PyTorch - 从scratch开始​​训练Convent

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  简述

  在本章中，我们将专注于从头开始创建 Convent 。这推断使用torch创建各自的Convent或样本神经网络。
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  第1步

  使用相应的参数创建一个必要的类。参数包括具有随机值的权重。

  
  class Neural_Network(nn.Module):
     def __init__(self, ):
        super(Neural_Network, self).__init__()
        self.inputSize = 2
        self.outputSize = 1
        self.hiddenSize = 3
        # weights
        self.W1 = torch.randn(self.inputSize, 
        self.hiddenSize) # 3 X 2 tensor
        self.W2 = torch.randn(self.hiddenSize, self.outputSize) # 3 X 1 tensor
  

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  第2步

  使用 sigmoid 函数创建函数的前馈模式。

  
  def forward(self, X):
     self.z = torch.matmul(X, self.W1) # 3 X 3 ".dot" 
     does not broadcast in PyTorch
     self.z2 = self.sigmoid(self.z) # activation function
     self.z3 = torch.matmul(self.z2, self.W2)
     o = self.sigmoid(self.z3) # final activation 
     function
     return o
     def sigmoid(self, s):
        return 1 / (1 + torch.exp(-s))
     def sigmoidPrime(self, s):
        # derivative of sigmoid
        return s * (1 - s)
     def backward(self, X, y, o):
        self.o_error = y - o # error in output
        self.o_delta = self.o_error * self.sigmoidPrime(o) # derivative of sig to error
        self.z2_error = torch.matmul(self.o_delta, torch.t(self.W2))
        self.z2_delta = self.z2_error * self.sigmoidPrime(self.z2)
        self.W1 + = torch.matmul(torch.t(X), self.z2_delta)
        self.W2 + = torch.matmul(torch.t(self.z2), self.o_delta)
  

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  第 3 步

  创建如下所述的训练和预测模型 -

  
  def train(self, X, y):
     # forward + backward pass for training
     o = self.forward(X)
     self.backward(X, y, o)
  def saveWeights(self, model):
     # Implement PyTorch internal storage functions
     torch.save(model, "NN")
     # you can reload model with all the weights and so forth with:
     # torch.load("NN")
  def predict(self):
     print ("Predicted data based on trained weights: ")
     print ("Input (scaled): \n" + str(xPredicted))
     print ("Output: \n" + str(self.forward(xPredicted)))
  

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