




'''simple docstring'''





import numpy as np
from matplotlib import pyplot as plt





def 			__a			(     _UpperCamelCase:					List[Any]					)		->				str:

     """simple docstring"""




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

class      _a       :

     '''simple docstring'''





     def __init__(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE=None      ,_SCREAMING_SNAKE_CASE=None      ,_SCREAMING_SNAKE_CASE=False					)							->    str:
          _snake_case							=   units
          _snake_case							=   None
          _snake_case							=   None
          _snake_case							=   activation
          if learning_rate is None:
               _snake_case							=   0.3
          _snake_case							=   learning_rate
          _snake_case							=   is_input_layer






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE					)							->    List[str]:
          _snake_case							=   np.asmatrix(np.random.normal(0      ,0.5      ,(self.units, back_units)					)					)
          _snake_case							=   np.asmatrix(np.random.normal(0      ,0.5      ,self.units					)					).T
          if self.activation is None:
               _snake_case							=   sigmoid






     def        _lowercase			(				self					)							->    Any:
          # activation function may be sigmoid or linear
          if self.activation == sigmoid:
               _snake_case							=   np.dot(self.output      ,(1 - self.output).T					)
               _snake_case							=   np.diag(np.diag(_SCREAMING_SNAKE_CASE					)					)
          else:
               _snake_case							=   1
          return gradient_activation






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE					)							->    Optional[Any]:
          _snake_case							=   xdata
          if self.is_input_layer:
               # input layer
               _snake_case							=   xdata
               _snake_case							=   xdata
               return xdata
          else:
               _snake_case							=   np.dot(self.weight      ,self.xdata					) - self.bias
               _snake_case							=   self.activation(self.wx_plus_b					)
               return self.output






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE					)							->    Union[str, Any]:
          _snake_case							=   self.cal_gradient()  # i * i 维
          _snake_case							=   np.asmatrix(np.dot(gradient.T      ,_SCREAMING_SNAKE_CASE					)					)

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

          _snake_case							=   np.dot(gradient.T      ,self._gradient_weight.T					)
          _snake_case							=   gradient * self._gradient_bias
          _snake_case							=   np.dot(_SCREAMING_SNAKE_CASE      ,self._gradient_x					).T
          # upgrade: the Negative gradient direction
          _snake_case							=   self.weight - self.learn_rate * self.gradient_weight
          _snake_case							=   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      _a       :

     '''simple docstring'''





     def __init__(				self					)							->    str:
          _snake_case							=   []
          _snake_case							=   []
          _snake_case							=   plt.figure()
          _snake_case							=   self.fig_loss.add_subplot(1      ,1      ,1					)






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE					)							->    Union[str, Any]:
          self.layers.append(_SCREAMING_SNAKE_CASE					)






     def        _lowercase			(				self					)							->    Any:
          for i, layer in enumerate(self.layers[:]					):
               if i < 1:
                    _snake_case							=   True
               else:
                    layer.initializer(self.layers[i - 1].units					)






     def        _lowercase			(				self					)							->    Tuple:
          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      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					)							->    str:
          _snake_case							=   train_round
          _snake_case							=   accuracy

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

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

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

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

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

               _snake_case							=   all_loss / x_shape[0]
               self.train_mse.append(_SCREAMING_SNAKE_CASE					)

               self.plot_loss()

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






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					)							->    Tuple:
          _snake_case							=   np.sum(np.power((ydata - ydata_)      ,2					)					)
          _snake_case							=   2 * (ydata_ - ydata)
          # vector (shape is the same as _ydata.shape)
          return self.loss, self.loss_gradient






     def        _lowercase			(				self					)							->    str:
          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 			__a			(     )		->				Union[str, Any]:

     """simple docstring"""




     _snake_case							=   np.random.randn(10      ,		10					)
     _snake_case							=   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],
         ]					)
     _snake_case							=   BPNN()
     for i in (10, 20, 30, 2):
          model.add_layer(DenseLayer(_UpperCamelCase					)					)
     model.build()
     model.summary()
     model.train(xdata=_UpperCamelCase      ,		ydata=_UpperCamelCase      ,		train_round=100      ,		accuracy=0.01					)


if __name__ == "__main__":
   example()








'''simple docstring'''





import io
import json
import subprocess


UpperCamelCase_		:    Any									=		[
    ['''en''', '''ru'''],
    ['''ru''', '''en'''],
    ['''en''', '''de'''],
    ['''de''', '''en'''],
]

UpperCamelCase_		:    Union[str, Any]									=		8





def 			__a			(     _UpperCamelCase:					int      ,		_UpperCamelCase:					Optional[Any]					)		->				Optional[int]:

     """simple docstring"""




     _snake_case							=   {}
     for src, tgt in pairs:
          _snake_case							=   F"""{src}-{tgt}"""
          _snake_case							=   F"""sacrebleu -t wmt19 -l {pair} --echo src""".split()
          _snake_case							=   subprocess.run(_UpperCamelCase      ,		stdout=subprocess.PIPE					).stdout.decode("utf-8"					).splitlines()
          _snake_case							=   F"""sacrebleu -t wmt19 -l {pair} --echo ref""".split()
          _snake_case							=   subprocess.run(_UpperCamelCase      ,		stdout=subprocess.PIPE					).stdout.decode("utf-8"					).splitlines()
          _snake_case							=   {"src": src_lines[:n_objs], "tgt": tgt_lines[:n_objs]}
     return text


UpperCamelCase_		:    Tuple									=		get_all_data(pairs, n_objs)
UpperCamelCase_		:    Optional[Any]									=		'''./fsmt_val_data.json'''
with io.open(filename, '''w''', encoding='''utf-8''') as f:
   UpperCamelCase_		:    List[str]									=		json.dump(text, f, indent=2, ensure_ascii=False)


