PyTorch - 实现第一个神经网络

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  简述

  PyTorch 包括创建和实现神经网络的特殊功能。在本章中，我们将创建一个简单的神经网络，其中一个隐藏层用于开发单个输出单元。

  我们将使用以下步骤使用 PyTorch 实现第一个神经网络 -
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  第1步

  首先，我们需要使用以下命令导入 PyTorch 库 -

  
  import torch 
  import torch.nn as nn
  

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  第2步

  定义所有层和批量大小以开始执行神经网络，如下所示 -

  
  # Defining input size, hidden layer size, output size and batch size respectively
  n_in, n_h, n_out, batch_size = 10, 5, 1, 10
  

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  第 3 步

  由于神经网络包括输入数据的组合以获得相应的输出数据，我们将遵循以下相同的程序 -

  
  # Create dummy input and target tensors (data)
  x = torch.randn(batch_size, n_in)
  y = torch.tensor([[1.0], [0.0], [0.0], 
  [1.0], [1.0], [1.0], [0.0], [0.0], [1.0], [1.0]])
  

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  第4步

  借助内置函数创建顺序模型。使用以下代码行，创建一个顺序模型 -

  
  # Create a model
  model = nn.Sequential(nn.Linear(n_in, n_h),
     nn.ReLU(),
     nn.Linear(n_h, n_out),
     nn.Sigmoid())
  

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  第 5 步

  借助梯度下降优化器构建损失函数，如下所示 -

  
  Construct the loss function
  criterion = torch.nn.MSELoss()
  # Construct the optimizer (Stochastic Gradient Descent in this case)
  optimizer = torch.optim.SGD(model.parameters(), lr = 0.01)
  

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  第 6 步

  使用给定代码行的迭代循环实现梯度下降模型 -

  
  # Gradient Descent
  for epoch in range(50):
     # Forward pass: Compute predicted y by passing x to the model
     y_pred = model(x)
     # Compute and print loss
     loss = criterion(y_pred, y)
     print('epoch: ', epoch,' loss: ', loss.item())
     # Zero gradients, perform a backward pass, and update the weights.
     optimizer.zero_grad()
     # perform a backward pass (backpropagation)
     loss.backward()
     # Update the parameters
     optimizer.step()
  

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  第 7 步

  生成的输出如下 -

  
  epoch: 0 loss: 0.2545787990093231
  epoch: 1 loss: 0.2545052170753479
  epoch: 2 loss: 0.254431813955307
  epoch: 3 loss: 0.25435858964920044
  epoch: 4 loss: 0.2542854845523834
  epoch: 5 loss: 0.25421255826950073
  epoch: 6 loss: 0.25413978099823
  epoch: 7 loss: 0.25406715273857117
  epoch: 8 loss: 0.2539947032928467
  epoch: 9 loss: 0.25392240285873413
  epoch: 10 loss: 0.25385022163391113
  epoch: 11 loss: 0.25377824902534485
  epoch: 12 loss: 0.2537063956260681
  epoch: 13 loss: 0.2536346912384033
  epoch: 14 loss: 0.25356316566467285
  epoch: 15 loss: 0.25349172949790955
  epoch: 16 loss: 0.25342053174972534
  epoch: 17 loss: 0.2533493936061859
  epoch: 18 loss: 0.2532784342765808
  epoch: 19 loss: 0.25320762395858765
  epoch: 20 loss: 0.2531369626522064
  epoch: 21 loss: 0.25306645035743713
  epoch: 22 loss: 0.252996027469635
  epoch: 23 loss: 0.2529257833957672
  epoch: 24 loss: 0.25285571813583374
  epoch: 25 loss: 0.25278574228286743
  epoch: 26 loss: 0.25271597504615784
  epoch: 27 loss: 0.25264623761177063
  epoch: 28 loss: 0.25257670879364014
  epoch: 29 loss: 0.2525072991847992
  epoch: 30 loss: 0.2524380087852478
  epoch: 31 loss: 0.2523689270019531
  epoch: 32 loss: 0.25229987502098083
  epoch: 33 loss: 0.25223103165626526
  epoch: 34 loss: 0.25216227769851685
  epoch: 35 loss: 0.252093642950058
  epoch: 36 loss: 0.25202515721321106
  epoch: 37 loss: 0.2519568204879761
  epoch: 38 loss: 0.251888632774353
  epoch: 39 loss: 0.25182053446769714
  epoch: 40 loss: 0.2517525553703308
  epoch: 41 loss: 0.2516847252845764
  epoch: 42 loss: 0.2516169846057892
  epoch: 43 loss: 0.2515493929386139
  epoch: 44 loss: 0.25148195028305054
  epoch: 45 loss: 0.25141456723213196
  epoch: 46 loss: 0.2513473629951477
  epoch: 47 loss: 0.2512802183628082
  epoch: 48 loss: 0.2512132525444031
  epoch: 49 loss: 0.2511464059352875