






import numpy as np
from matplotlib import pyplot as plt







def 					lowerCamelCase__	(							_lowerCamelCase	:		Tuple			)				->    Any:
  return 1 / (1 + np.exp(-1 * x			))






class 					a						:






    def __init__(	self					:							Optional[int]	,						__SCREAMING_SNAKE_CASE					:							Dict	,						__SCREAMING_SNAKE_CASE					:							List[Any]=None	,						__SCREAMING_SNAKE_CASE					:							Optional[Any]=None	,						__SCREAMING_SNAKE_CASE					:							Optional[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        UpperCamelCase	(	self					:							Tuple	,						__SCREAMING_SNAKE_CASE					:							Union[str, Any] )					->		List[Any]:
      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        UpperCamelCase	(	self					:							List[str] )					->		int:
      # 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(__SCREAMING_SNAKE_CASE ) )
      else:
        lowerCamelCase_   					=						1
      return gradient_activation






    def        UpperCamelCase	(	self					:							Dict	,						__SCREAMING_SNAKE_CASE					:							Any )					->		int:
      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        UpperCamelCase	(	self					:							List[str]	,						__SCREAMING_SNAKE_CASE					:							Any )					->		List[Any]:
      lowerCamelCase_   					=						self.cal_gradient()  # i * i 维
      lowerCamelCase_   					=						np.asmatrix(np.dot(gradient.T	,						__SCREAMING_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(__SCREAMING_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 					a						:






    def __init__(	self					:							Tuple )					->		Optional[Any]:
      lowerCamelCase_   					=						[]
      lowerCamelCase_   					=						[]
      lowerCamelCase_   					=						plt.figure()
      lowerCamelCase_   					=						self.fig_loss.add_subplot(1	,						1	,						1 )






    def        UpperCamelCase	(	self					:							List[Any]	,						__SCREAMING_SNAKE_CASE					:							Any )					->		Dict:
      self.layers.append(__SCREAMING_SNAKE_CASE )






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






    def        UpperCamelCase	(	self					:							int )					->		Union[str, 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        UpperCamelCase	(	self					:							Optional[int]	,						__SCREAMING_SNAKE_CASE					:							List[Any]	,						__SCREAMING_SNAKE_CASE					:							List[Any]	,						__SCREAMING_SNAKE_CASE					:							str	,						__SCREAMING_SNAKE_CASE					:							Tuple )					->		str:
      lowerCamelCase_   					=						train_round
      lowerCamelCase_   					=						accuracy

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

      lowerCamelCase_   					=						np.shape(__SCREAMING_SNAKE_CASE )
      for _ in range(__SCREAMING_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(__SCREAMING_SNAKE_CASE )

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

        self.plot_loss()

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






    def        UpperCamelCase	(	self					:							Any	,						__SCREAMING_SNAKE_CASE					:							Optional[Any]	,						__SCREAMING_SNAKE_CASE					:							Tuple )					->		int:
      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        UpperCamelCase	(	self					:							Optional[Any] )					->		List[str]:
      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__	(							)				->    Any:
  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=100		,    accuracy=0.01			)


if __name__ == "__main__":
  example()












"""simple docstring"""





import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union






class 					a						( enum.Enum		):
    SCREAMING_SNAKE_CASE						:					str						=			"""timm"""
    SCREAMING_SNAKE_CASE						:					List[Any]						=			"""transformers"""







def 					lowerCamelCase__	(							_lowerCamelCase	:		Optional[Iterable[str]]		,    _lowerCamelCase	:		Optional[Iterable[int]]		,    _lowerCamelCase	:		Optional[Iterable[str]]			)				->    List[Any]:
  if stage_names is None:
    raise ValueError('Stage_names must be set for transformers backbones'			)

  if out_features is not None:
    if not isinstance(_lowerCamelCase		,    (list,)			):
      raise ValueError(F'''out_features must be a list {type(_lowerCamelCase			)}'''			)
    if any(feat not in stage_names for feat in out_features			):
      raise ValueError(F'''out_features must be a subset of stage_names: {stage_names} got {out_features}'''			)

  if out_indices is not None:
    if not isinstance(_lowerCamelCase		,    (list, tuple)			):
      raise ValueError(F'''out_indices must be a list or tuple, got {type(_lowerCamelCase			)}'''			)
    if any(idx >= len(_lowerCamelCase			) for idx in out_indices			):
      raise ValueError('out_indices must be valid indices for stage_names {stage_names}, got {out_indices}'			)

