





'''simple docstring'''






import numpy as np
from matplotlib import pyplot as plt





def    lowercase_   (		lowercase__    ) ->Dict:
						return 1 / (1 + np.exp(-1 * x    ))







class       lowerCamelCase       :


					def __init__(       self						:	Optional[Any]							,  __snake_case						:	Optional[Any]							,  __snake_case						:	str=None							,  __snake_case						:	Optional[int]=None							,  __snake_case						:	Union[str, Any]=False							):





											'''simple docstring'''





											_snake_case: Optional[Any]	       =		units
											_snake_case: Union[str, Any]	       =		None
											_snake_case: List[str]	       =		None
											_snake_case: Union[str, Any]	       =		activation
											if learning_rate is None:
																	_snake_case: int	       =		0.3
											_snake_case: List[str]	       =		learning_rate
											_snake_case: List[Any]	       =		is_input_layer


					def  SCREAMING_SNAKE_CASE_		(       self						:	str							,  __snake_case						:	Optional[int]							):





											'''simple docstring'''





											_snake_case: Tuple	       =		np.asmatrix(np.random.normal(0							,  0.5							,  (self.units, back_units)							)							)
											_snake_case: Optional[Any]	       =		np.asmatrix(np.random.normal(0							,  0.5							,  self.units							)							).T
											if self.activation is None:
																	_snake_case: int	       =		sigmoid


					def  SCREAMING_SNAKE_CASE_		(       self						:	Dict							):





											'''simple docstring'''





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


					def  SCREAMING_SNAKE_CASE_		(       self						:	Dict							,  __snake_case						:	int							):





											'''simple docstring'''





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




					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[Any]							,  __snake_case						:	Optional[Any]							):





											'''simple docstring'''





											_snake_case: Optional[Any]	       =		self.cal_gradient()  # i * i 维
											_snake_case: List[Any]	       =		np.asmatrix(np.dot(gradient.T							,  lowerCamelCase_							)							)

											_snake_case: Union[str, Any]	       =		np.asmatrix(self.xdata							)
											_snake_case: List[str]	       =		-1
											_snake_case: List[str]	       =		self.weight

											_snake_case: Optional[int]	       =		np.dot(gradient.T							,  self._gradient_weight.T							)
											_snake_case: List[Any]	       =		gradient * self._gradient_bias
											_snake_case: Any	       =		np.dot(lowerCamelCase_							,  self._gradient_x							).T
											# upgrade: the Negative gradient direction
											_snake_case: Any	       =		self.weight - self.learn_rate * self.gradient_weight
											_snake_case: Optional[Any]	       =		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       lowerCamelCase       :


					def __init__(       self						:	Tuple							):





											'''simple docstring'''





											_snake_case: Any	       =		[]
											_snake_case: Tuple	       =		[]
											_snake_case: Union[str, Any]	       =		plt.figure()
											_snake_case: List[Any]	       =		self.fig_loss.add_subplot(1							,  1							,  1							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[Any]							,  __snake_case						:	List[Any]							):





											'''simple docstring'''





											self.layers.append(lowerCamelCase_							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Union[str, Any]							):





											'''simple docstring'''





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


					def  SCREAMING_SNAKE_CASE_		(       self						:	int							):





											'''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  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							,  __snake_case						:	Any							,  __snake_case						:	Tuple							,  __snake_case						:	Any							,  __snake_case						:	Union[str, Any]							):





											'''simple docstring'''





											_snake_case: Tuple	       =		train_round
											_snake_case: Tuple	       =		accuracy

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

											_snake_case: List[str]	       =		np.shape(lowerCamelCase_							)
											for _ in range(lowerCamelCase_							):
																	_snake_case: Union[str, Any]	       =		0
																	for row in range(x_shape[0]							):
																							_snake_case: str	       =		np.asmatrix(xdata[row, :]							).T
																							_snake_case: int	       =		np.asmatrix(ydata[row, :]							).T

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

																							_snake_case       ,    _snake_case: Dict	       =		self.cal_loss(_ydata							,  _xdata							)
																							_snake_case: Optional[Any]	       =		all_loss + loss

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

																	_snake_case: Any	       =		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  SCREAMING_SNAKE_CASE_		(       self						:	Optional[Any]							,  __snake_case						:	Union[str, Any]							,  __snake_case						:	Union[str, Any]							):





											'''simple docstring'''





											_snake_case: Optional[Any]	       =		np.sum(np.power((ydata - ydata_)							,  2							)							)
											_snake_case: Optional[int]	       =		2 * (ydata_ - ydata)
											# vector (shape is the same as _ydata.shape)
											return self.loss, self.loss_gradient




					def  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							):





											'''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    lowercase_   (		) ->List[str]:
						_snake_case: Optional[Any]	       =		np.random.randn(10					,	10    )
						_snake_case: Dict	       =		np.asarray(
						    [
						        [0.8, 0.4],
						        [0.4, 0.3],
						        [0.3_4, 0.4_5],
						        [0.6_7, 0.3_2],
						        [0.8_8, 0.6_7],
						        [0.7_8, 0.7_7],
						        [0.5_5, 0.6_6],
						        [0.5_5, 0.4_3],
						        [0.5_4, 0.1],
						        [0.1, 0.5],
						    ]    )
						_snake_case: Optional[int]	       =		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=100					,	accuracy=0.0_1    )


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_deformable_detr': ['DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeformableDetrConfig'],
}

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

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE_  					= [
      'DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
      'DeformableDetrForObjectDetection',
      'DeformableDetrModel',
      'DeformableDetrPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor
    from .image_processing_deformable_detr import DeformableDetrImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_deformable_detr import (
        DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
        DeformableDetrForObjectDetection,
        DeformableDetrModel,
        DeformableDetrPreTrainedModel,
    )

else:
  import sys

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

