
'''simple docstring'''





import numpy as np
from matplotlib import pyplot as plt





def 							snake_case				(		UpperCAmelCase						)->      Dict:







      """simple docstring"""
      return 1 / (1 + np.exp(-1 * x						))


class 			UpperCamelCase__	:






 def __init__(      self	:Tuple						,					_A	:Optional[Any]						,					_A	:str=None						,					_A	:Tuple=None						,					_A	:List[Any]=False      )					->      Optional[Any]:







       '''simple docstring'''



       __A													=      units
       __A													=      None
       __A													=      None
       __A													=      activation
       if learning_rate is None:
             __A													=      0.3
       __A													=      learning_rate
       __A													=      is_input_layer






 def 						lowercase_    (      self	:Dict						,					_A	:Any      )					->      Tuple:







       '''simple docstring'''



       __A													=      np.asmatrix(np.random.normal(0						,					0.5						,					(self.units, back_units)      )      )
       __A													=      np.asmatrix(np.random.normal(0						,					0.5						,					self.units      )      ).T
       if self.activation is None:
             __A													=      sigmoid






 def 						lowercase_    (      self	:List[Any]      )					->      Tuple:







       '''simple docstring'''



       if self.activation == sigmoid:
             __A													=      np.dot(self.output						,					(1 - self.output).T      )
             __A													=      np.diag(np.diag(_A      )      )
       else:
             __A													=      1
       return gradient_activation






 def 						lowercase_    (      self	:int						,					_A	:Union[str, Any]      )					->      str:







       '''simple docstring'''



       __A													=      xdata
       if self.is_input_layer:
             # input layer
             __A													=      xdata
             __A													=      xdata
             return xdata
       else:
             __A													=      np.dot(self.weight						,					self.xdata      ) - self.bias
             __A													=      self.activation(self.wx_plus_b      )
             return self.output






 def 						lowercase_    (      self	:Tuple						,					_A	:Dict      )					->      Optional[Any]:







       '''simple docstring'''



       __A													=      self.cal_gradient()  # i * i 维
       __A													=      np.asmatrix(np.dot(gradient.T						,					_A      )      )

       __A													=      np.asmatrix(self.xdata      )
       __A													=      -1
       __A													=      self.weight

       __A													=      np.dot(gradient.T						,					self._gradient_weight.T      )
       __A													=      gradient * self._gradient_bias
       __A													=      np.dot(_A						,					self._gradient_x      ).T
       # upgrade: the Negative gradient direction
       __A													=      self.weight - self.learn_rate * self.gradient_weight
       __A													=      self.bias - self.learn_rate * self.gradient_bias.T
       # updates the weights and bias according to learning rate (0.3 if undefined)
       return self.gradient


class 			UpperCamelCase__	:






 def __init__(      self	:int      )					->      List[str]:







       '''simple docstring'''



       __A													=      []
       __A													=      []
       __A													=      plt.figure()
       __A													=      self.fig_loss.add_subplot(1						,					1						,					1      )






 def 						lowercase_    (      self	:Union[str, Any]						,					_A	:Tuple      )					->      Union[str, Any]:







       '''simple docstring'''



       self.layers.append(_A      )






 def 						lowercase_    (      self	:List[Any]      )					->      Any:







       '''simple docstring'''



       for i, layer in enumerate(self.layers[:]      ):
             if i < 1:
                   __A													=      True
             else:
                   layer.initializer(self.layers[i - 1].units      )






 def 						lowercase_    (      self	:Tuple      )					->      List[str]:







       '''simple docstring'''



       for i, layer in enumerate(self.layers[:]      ):
             print(F'------- layer {i} -------'      )
             print('weight.shape '						,					np.shape(layer.weight      )      )
             print('bias.shape '						,					np.shape(layer.bias      )      )






 def 						lowercase_    (      self	:Tuple						,					_A	:List[str]						,					_A	:Optional[int]						,					_A	:Union[str, Any]						,					_A	:str      )					->      Optional[int]:







       '''simple docstring'''



       __A													=      train_round
       __A													=      accuracy

       self.ax_loss.hlines(self.accuracy						,					0						,					self.train_round * 1.1      )

       __A													=      np.shape(_A      )
       for _ in range(_A      ):
             __A													=      0
             for row in range(x_shape[0]      ):
                   __A													=      np.asmatrix(xdata[row, :]      ).T
                   __A													=      np.asmatrix(ydata[row, :]      ).T

                   # forward propagation
                   for layer in self.layers:
                         __A													=      layer.forward_propagation(_xdata      )

                   __A						,   __A													=      self.cal_loss(_ydata						,					_xdata      )
                   __A													=      all_loss + loss

                   # back propagation: the input_layer does not upgrade
                   for layer in self.layers[:0:-1]:
                         __A													=      layer.back_propagation(_A      )

             __A													=      all_loss / x_shape[0]
             self.train_mse.append(_A      )

             self.plot_loss()

             if mse < self.accuracy:
                   print('----达到精度----'      )
                   return mse
       return None






 def 						lowercase_    (      self	:Dict						,					_A	:Tuple						,					_A	:str      )					->      Tuple:







       '''simple docstring'''



       __A													=      np.sum(np.power((ydata - ydata_)						,					2      )      )
       __A													=      2 * (ydata_ - ydata)
       # vector (shape is the same as _ydata.shape)
       return self.loss, self.loss_gradient






 def 						lowercase_    (      self	:Tuple      )					->      Dict:







       '''simple docstring'''



       if self.ax_loss.lines:
             self.ax_loss.lines.remove(self.ax_loss.lines[0]      )
       self.ax_loss.plot(self.train_mse						,					'r-'      )
       plt.ion()
       plt.xlabel('step'      )
       plt.ylabel('loss'      )
       plt.show()
       plt.pause(0.1      )





def 							snake_case				(		)->      List[str]:







      """simple docstring"""
      __A													=      np.random.randn(1_0   ,	1_0						)
      __A													=      np.asarray(
          [
              [0.8, 0.4],
              [0.4, 0.3],
              [0.34, 0.45],
              [0.67, 0.32],
              [0.88, 0.67],
              [0.78, 0.77],
              [0.55, 0.66],
              [0.55, 0.43],
              [0.54, 0.1],
              [0.1, 0.5],
          ]						)
      __A													=      BPNN()
      for i in (1_0, 2_0, 3_0, 2):
            model.add_layer(DenseLayer(UpperCAmelCase						)						)
      model.build()
      model.summary()
      model.train(xdata=UpperCAmelCase   ,	ydata=UpperCAmelCase   ,	train_round=1_0_0   ,	accuracy=0.01						)


if __name__ == "__main__":
 example()








'''simple docstring'''





def 							snake_case				(		UpperCAmelCase						)->      int:







      """simple docstring"""
      if not isinstance(UpperCAmelCase   ,	UpperCAmelCase						):
            raise ValueError('double_factorial() only accepts integral values'						)
      if num < 0:
            raise ValueError('double_factorial() not defined for negative values'						)
      __A													=      1
      for i in range(UpperCAmelCase   ,	0   ,	-2						):
            value *= i
      return value


if __name__ == "__main__":
 import doctest

 doctest.testmod()






