
"""simple docstring"""







import numpy as np
from matplotlib import pyplot as plt
def       __UpperCAmelCase		(		a_      :		List[str]					)  ->							Dict:
   return 1 / (1 + np.exp(-1 * x					))






class      __a      :







       def __init__(       self				,lowerCamelCase_				,lowerCamelCase_=None				,lowerCamelCase_=None				,lowerCamelCase_=False		):



          '''simple docstring'''

          _UpperCAmelCase 						=       units
          _UpperCAmelCase 						=       None
          _UpperCAmelCase 						=       None
          _UpperCAmelCase 						=       activation
          if learning_rate is None:
             _UpperCAmelCase 						=       0.3
          _UpperCAmelCase 						=       learning_rate
          _UpperCAmelCase 						=       is_input_layer







       def  _snake_case      (       self				,lowerCamelCase_		):



          '''simple docstring'''

          _UpperCAmelCase 						=       np.asmatrix(np.random.normal(0				,0.5				,(self.units, back_units)		)		)
          _UpperCAmelCase 						=       np.asmatrix(np.random.normal(0				,0.5				,self.units		)		).T
          if self.activation is None:
             _UpperCAmelCase 						=       sigmoid







       def  _snake_case      (       self		):



          '''simple docstring'''

          if self.activation == sigmoid:
             _UpperCAmelCase 						=       np.dot(self.output				,(1 - self.output).T		)
             _UpperCAmelCase 						=       np.diag(np.diag(lowerCamelCase_		)		)
          else:
             _UpperCAmelCase 						=       1
          return gradient_activation







       def  _snake_case      (       self				,lowerCamelCase_		):



          '''simple docstring'''

          _UpperCAmelCase 						=       xdata
          if self.is_input_layer:
             # input layer
             _UpperCAmelCase 						=       xdata
             _UpperCAmelCase 						=       xdata
             return xdata
          else:
             _UpperCAmelCase 						=       np.dot(self.weight				,self.xdata		) - self.bias
             _UpperCAmelCase 						=       self.activation(self.wx_plus_b		)
             return self.output







       def  _snake_case      (       self				,lowerCamelCase_		):



          '''simple docstring'''

          _UpperCAmelCase 						=       self.cal_gradient()  # i * i 维
          _UpperCAmelCase 						=       np.asmatrix(np.dot(gradient.T				,lowerCamelCase_		)		)

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

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







       def __init__(       self		):



          '''simple docstring'''

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







       def  _snake_case      (       self				,lowerCamelCase_		):



          '''simple docstring'''

          self.layers.append(lowerCamelCase_		)







       def  _snake_case      (       self		):



          '''simple docstring'''

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







       def  _snake_case      (       self		):



          '''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  _snake_case      (       self				,lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_		):



          '''simple docstring'''

          _UpperCAmelCase 						=       train_round
          _UpperCAmelCase 						=       accuracy

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

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

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

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

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

             _UpperCAmelCase 						=       all_loss / x_shape[0]
             self.train_mse.append(lowerCamelCase_		)

             self.plot_loss()

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







       def  _snake_case      (       self				,lowerCamelCase_				,lowerCamelCase_		):



          '''simple docstring'''

          _UpperCAmelCase 						=       np.sum(np.power((ydata - ydata_)				,2		)		)
          _UpperCAmelCase 						=       2 * (ydata_ - ydata)
          # vector (shape is the same as _ydata.shape)
          return self.loss, self.loss_gradient







       def  _snake_case      (       self		):



          '''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		(		)  ->							List[str]:
   _UpperCAmelCase 						=       np.random.randn(10     ,	10					)
   _UpperCAmelCase 						=       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],
       ]					)
   _UpperCAmelCase 						=       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()



"""simple docstring"""







from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_  					= {'configuration_detr': ['DETR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DetrConfig', 'DetrOnnxConfig']}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE_  					= ['DetrFeatureExtractor']
  SCREAMING_SNAKE_CASE_  					= ['DetrImageProcessor']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE_  					= [
      'DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
      'DetrForObjectDetection',
      'DetrForSegmentation',
      'DetrModel',
      'DetrPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_detr import DetrFeatureExtractor
    from .image_processing_detr import DetrImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_detr import (
        DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
        DetrForObjectDetection,
        DetrForSegmentation,
        DetrModel,
        DetrPreTrainedModel,
    )

else:
  import sys

  SCREAMING_SNAKE_CASE_  					= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