  if out_features is not None and out_indices is not None:
    if len(_lowerCamelCase			) != len(_lowerCamelCase			):
      raise ValueError('out_features and out_indices should have the same length if both are set'			)
    if out_features != [stage_names[idx] for idx in out_indices]:
      raise ValueError('out_features and out_indices should correspond to the same stages if both are set'			)







def 					lowerCamelCase__	(							_lowerCamelCase	:		Optional[List[str]]		,    _lowerCamelCase	:		Optional[Union[List[int], Tuple[int]]]		,    _lowerCamelCase	:		List[str]		,    )				->    Union[str, Any]:
  if out_indices is None and out_features is None:
    lowerCamelCase_   					=						[len(_lowerCamelCase			) - 1]
    lowerCamelCase_   					=						[stage_names[-1]]
  elif out_indices is None and out_features is not None:
    lowerCamelCase_   					=						[stage_names.index(_lowerCamelCase			) for layer in out_features]
  elif out_features is None and out_indices is not None:
    lowerCamelCase_   					=						[stage_names[idx] for idx in out_indices]
  return out_features, out_indices







def 					lowerCamelCase__	(							_lowerCamelCase	:		Optional[List[str]]		,    _lowerCamelCase	:		Optional[Union[List[int], Tuple[int]]]		,    _lowerCamelCase	:		List[str]		,    )				->    Tuple[List[str], List[int]]:
  verify_out_features_out_indices(out_features=_lowerCamelCase		,    out_indices=_lowerCamelCase		,    stage_names=_lowerCamelCase			)
  lowerCamelCase_					,     lowerCamelCase_   					=						_align_output_features_output_indices(
      out_features=_lowerCamelCase		,    out_indices=_lowerCamelCase		,    stage_names=_lowerCamelCase			)
  # Verify that the aligned out_features and out_indices are valid
  verify_out_features_out_indices(out_features=_lowerCamelCase		,    out_indices=_lowerCamelCase		,    stage_names=_lowerCamelCase			)
  return output_features, output_indices






class 					a						:
    SCREAMING_SNAKE_CASE						:					Optional[BackboneType]						=			None






    def        UpperCamelCase	(	self					:							Optional[Any]	,						__SCREAMING_SNAKE_CASE					:							List[str] )					->		None:
      if getattr(self	,						'_backbone'	,						__SCREAMING_SNAKE_CASE ) is None:
        raise ValueError('self._backbone must be set before calling _init_timm_backbone' )

      # These will diagree with the defaults for the transformers models e.g. for resnet50
      # the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
      # the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
      lowerCamelCase_   					=						[stage['module'] for stage in self._backbone.feature_info.info]
      lowerCamelCase_   					=						[stage['num_chs'] for stage in self._backbone.feature_info.info]
      lowerCamelCase_   					=						self._backbone.feature_info.out_indices
      lowerCamelCase_   					=						self._backbone.feature_info.module_name()

      # We verify the out indices and out features are valid
      verify_out_features_out_indices(
          out_features=__SCREAMING_SNAKE_CASE	,						out_indices=__SCREAMING_SNAKE_CASE	,						stage_names=self.stage_names )
      lowerCamelCase_					,     lowerCamelCase_   					=						out_features, out_indices






    def        UpperCamelCase	(	self					:							Tuple	,						__SCREAMING_SNAKE_CASE					:							Any )					->		None:
      lowerCamelCase_   					=						getattr(__SCREAMING_SNAKE_CASE	,						'stage_names' )
      lowerCamelCase_   					=						getattr(__SCREAMING_SNAKE_CASE	,						'out_features'	,						__SCREAMING_SNAKE_CASE )
      lowerCamelCase_   					=						getattr(__SCREAMING_SNAKE_CASE	,						'out_indices'	,						__SCREAMING_SNAKE_CASE )

      lowerCamelCase_   					=						stage_names
      lowerCamelCase_					,     lowerCamelCase_   					=						get_aligned_output_features_output_indices(
          out_features=__SCREAMING_SNAKE_CASE	,						out_indices=__SCREAMING_SNAKE_CASE	,						stage_names=__SCREAMING_SNAKE_CASE )
      # Number of channels for each stage. This is set in the transformer backbone model init
      lowerCamelCase_   					=						None






    def        UpperCamelCase	(	self					:							Any	,						__SCREAMING_SNAKE_CASE					:							Dict )					->		None:
      lowerCamelCase_   					=						config

      lowerCamelCase_   					=						getattr(__SCREAMING_SNAKE_CASE	,						'use_timm_backbone'	,						__SCREAMING_SNAKE_CASE )
      lowerCamelCase_   					=						BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

      if self.backbone_type == BackboneType.TIMM:
        self._init_timm_backbone(__SCREAMING_SNAKE_CASE )
      elif self.backbone_type == BackboneType.TRANSFORMERS:
        self._init_transformers_backbone(__SCREAMING_SNAKE_CASE )
      else:
        raise ValueError(F'''backbone_type {self.backbone_type} not supported.''' )






