Create pytorch_example.py

2023.11.08_pytorch_example/pytorch_example.py

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+import torch
+import numpy as np
+
+# Torch数据类型【和NumPy相近，但不是同一个类型】
+
+scalar = torch.tensor(3.14) # 创建一个Torch标量
+print('\nTorch标量：\n', scalar)
+
+vector = torch.tensor([1, 2, 3]) # 创建一个Torch向量
+print('\n\nTorch向量：\n', vector)
+
+matrix = torch.tensor([[1, 2, 3], [4, 5, 6]]) # # 创建一个Torch矩阵
+print('\n\nTorch矩阵：\n', matrix)
+
+
+
+
+# Torch数据和NumPy数据的转换
+
+numpy_array = np.arange(6).reshape((2, 3))  # NumPy数组
+torch_tensor = torch.from_numpy(numpy_array)  # NumPy数组转Torch张量
+numpy_array_2 = torch_tensor.numpy()  # Torch张量转NumPy数组
+print('\n\n\n\nNumPy数组：\n', numpy_array)
+print('\n\nNumpy数组转Torch张量：\n', torch_tensor) 
+print('\n\nTorch张量转NumPy数组：\n', numpy_array_2)
+
+
+
+
+# torch.from_numpy() 和 torch.tensor() 的关系【前者共享内存，后者不共享内存】
+print('\n\n\n\ntorch.from_numpy() 和 torch.tensor() 的关系：')
+
+np_array = np.array([1, 2, 3])
+torch_tensor = torch.from_numpy(np_array)  # 共享内存
+torch_tensor[0] = 100  # 修改Torch张量
+print(np_array)  # NumPy数组也会被修改
+np_array[1] = 200  # 修改NumPy数组
+print(torch_tensor)  # Torch张量也会被修改
+
+print()
+np_array_2 = np.array([1, 2, 3])
+torch_tensor_2 = torch.tensor(np_array_2)  # 不共享内存
+torch_tensor_2[0] = 100  # 修改Torch张量
+print(np_array_2)  # NumPy数组不会被修改
+np_array_2[1] = 200   # 修改NumPy数组
+print(torch_tensor_2)  # Torch张量不会被修改
+
+
+
+
+# torch_tensor.numpy() 和 np.array() 的关系【前者共享内存，后者不共享内存】
+print('\n\n\n\ntorch_tensor.numpy() 和 np.array() 的关系：')
+
+torch_tensor = torch.tensor([1, 2, 3])
+numpy_array = torch_tensor.numpy()  # 共享内存
+numpy_array[0] = 100  # 修改NumPy数组
+print(torch_tensor)  # Torch张量会被修改
+torch_tensor[1] = 200  # 修改Torch张量
+print(numpy_array)   # NumPy数组也会被修改
+
+print()
+torch_tensor_2 = torch.tensor([1, 2, 3])
+numpy_array_2 = np.array(torch_tensor_2)  # 不共享内存
+numpy_array_2[0] = 100  # 修改NumPy数组
+print(torch_tensor_2)  # Torch张量不会被修改
+torch_tensor_2[1] = 200 # 修改Torch张量
+print(numpy_array_2)   # NumPy数组不会被修改
+
+
+
+
+# 绝对值操作
+
+print('\n\n\n\nNumPy绝对值和Torch绝对值：')
+numpy_array = [-1, -2, 1, 2]
+torch_tensor = torch.tensor(numpy_array)
+float_tensor = torch.FloatTensor(numpy_array)  # Torch的32位浮点张量
+print(np.abs(numpy_array))
+print(np.abs(torch_tensor))  # 不报错。numpy.abs()也可以处理tensor数据，仍然是返回Torch张量
+print(np.abs(float_tensor))  # 不报错。numpy.abs()也可以处理tensor数据，仍然是返回Torch张量
+print()
+# print(torch.abs(numpy_array))  # 报错。torch.abs()只能处理tensor数据
+print(torch.abs(torch_tensor))
+print(torch.abs(float_tensor))
+
+
+
+
+
+# 矩阵乘积操作
+
+A = torch.tensor([[1, 2], [3, 4]])
+B = torch.tensor([[5, 6], [7, 8]])
+result_mm = torch.mm(A, B) # 使用torch.mm执行矩阵乘积
+result_matmul = torch.matmul(A, B) # 使用torch.matmul执行矩阵乘积
+print("\n\n\n\n使用torch.mm()：\n", result_mm)
+print("\n使用torch.matmul()：\n", result_matmul)
+
+result_np_dot = np.dot(A, B) # np.dot()也可以处理tensor数据，但这里返回的是NumPy数组格式
+print('\n', type(result_np_dot))
+print(result_np_dot)
+
+# A_array = np.array([[1, 2], [3, 4]])
+# B_array = np.array([[5, 6], [7, 8]])
+# result_mm = torch.mm(A_array, B_array) # 报错。torch.mm只能处理tensor数据
+# result_matmul = torch.matmul(A_array, B_array) #报错。torch.matmul只能处理tensor数据