code: 
  
  import numpy as np
  from matplotlib import pyplot as plt
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				List[str]							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    return 1 / (1 + np.exp(-1 * x							))
  
  
  
  
  
  
  
  class 		__magic_name__    :
  
     '''simple docstring'''
  
  
  
  
     def __init__(       self,					lowercase_,					lowercase_=None,					lowercase_=None,					lowercase_=False )							->					str:
  
  
  
  
  
  
       """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 				_UpperCAmelCase				(       self,					lowercase_ )							->					List[str]:
  
  
  
  
  
  
       """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 				_UpperCAmelCase				(       self )							->					Optional[int]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if self.activation == sigmoid:
         a__						=np.dot(self.output,					(1 - self.output).T )
         a__						=np.diag(np.diag(lowercase_ ) )
       else:
         a__						=1
       return gradient_activation
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_ )							->					List[Any]:
  
  
  
  
  
  
       """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 				_UpperCAmelCase				(       self,					lowercase_ )							->					int:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=self.cal_gradient()  # i * i 维
       a__						=np.asmatrix(np.dot(gradient.T,					lowercase_ ) )
  
       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(lowercase_,					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 		__magic_name__    :
  
     '''simple docstring'''
  
  
  
  
     def __init__(       self )							->					List[Any]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=[]
       a__						=[]
       a__						=plt.figure()
       a__						=self.fig_loss.add_subplot(1,					1,					1 )
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_ )							->					str:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       self.layers.append(lowercase_ )
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Optional[Any]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       for i, layer in enumerate(self.layers[:] ):
         if i < 1:
           a__						=True
         else:
           layer.initializer(self.layers[i - 1].units )
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Optional[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 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_,					lowercase_,					lowercase_ )							->					List[Any]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=train_round
       a__						=accuracy
  
       self.ax_loss.hlines(self.accuracy,					0,					self.train_round * 1.1 )
  
       a__						=np.shape(lowercase_ )
       for _ in range(lowercase_ ):
         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(lowercase_ )
  
         a__						=all_loss / x_shape[0]
         self.train_mse.append(lowercase_ )
  
         self.plot_loss()
  
         if mse < self.accuracy:
           print('''----达到精度----''' )
           return mse
       return None
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ )							->					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 				_UpperCAmelCase				(       self )							->					Optional[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__       (    ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						=np.random.randn(10			,   10							)
    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 (10, 20, 30, 2):
      model.add_layer(DenseLayer(_A							)							)
    model.build()
    model.summary()
    model.train(xdata=_A			,   ydata=_A			,   train_round=1_00			,   accuracy=0.01							)
  
  
  if __name__ == "__main__":
         example()
  
style_context: 
  
  from dataclasses import dataclass
  from typing import List, Optional, Union
  
  import numpy as np
  import PIL
  from PIL import Image
  
  from ...utils import BaseOutput, is_torch_available, is_transformers_available
  
  
  
  
  
  
  
  @dataclass
  # Copied from diffusers.pipelines.stable_diffusion.__init__.StableDiffusionPipelineOutput with Stable->Alt
  class 		__magic_name__    (							lowerCamelCase__							):
  
     '''simple docstring'''
  
  
  
  
     lowerCamelCase__				:			Union[List[PIL.Image.Image], np.ndarray]
     lowerCamelCase__				:			Optional[List[bool]]
  
  
  if is_transformers_available() and is_torch_available():
         from .modeling_roberta_series import RobertaSeriesModelWithTransformation
         from .pipeline_alt_diffusion import AltDiffusionPipeline
         from .pipeline_alt_diffusion_imgaimg import AltDiffusionImgaImgPipeline
  