





'''simple docstring'''






import numpy as np
from matplotlib import pyplot as plt
def      UpperCamelCase__   (	lowerCAmelCase						):





      """simple docstring"""


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




class 				UpperCAmelCase  :




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




      def        lowercase__					(    self    :				List[str]    ,    __snake_case    :				Optional[Any]						)       ->   Dict:
            _lowerCAmelCase							  =							np.asmatrix(np.random.normal(0    ,    0.5    ,    (self.units, back_units)						)						)
            _lowerCAmelCase							  =							np.asmatrix(np.random.normal(0    ,    0.5    ,    self.units						)						).T
            if self.activation is None:
                  _lowerCAmelCase							  =							sigmoid




      def        lowercase__					(    self    :				List[Any]						)       ->   str:
            # activation function may be sigmoid or linear
            if self.activation == sigmoid:
                  _lowerCAmelCase							  =							np.dot(self.output    ,    (1 - self.output).T						)
                  _lowerCAmelCase							  =							np.diag(np.diag(__snake_case						)						)
            else:
                  _lowerCAmelCase							  =							1
            return gradient_activation




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




      def        lowercase__					(    self    :				Optional[Any]    ,    __snake_case    :				int						)       ->   List[Any]:
            _lowerCAmelCase							  =							self.cal_gradient()  # i * i 维
            _lowerCAmelCase							  =							np.asmatrix(np.dot(gradient.T    ,    __snake_case						)						)

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

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




      def        lowercase__					(    self    :				str    ,    __snake_case    :				Tuple						)       ->   Tuple:
            self.layers.append(__snake_case						)




      def        lowercase__					(    self    :				List[str]						)       ->   str:
            for i, layer in enumerate(self.layers[:]						):
                  if i < 1:
                        _lowerCAmelCase							  =							True
                  else:
                        layer.initializer(self.layers[i - 1].units						)




      def        lowercase__					(    self    :				Any						)       ->   Tuple:
            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        lowercase__					(    self    :				Optional[int]    ,    __snake_case    :				List[str]    ,    __snake_case    :				List[Any]    ,    __snake_case    :				List[Any]    ,    __snake_case    :				Optional[int]						)       ->   List[str]:
            _lowerCAmelCase							  =							train_round
            _lowerCAmelCase							  =							accuracy

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

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

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

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

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

                  _lowerCAmelCase							  =							all_loss / x_shape[0]
                  self.train_mse.append(__snake_case						)

                  self.plot_loss()

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




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




      def        lowercase__					(    self    :				List[str]						)       ->   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      UpperCamelCase__   (	):





      """simple docstring"""


      _lowerCAmelCase							  =							np.random.randn(10							,			10						)
      _lowerCAmelCase							  =							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],
          ]						)
      _lowerCAmelCase							  =							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'''






import torch

from transformers import PreTrainedModel

from .custom_configuration import CustomConfig, NoSuperInitConfig




class 				UpperCAmelCase  (    snake_case_     ):
      _lowercase:    str		  =     CustomConfig




      def __init__(    self    :				Optional[int]    ,    __snake_case    :				Tuple						)       ->   Optional[Any]:
            super().__init__(__snake_case						)
            _lowerCAmelCase							  =							torch.nn.Linear(config.hidden_size    ,    config.hidden_size						)




      def        lowercase__					(    self    :				Dict    ,    __snake_case    :				List[Any]						)       ->   Dict:
            return self.linear(__snake_case						)




      def        lowercase__					(    self    :				List[Any]    ,    __snake_case    :				int						)       ->   Any:
            pass




class 				UpperCAmelCase  (    snake_case_     ):
      _lowercase:    List[str]		  =     NoSuperInitConfig




      def __init__(    self    :				List[str]    ,    __snake_case    :				List[str]						)       ->   int:
            super().__init__(__snake_case						)
            _lowerCAmelCase							  =							torch.nn.Linear(config.attribute    ,    config.attribute						)




      def        lowercase__					(    self    :				List[str]    ,    __snake_case    :				Dict						)       ->   Any:
            return self.linear(__snake_case						)




      def        lowercase__					(    self    :				Optional[int]    ,    __snake_case    :				List[Any]						)       ->   Dict:
            pass






