


'''simple docstring'''

import numpy as np
from matplotlib import pyplot as plt

def _SCREAMING_SNAKE_CASE				(A	)	->	Any:






							"""simple docstring"""







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





						'''simple docstring'''






						def __init__(self   :   Dict       ,						UpperCamelCase   :   Optional[Any]       ,						UpperCamelCase   :   Tuple=None       ,						UpperCamelCase   :   Optional[int]=None       ,						UpperCamelCase   :   Optional[int]=False			):

													'''simple docstring'''







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



						def 				UpperCamelCase__		(self   :   List[Any]       ,						UpperCamelCase   :   Optional[Any]			):

													'''simple docstring'''







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



						def 				UpperCamelCase__		(self   :   Optional[Any]			):

													'''simple docstring'''







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



						def 				UpperCamelCase__		(self   :   Optional[Any]       ,						UpperCamelCase   :   Dict			):

													'''simple docstring'''







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






						def 				UpperCamelCase__		(self   :   Union[str, Any]       ,						UpperCamelCase   :   Dict			):

													'''simple docstring'''







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

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

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





						'''simple docstring'''






						def __init__(self   :   Optional[int]			):

													'''simple docstring'''







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



						def 				UpperCamelCase__		(self   :   str       ,						UpperCamelCase   :   int			):

													'''simple docstring'''







													self.layers.append(UpperCamelCase			)



						def 				UpperCamelCase__		(self   :   Optional[Any]			):

													'''simple docstring'''







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



						def 				UpperCamelCase__		(self   :   Tuple			):

													'''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   :   Dict       ,						UpperCamelCase   :   Dict       ,						UpperCamelCase   :   Dict       ,						UpperCamelCase   :   str       ,						UpperCamelCase   :   Optional[Any]			):

													'''simple docstring'''







													lowercase__				      =			train_round
													lowercase__				      =			accuracy

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

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

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

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

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

																				lowercase__				      =			all_loss / x_shape[0]
																				self.train_mse.append(UpperCamelCase			)

																				self.plot_loss()

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



						def 				UpperCamelCase__		(self   :   Optional[Any]       ,						UpperCamelCase   :   List[str]       ,						UpperCamelCase   :   Optional[int]			):

													'''simple docstring'''







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






						def 				UpperCamelCase__		(self   :   List[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 _SCREAMING_SNAKE_CASE				()	->	Optional[Any]:






							"""simple docstring"""







							lowercase__				      =			np.random.randn(10  ,			10	)
							lowercase__				      =			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],
							    ]	)
							lowercase__				      =			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=0.01	)


if __name__ == "__main__":
	example()








'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase							:       str  						=     {'configuration_nat': ['NAT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NatConfig']}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase							:       Tuple  						=     [
     'NAT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'NatForImageClassification',
     'NatModel',
     'NatPreTrainedModel',
     'NatBackbone',
 ]

if TYPE_CHECKING:
 from .configuration_nat import NAT_PRETRAINED_CONFIG_ARCHIVE_MAP, NatConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_nat import (
      NAT_PRETRAINED_MODEL_ARCHIVE_LIST,
      NatBackbone,
      NatForImageClassification,
      NatModel,
      NatPreTrainedModel,
  )

else:
 import sys

 lowerCamelCase							:       Optional[Any]  						=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




