• 造车先做三蹦子-之二:自制数据集(5x5数据集)230102

    1. #自制数据集:5*5的点阵输入，数字的输出9个OneHot输出……Jupyter Notebook231001
    2.  
    3. import torch   #pytorch
    4. import torch.nn as nn
    5. import torch.optim as optim   #优化器optimizer
    6.  
    7. # 定义模型
    8. class Net(nn.Module):
    9.     def __init__(self):
    10.         super(Net, self).__init__()
    11.         self.fc1 = nn.Linear(25, 50)
    12.         self.fc2 = nn.Linear(50, 9)
    13.     
    14.     def forward(self, x):
    15.         x = x.view(-1, 25)
    16.         x = torch.relu(self.fc1(x))
    17.         x = self.fc2(x)
    18.         return x
    19.  
    20. # 训练数据
    21.  
    22.  
    23. i = [
    24. #0
    25.     torch.tensor([[1,1,1,1,1],
    26.                    [1,0,0,0,1],
    27.                    [1,0,0,0,1],
    28.                    [1,0,0,0,1],
    29.                    [1,1,1,1,1]], dtype=torch.float32),
    30. #1
    31.     torch.tensor([[0,0,1,0,0],
    32.                    [0,0,1,0,0],
    33.                    [0,0,1,0,0],
    34.                    [0,0,1,0,0],
    35.                    [0,0,1,0,0]], dtype=torch.float32),
    36. #2
    37.      torch.tensor([[0,0,0,0,0],
    38.                    [0,0,0,0,0],
    39.                    [1,1,1,1,1],
    40.                    [0,0,0,0,0],
    41.                    [0,0,0,0,0]], dtype=torch.float32),
    42.      
    43.      torch.tensor([[0,0,1,0,0],
    44.                    [0,0,1,0,0],
    45.                    [1,1,1,1,1],
    46.                    [0,0,1,0,0],
    47.                    [0,0,1,0,0]], dtype=torch.float32),
    48.      torch.tensor([[1,0,0,0,1],
    49.                    [0,1,0,1,0],
    50.                    [0,0,1,0,0],
    51.                    [0,1,0,1,0],
    52.                    [1,0,0,0,1]], dtype=torch.float32),
    53.      torch.tensor([[1,1,1,1,1],
    54.                    [1,0,0,0,0],
    55.                    [1,1,1,1,1],
    56.                    [0,0,0,0,1],
    57.                    [1,1,1,1,1]], dtype=torch.float32),
    58.      torch.tensor([[1,1,0,0,0],
    59.                    [1,0,0,0,0],
    60.                    [1,1,1,1,0],
    61.                    [1,0,0,0,1],
    62.                    [1,1,1,1,1]], dtype=torch.float32),
    63. #7
    64.       torch.tensor([[0,1,1,1,1],
    65.                    [0,0,0,1,1],
    66.                    [0,0,0,0,1],
    67.                    [0,0,0,0,1],
    68.                    [0,0,0,1,1]], dtype=torch.float32),
    69. #数字8
    70.       torch.tensor([[1,1,1,1,1],
    71.                    [1,0,0,0,1],
    72.                    [1,1,1,1,1],
    73.                    [1,0,0,0,1],
    74.                    [1,1,1,1,1]], dtype=torch.float32),
    75.  
    76.      # ...其他数据
    77.  
    78.      ]
    79.  
    80. target = [torch.tensor([1,0,0,0,0,0,0,0,0]),    #0
    81.           torch.tensor([0,1,0,0,0,0,0,0,0]),   #1
    82.           torch.tensor([0,0,1,0,0,0,0,0,0]),   #2
    83.           torch.tensor([0,0,0,1,0,0,0,0,0]),   #3
    84.           torch.tensor([0,0,0,0,1,0,0,0,0]),   #4
    85.           torch.tensor([0,0,0,0,0,1,0,0,0]),
    86.           torch.tensor([0,0,0,0,0,0,1,0,0]),   #6
    87.           torch.tensor([0,0,0,0,0,0,0,1,0]),   #7
    88.           torch.tensor([0,0,0,0,0,0,0,0,1]),   #8
    89.           # ...其他目标
    90.          ]
    91.  
    92. # 测试数据
    93. ib = [
    94.  
    95. #预计数字:"0"
    96.     torch.tensor([[1,1,1,1,1],
    97.                    [1,0,0,0,1],
    98.                    [1,0,0,0,1],
    99.                    [1,0,0,1,1],
    100.                    [1,1,1,1,2]], dtype=torch.float32),
    101.     #预计6
    102.     torch.tensor([[6,1,0,0,0],
    103.                    [6,0,0,0,0],
    104.                    [6,1,1,0,0],
    105.                    [6,0,0,1,0],
    106.                    [6,6,6,1,0]], dtype=torch.float32),
    107.  
