

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()



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
