




import numpy as np
from matplotlib import pyplot as plt


def 		_lowerCamelCase					(       UpperCamelCase				)      ->		Tuple:





  """simple docstring"""





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

class   UpperCAmelCase    :






   def __init__(						self :							Tuple    ,       UpperCamelCase_ :							str    ,       UpperCamelCase_ :							Tuple=None    ,       UpperCamelCase_ :							List[str]=None    ,       UpperCamelCase_ :							Optional[int]=False  )->      Optional[int]:
     _lowercase:			Dict      =     units
     _lowercase:			Optional[int]      =     None
     _lowercase:			List[str]      =     None
     _lowercase:			str      =     activation
     if learning_rate is None:
       _lowercase:			List[Any]      =     0.3
     _lowercase:			List[Any]      =     learning_rate
     _lowercase:			Optional[Any]      =     is_input_layer






   def 			_a    (						self :							Dict    ,       UpperCamelCase_ :							List[Any]  )->      Optional[Any]:
     _lowercase:			Union[str, Any]      =     np.asmatrix(np.random.normal(0    ,       0.5    ,       (self.units, back_units)  )  )
     _lowercase:			List[Any]      =     np.asmatrix(np.random.normal(0    ,       0.5    ,       self.units  )  ).T
     if self.activation is None:
       _lowercase:			int      =     sigmoid






   def 			_a    (						self :							Optional[int]  )->      Union[str, Any]:
     # activation function may be sigmoid or linear
     if self.activation == sigmoid:
       _lowercase:			Tuple      =     np.dot(self.output    ,       (1 - self.output).T  )
       _lowercase:			str      =     np.diag(np.diag(UpperCamelCase_  )  )
     else:
       _lowercase:			List[Any]      =     1
     return gradient_activation






   def 			_a    (						self :							List[Any]    ,       UpperCamelCase_ :							Dict  )->      str:
     _lowercase:			Tuple      =     xdata
     if self.is_input_layer:
       # input layer
       _lowercase:			List[Any]      =     xdata
       _lowercase:			Tuple      =     xdata
       return xdata
     else:
       _lowercase:			List[str]      =     np.dot(self.weight    ,       self.xdata  ) - self.bias
       _lowercase:			List[Any]      =     self.activation(self.wx_plus_b  )
       return self.output






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

     _lowercase:			Union[str, Any]      =     np.asmatrix(self.xdata  )
     _lowercase:			Dict      =     -1
     _lowercase:			List[Any]      =     self.weight

     _lowercase:			List[str]      =     np.dot(gradient.T    ,       self._gradient_weight.T  )
     _lowercase:			Any      =     gradient * self._gradient_bias
     _lowercase:			List[str]      =     np.dot(UpperCamelCase_    ,       self._gradient_x  ).T
     # upgrade: the Negative gradient direction
     _lowercase:			Any      =     self.weight - self.learn_rate * self.gradient_weight
     _lowercase:			Optional[int]      =     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 :							List[Any]  )->      List[str]:
     _lowercase:			Optional[Any]      =     []
     _lowercase:			Tuple      =     []
     _lowercase:			Tuple      =     plt.figure()
     _lowercase:			Dict      =     self.fig_loss.add_subplot(1    ,       1    ,       1  )






   def 			_a    (						self :							Union[str, Any]    ,       UpperCamelCase_ :							int  )->      List[Any]:
     self.layers.append(UpperCamelCase_  )






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






   def 			_a    (						self :							Tuple  )->      List[Any]:
     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 			_a    (						self :							Any    ,       UpperCamelCase_ :							int    ,       UpperCamelCase_ :							Optional[int]    ,       UpperCamelCase_ :							Any    ,       UpperCamelCase_ :							List[str]  )->      Any:
     _lowercase:			Optional[int]      =     train_round
     _lowercase:			Union[str, Any]      =     accuracy

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

     _lowercase:			List[Any]      =     np.shape(UpperCamelCase_  )
     for _ in range(UpperCamelCase_  ):
       _lowercase:			Any      =     0
       for row in range(x_shape[0]  ):
         _lowercase:			List[str]      =     np.asmatrix(xdata[row, :]  ).T
         _lowercase:			Optional[Any]      =     np.asmatrix(ydata[row, :]  ).T

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

         _lowercase  ,       _lowercase:			str      =     self.cal_loss(_ydata    ,       _xdata  )
         _lowercase:			Union[str, Any]      =     all_loss + loss

         # back propagation: the input_layer does not upgrade
         for layer in self.layers[:0:-1]:
           _lowercase:			Union[str, Any]      =     layer.back_propagation(UpperCamelCase_  )

       _lowercase:			str      =     all_loss / x_shape[0]
       self.train_mse.append(UpperCamelCase_  )

       self.plot_loss()

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






   def 			_a    (						self :							Optional[Any]    ,       UpperCamelCase_ :							Any    ,       UpperCamelCase_ :							List[Any]  )->      Tuple:
     _lowercase:			Union[str, Any]      =     np.sum(np.power((ydata - ydata_)    ,       2  )  )
     _lowercase:			Optional[int]      =     2 * (ydata_ - ydata)
     # vector (shape is the same as _ydata.shape)
     return self.loss, self.loss_gradient






   def 			_a    (						self :							List[Any]  )->      Any:
     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 		_lowerCamelCase					(       )      ->		Dict:





  """simple docstring"""





  _lowercase:			List[str]      =     np.random.randn(10       ,  10				)
  _lowercase:			Optional[Any]      =     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],
      ]				)
  _lowercase:			Union[str, Any]      =     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()






from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


UpperCAmelCase__    :							List[str]       		=     {
    'configuration_ernie_m': ['ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ErnieMConfig'],
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__    :							Any       		=     ['ErnieMTokenizer']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__    :							Dict       		=     [
       'ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST',
       'ErnieMForMultipleChoice',
       'ErnieMForQuestionAnswering',
       'ErnieMForSequenceClassification',
       'ErnieMForTokenClassification',
       'ErnieMModel',
       'ErnieMPreTrainedModel',
       'ErnieMForInformationExtraction',
   ]


if TYPE_CHECKING:
   from .configuration_ernie_m import ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieMConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_ernie_m import ErnieMTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_ernie_m import (
          ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST,
          ErnieMForInformationExtraction,
          ErnieMForMultipleChoice,
          ErnieMForQuestionAnswering,
          ErnieMForSequenceClassification,
          ErnieMForTokenClassification,
          ErnieMModel,
          ErnieMPreTrainedModel,
      )


else:
   import sys

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