    108.     torch.tensor([[0,0,0,0,0],
    109.                     [0,1,1,1,1],
    110.                     [1,1,1,1,1],
    111.                     [0,0,0,0,0],
    112.                     [0,0,0,0,0]], dtype=torch.float32),
    113.       
    114.       torch.tensor([[0,0,1,0,0],
    115.                     [0,0,1,0,0],
    116.                     [0,0,1,0,0],
    117.                     [0,0,1,1,0],
    118.                     [0,0,1,0,0]], dtype=torch.float32),
    119.       
    120.       torch.tensor([[0,0,1,0,0],
    121.                    [0,0,1,0,0],
    122.                    [1,1,1,1,1],
    123.                    [0,0,1,0,0],
    124.                    [0,0,1,0,0]], dtype=torch.float32),
    125.      torch.tensor([[1,0,0,0,1],
    126.                    [0,1,0,1,0],
    127.                    [0,0,1,0,0],
    128.                    [0,1,0,1,0],
    129.                    [1,0,0,0,1]], dtype=torch.float32),
    130.      torch.tensor([[1,1,1,1,1],
    131.                    [1,0,0,0,1],
    132.                    [1,0,0,0,1],
    133.                    [1,0,0,0.5,1],
    134.                    [1,1,1,1,2]], dtype=torch.float32),
    135.     #预计"7"
    136.      torch.tensor([[0,1,1,1,0],
    137.                    [0,0,0,1,0],
    138.                    [0,0,0.05,0,0],
    139.                    [0,0,0,1,0],
    140.                    [0,0,1,0,0]], dtype=torch.float32),
    141. #预计数字:"8"
    142.     torch.tensor([[1,1,1,1,1],
    143.                    [1,0,0,0,1],
    144.                    [1,1,1,1,1],
    145.                    [1,0,0.5,0.9, 1.2],
    146.                    [1,1,1,2,3]], dtype=torch.float32),
    147.       
    148.      ]
    149.  
    150. # 初始化网络、损失函数和优化器
    151. net = Net()
    152. criterion = nn.CrossEntropyLoss()
    153. optimizer = optim.SGD(net.parameters(), lr=0.01)
    154.  
    155. # 训练模型
    156. for epoch in range(100):
    157.     for inputs, labels in zip(i, target):
    158.         optimizer.zero_grad()
    159.         
    160.         inputs = inputs.unsqueeze(0) # 增加批次维度
    161.         outputs = net(inputs)
    162.         loss = criterion(outputs, torch.argmax(labels.unsqueeze(0), 1))
    163.         loss.backward()
    164.         optimizer.step()
    165.         
    166.     print(f"Epoch {epoch+1}, Loss: {loss.item()}")
    167.  
    168. print("Training complete!")
    169.  
    170. # 使用测试数据进行测试
    171. with torch.no_grad():
    172.     for test_input in ib:
    173.         test_input = test_input.unsqueeze(0) # 增加批次维度
    174.         outputs = net(test_input)
    175.         prediction = torch.argmax(outputs, 1)
    176.         print(f"Test input:\n{test_input.squeeze(0)}\nPredicted class: {prediction.item()}\n")
    177.  

    1. #Jupyter Notebook231001
    2.  
    3. import torch
    4. import torch.nn as nn
    5. import torch.optim as optim
    6.  
    7. # 定义模型
    8. class Net(nn.Module):
    9.     def __init__(self):
    10.         super(Net, self).__init__()
    11.         self.fc1 = nn.Linear(25, 50)
    12.         self.fc2 = nn.Linear(50, 6)
    13.     
    14.     def forward(self, x):
    15.         x = x.view(-1, 25)
    16.         x = torch.relu(self.fc1(x))
    17.         x = self.fc2(x)
    18.         return x
    19.  
    20. # 训练数据
    21. i = [torch.tensor([[0,0,1,0,0],
    22.                    [0,0,1,0,0],
    23.                    [0,0,1,0,0],
    24.                    [0,0,1,0,0],
    25.                    [0,0,1,0,0]], dtype=torch.float32),
    26.  
    27.      torch.tensor([[0,0,0,0,0],
    28.                    [0,0,0,0,0],
    29.                    [1,1,1,1,1],
    30.                    [0,0,0,0,0],
    31.                    [0,0,0,0,0]], dtype=torch.float32),
    32.      
