




import numpy as np
from matplotlib import pyplot as plt




def  a_		(							__snake_case					)	->			Optional[int]:

			'''simple docstring'''


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




class     A					:







							def __init__(    self   :	Optional[Any]   ,     __UpperCAmelCase   :	int   ,     __UpperCAmelCase   :	Dict=None   ,     __UpperCAmelCase   :	List[str]=None   ,     __UpperCAmelCase   :	Optional[Any]=False				)		->					Any:

										"""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        lowercase__							(    self   :	List[Any]   ,     __UpperCAmelCase   :	Dict				)		->					Dict:

										"""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        lowercase__							(    self   :	int				)		->					str:

										"""simple docstring"""



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







							def        lowercase__							(    self   :	Optional[int]   ,     __UpperCAmelCase   :	List[Any]				)		->					str:

										"""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        lowercase__							(    self   :	str   ,     __UpperCAmelCase   :	Any				)		->					int:

										"""simple docstring"""



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

										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(__UpperCAmelCase   ,     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   :	Optional[int]				)		->					Optional[int]:

										"""simple docstring"""



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







							def        lowercase__							(    self   :	int   ,     __UpperCAmelCase   :	Dict				)		->					Union[str, Any]:

										"""simple docstring"""



										self.layers.append(__UpperCAmelCase				)







							def        lowercase__							(    self   :	Optional[int]				)		->					Tuple:

										"""simple docstring"""



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







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

										"""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        lowercase__							(    self   :	List[str]   ,     __UpperCAmelCase   :	Optional[int]   ,     __UpperCAmelCase   :	Any   ,     __UpperCAmelCase   :	Tuple   ,     __UpperCAmelCase   :	List[str]				)		->					Union[str, Any]:

										"""simple docstring"""



										UpperCamelCase_     				= train_round
										UpperCamelCase_     				= accuracy

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

										UpperCamelCase_     				= np.shape(__UpperCAmelCase				)
										for _ in range(__UpperCAmelCase				):
													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(__UpperCAmelCase				)

													UpperCamelCase_     				= 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        lowercase__							(    self   :	Any   ,     __UpperCAmelCase   :	Dict   ,     __UpperCAmelCase   :	Dict				)		->					Dict:

										"""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        lowercase__							(    self   :	Optional[Any]				)		->					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  a_		(							)	->			Dict:

			'''simple docstring'''


			UpperCamelCase_     				= np.random.randn(1_0     ,		1_0					)
			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 (1_0, 2_0, 3_0, 2):
						model.add_layer(DenseLayer(__snake_case					)					)
			model.build()
			model.summary()
			model.train(xdata=__snake_case     ,		ydata=__snake_case     ,		train_round=1_0_0     ,		accuracy=0.01					)


if __name__ == "__main__":
				example()






def  a_		(							__snake_case					)	->			str:

			'''simple docstring'''


			return " ".join([word[::-1] for word in input_str.split()]					)


if __name__ == "__main__":
				import doctest

				doctest.testmod()
