




'''simple docstring'''



import numpy as np
from matplotlib import pyplot as plt







def      _UpperCamelCase	(_lowerCamelCase							:       Optional[int]		)->				Dict:




     '''simple docstring'''





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





class       lowerCAmelCase				:


       def __init__(				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE=None      ,					__SCREAMING_SNAKE_CASE=None      ,					__SCREAMING_SNAKE_CASE=False					) ->					Dict:



            '''simple docstring'''




            __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  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE					) ->					Union[str, Any]:



            '''simple docstring'''




            __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  lowerCAmelCase (				self					) ->					Dict:



            '''simple docstring'''




            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  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE					) ->					Tuple:



            '''simple docstring'''




            __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  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE					) ->					str:



            '''simple docstring'''




            __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       lowerCAmelCase				:


       def __init__(				self					) ->					int:



            '''simple docstring'''




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


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE					) ->					Tuple:



            '''simple docstring'''




            self.layers.append(__SCREAMING_SNAKE_CASE					)


       def  lowerCAmelCase (				self					) ->					str:



            '''simple docstring'''




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


       def  lowerCAmelCase (				self					) ->					Any:



            '''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  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					) ->					Union[str, Any]:



            '''simple docstring'''




            __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  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					) ->					str:



            '''simple docstring'''




            __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  lowerCAmelCase (				self					) ->					Any:



            '''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      _UpperCamelCase	()->				Optional[int]:




     '''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(_lowerCamelCase		)		)
     model.build()
     model.summary()
     model.train(xdata=_lowerCamelCase				,		ydata=_lowerCamelCase				,		train_round=1_00				,		accuracy=0.01		)


if __name__ == "__main__":
     example()












'''simple docstring'''



from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCAmelCase_    :					Any					      =						{
    '''configuration_retribert''': ['''RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RetriBertConfig'''],
    '''tokenization_retribert''': ['''RetriBertTokenizer'''],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase_    :					str					      =						['''RetriBertTokenizerFast''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase_    :					Optional[int]					      =						[
         '''RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''RetriBertModel''',
         '''RetriBertPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_retribert import RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RetriBertConfig
     from .tokenization_retribert import RetriBertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_retribert_fast import RetriBertTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_retribert import (
              RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              RetriBertModel,
              RetriBertPreTrainedModel,
          )

else:
     import sys

     UpperCAmelCase_    :					Any					      =						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