    33.      torch.tensor([[0,0,1,0,0],
    34.                    [0,0,1,0,0],
    35.                    [1,1,1,1,1],
    36.                    [0,0,1,0,0],
    37.                    [0,0,1,0,0]], dtype=torch.float32),
    38.      torch.tensor([[1,0,0,0,1],
    39.                    [0,1,0,1,0],
    40.                    [0,0,1,0,0],
    41.                    [0,1,0,1,0],
    42.                    [1,0,0,0,1]], dtype=torch.float32),
    43.      torch.tensor([[1,1,1,1,1],
    44.                    [1,0,0,0,1],
    45.                    [1,0,0,0,1],
    46.                    [1,0,0,0,1],
    47.                    [1,1,1,1,1]], dtype=torch.float32),
    48.      torch.tensor([[0,0,0,0,0],
    49.                    [0,0,0,0,0],
    50.                    [0,0,0,0,0],
    51.                    [0,0,0,0,0],
    52.                    [0,0,0,0,0]], dtype=torch.float32),
    53.  
    54.      # ...其他数据
    55.  
    56.      ]
    57.  
    58. target = [torch.tensor([1,0,0,0,0,0]),
    59.           torch.tensor([0,1,0,0,0,0]),
    60.           torch.tensor([0,0,1,0,0,0]),
    61.           torch.tensor([0,0,0,1,0,0]),
    62.           torch.tensor([0,0,0,0,1,0]),
    63.           torch.tensor([0,0,0,0,0,1]),
    64.           # ...其他目标
    65.          ]
    66.  
    67. # 测试数据
    68. ib = [torch.tensor([[0,0,0,0,0],
    69.                     [0,1,1,1,1],
    70.                     [1,1,1,1,1],
    71.                     [0,0,0,0,0],
    72.                     [0,0,0,0,0]], dtype=torch.float32),
    73.       
    74.       torch.tensor([[0,0,1,0,0],
    75.                     [0,0,1,0,0],
    76.                     [0,0,1,0,0],
    77.                     [0,0,1,1,0],
    78.                     [0,0,1,0,0]], dtype=torch.float32),
    79.       
    80.       torch.tensor([[0,0,1,0,0],
    81.                    [0,0,1,0,0],
    82.                    [1,1,1,1,1],
    83.                    [0,0,1,0,0],
    84.                    [0,0,1,0,0]], dtype=torch.float32),
    85.      torch.tensor([[1,0,0,0,1],
    86.                    [0,1,0,1,0],
    87.                    [0,0,1,0,0],
    88.                    [0,1,0,1,0],
    89.                    [1,0,0,0,1]], dtype=torch.float32),
    90.      torch.tensor([[1,1,1,1,1],
    91.                    [1,0,0,0,1],
    92.                    [1,0,0,0,1],
    93.                    [1,0,0,0.5,1],
    94.                    [1,1,1,1,2]], dtype=torch.float32),
    95.      torch.tensor([[0,0,0,0,0],
    96.                    [0,0,0,0,0],
    97.                    [0,0,0.05,0,0],
    98.                    [0,0,0,0,0],
    99.                    [0,0,0,0,0]], dtype=torch.float32),
    100.      ]
    101.  
    102. # 初始化网络、损失函数和优化器
    103. net = Net()
    104. criterion = nn.CrossEntropyLoss()
    105. optimizer = optim.SGD(net.parameters(), lr=0.01)
    106.  
    107. # 训练模型
    108. for epoch in range(100):
    109.     for inputs, labels in zip(i, target):
    110.         optimizer.zero_grad()
    111.         
    112.         inputs = inputs.unsqueeze(0) # 增加批次维度
    113.         outputs = net(inputs)
    114.         loss = criterion(outputs, torch.argmax(labels.unsqueeze(0), 1))
    115.         loss.backward()
    116.         optimizer.step()
    117.         
    118.     print(f"Epoch {epoch+1}, Loss: {loss.item()}")
    119.  
    120. print("Training complete!")
    121.  
    122. # 使用测试数据进行测试
    123. with torch.no_grad():
    124.     for test_input in ib:
    125.         test_input = test_input.unsqueeze(0) # 增加批次维度
    126.         outputs = net(test_input)
    127.         prediction = torch.argmax(outputs, 1)
    128.         print(f"Test input:\n{test_input.squeeze(0)}\nPredicted class: {prediction.item()}\n")
    129.

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  • 原文地址：https://blog.csdn.net/aw344/article/details/134025828