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nolog1n Apr 18th, 2019 71 Never
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  1.  def predict(self, inputs):
  2.         inputs_1 = tc.mm(inputs,self.weights_0_1) + self.bias1
  3.         outputs_1 = self.relu(inputs_1)
  4.  
  5.         inputs_2 = tc.mm(outputs_1, self.weights_1_2) + self.bias2
  6.         outputs_2 = inputs_2.softmax(1)
  7.  
  8.         return outputs_2
  9.  
  10.     def check_los(self,inputs,clas):
  11.         inputs_1 = tc.mm(inputs, self.weights_0_1) + self.bias1
  12.         outputs_1 = self.relu(inputs_1)
  13.  
  14.         inputs_2 = tc.mm(outputs_1, self.weights_1_2) + self.bias2
  15.         outputs_2 = inputs_2.softmax(1)
  16.  
  17.         L = -tc.mean(tc.log(outputs_2[tc.arange(len(outputs_2)), clas]))
  18.         self.test_costs.append(L)
  19.  
  20.         return outputs_2
  21.  
  22.     def train(self, inputs, clas):
  23.  
  24.         inputs_1 = tc.mm(inputs, self.weights_0_1) + self.bias1
  25.         outputs_1 = self.relu(inputs_1)
  26.  
  27.         inputs_2 = tc.mm(outputs_1, self.weights_1_2) + self.bias2
  28.         outputs_2 = inputs_2.softmax(1)
  29.  
  30.         with tc.no_grad():
  31.             if self.weights_0_1.grad is not None:
  32.                 self.weights_0_1.grad.zero_()
  33.  
  34.             if self.weights_1_2.grad is not None:
  35.                 self.weights_1_2.grad.zero_()
  36.  
  37.             if self.bias1.grad is not None:
  38.                 self.bias1.grad.zero_()
  39.  
  40.             if self.bias2.grad is not None:
  41.                 self.bias2.grad.zero_()
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