode/
+
+.ipynb_checkpoints/

Lab1/Pytorch基本操作实验报告.ipynb

@@ -393,7 +393,7 @@
    "id": "6ab83528-a88b-4d66-b0c9-b1315cf75c22",
    "metadata": {},
    "source": [
-    "线性层主要有一个权重（weight)和一个偏置（bias）。\n",
+    "线性层主要有一个权重（weight）和一个偏置（bias）。\n",
     "线性层的数学公式如下：\n",
     "$$\n",
     "x:=x \\times weight^T+bias\n",

Lab1/code/.ipynb_checkpoints/1.1-checkpoint.py

@@ -1,39 +0,0 @@
-import torch
-
-A = torch.tensor([[1, 2, 3]])
-
-B = torch.tensor([[4],
-                  [5]])
-
-# 方法1: 使用PyTorch的减法操作符
-result1 = A - B
-
-# 方法2: 使用PyTorch的sub函数
-result2 = torch.sub(A, B)
-
-# 方法3: 手动实现广播机制并作差
-def my_sub(a:torch.Tensor, b:torch.Tensor):
-    if not (
-        (a.size(0) == 1 and b.size(1) == 1) 
-        or 
-        (a.size(1) == 1 and b.size(0) == 1)
-        ):
-        raise ValueError("输入的张量大小无法满足广播机制的条件。")
-    else:
-        target_shape = torch.Size([max(A.size(0), B.size(0)), max(A.size(1), B.size(1))])
-        A_broadcasted = A.expand(target_shape)
-        B_broadcasted = B.expand(target_shape)
-        result = torch.zeros(target_shape, dtype=torch.int64).to(device=A_broadcasted.device)
-        for i in range(target_shape[0]):
-            for j in range(target_shape[1]):
-                result[i, j] = A_broadcasted[i, j] - B_broadcasted[i, j]
-        return result
-
-result3 = my_sub(A, B)
-
-print("方法1的结果:")
-print(result1)
-print("方法2的结果:")
-print(result2)
-print("方法3的结果:")
-print(result3)