    @property
    def        UpperCamelCase	(	self					:							Dict )					->		Any:
      return self._out_features






    @out_features.setter
    def        UpperCamelCase	(	self					:							Optional[Any]	,						__SCREAMING_SNAKE_CASE					:							List[str] )					->		List[Any]:
      lowerCamelCase_					,     lowerCamelCase_   					=						get_aligned_output_features_output_indices(
          out_features=__SCREAMING_SNAKE_CASE	,						out_indices=__SCREAMING_SNAKE_CASE	,						stage_names=self.stage_names )






    @property
    def        UpperCamelCase	(	self					:							List[Any] )					->		Dict:
      return self._out_indices






    @out_indices.setter
    def        UpperCamelCase	(	self					:							List[str]	,						__SCREAMING_SNAKE_CASE					:							Union[Tuple[int], List[int]] )					->		List[str]:
      lowerCamelCase_					,     lowerCamelCase_   					=						get_aligned_output_features_output_indices(
          out_features=__SCREAMING_SNAKE_CASE	,						out_indices=__SCREAMING_SNAKE_CASE	,						stage_names=self.stage_names )






    @property
    def        UpperCamelCase	(	self					:							Tuple )					->		Tuple:
      # the current backbones will output the number of channels for each stage
      # even if that stage is not in the out_features list.
      return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names )}






    @property
    def        UpperCamelCase	(	self					:							Dict )					->		List[Any]:
      return [self.out_feature_channels[name] for name in self.out_features]






    def        UpperCamelCase	(	self					:							Optional[int]	,						*__SCREAMING_SNAKE_CASE					:							Any	,						**__SCREAMING_SNAKE_CASE					:							str )					->		Tuple:
      lowerCamelCase_   					=						dict(inspect.signature(self.forward ).parameters )
      lowerCamelCase_   					=						{k: v for k, v in kwargs.items() if k in signature}
      return self(*__SCREAMING_SNAKE_CASE	,						**__SCREAMING_SNAKE_CASE )






    def        UpperCamelCase	(	self					:							Optional[int]	,						__SCREAMING_SNAKE_CASE					:							Tuple	,						__SCREAMING_SNAKE_CASE					:							Optional[bool] = None	,						__SCREAMING_SNAKE_CASE					:							Optional[bool] = None	,						__SCREAMING_SNAKE_CASE					:							Optional[bool] = None	,						)					->		int:
      raise NotImplementedError('This method should be implemented by the derived class.' )






    def        UpperCamelCase	(	self					:							Union[str, Any] )					->		List[str]:
      lowerCamelCase_   					=						super().to_dict()
      lowerCamelCase_   					=						output.pop('_out_features' )
      lowerCamelCase_   					=						output.pop('_out_indices' )
      return output






class 					a						:






    @property
    def        UpperCamelCase	(	self					:							int )					->		Optional[Any]:
      return self._out_features






    @out_features.setter
    def        UpperCamelCase	(	self					:							List[str]	,						__SCREAMING_SNAKE_CASE					:							List[str] )					->		List[str]:
      lowerCamelCase_					,     lowerCamelCase_   					=						get_aligned_output_features_output_indices(
          out_features=__SCREAMING_SNAKE_CASE	,						out_indices=__SCREAMING_SNAKE_CASE	,						stage_names=self.stage_names )






    @property
    def        UpperCamelCase	(	self					:							Union[str, Any] )					->		Tuple:
      return self._out_indices






    @out_indices.setter
    def        UpperCamelCase	(	self					:							List[Any]	,						__SCREAMING_SNAKE_CASE					:							Union[Tuple[int], List[int]] )					->		str:
      lowerCamelCase_					,     lowerCamelCase_   					=						get_aligned_output_features_output_indices(
          out_features=__SCREAMING_SNAKE_CASE	,						out_indices=__SCREAMING_SNAKE_CASE	,						stage_names=self.stage_names )






    def        UpperCamelCase	(	self					:							int )					->		str:
      lowerCamelCase_   					=						super().to_dict()
      lowerCamelCase_   					=						output.pop('_out_features' )
      lowerCamelCase_   					=						output.pop('_out_indices' )
      return output




