






"""simple docstring"""



import numpy as np
from matplotlib import pyplot as plt



def 						lowercase   (    __snake_case    :			str     ):
		return 1 / (1 + np.exp(-1 * x     ))





class  _UpperCAmelCase	:




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




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




					def   A  (    self					:						int       )  ->  Tuple:
							# activation function may be sigmoid or linear
							if self.activation == sigmoid:
									lowercase_			:			Optional[int]								=       np.dot(self.output   ,						(1 - self.output).T       )
									lowercase_			:			Any								=       np.diag(np.diag(A       )       )
							else:
									lowercase_			:			int								=       1
							return gradient_activation




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






					def   A  (    self					:						List[str]   ,						A					:						Union[str, Any]       )  ->  str:
							lowercase_			:			List[Any]								=       self.cal_gradient()  # i * i 维
							lowercase_			:			str								=       np.asmatrix(np.dot(gradient.T   ,						A       )       )

							lowercase_			:			Dict								=       np.asmatrix(self.xdata       )
							lowercase_			:			int								=       -1
							lowercase_			:			Dict								=       self.weight

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




					def   A  (    self					:						Dict   ,						A					:						List[Any]       )  ->  Optional[int]:
							self.layers.append(A       )




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




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

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

							lowercase_			:			Optional[Any]								=       np.shape(A       )
							for _ in range(A       ):
									lowercase_			:			Union[str, Any]								=       0
									for row in range(x_shape[0]       ):
											lowercase_			:			str								=       np.asmatrix(xdata[row, :]       ).T
											lowercase_			:			Union[str, Any]								=       np.asmatrix(ydata[row, :]       ).T

											# forward propagation
											for layer in self.layers:
													lowercase_			:			Optional[int]								=       layer.forward_propagation(_xdata       )

											lowercase_	, lowercase_			:			List[str]								=       self.cal_loss(_ydata   ,						_xdata       )
											lowercase_			:			List[Any]								=       all_loss + loss

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

									lowercase_			:			Tuple								=       all_loss / x_shape[0]
									self.train_mse.append(A       )

									self.plot_loss()

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




					def   A  (    self					:						List[str]   ,						A					:						List[str]   ,						A					:						str       )  ->  Optional[int]:
							lowercase_			:			Tuple								=       np.sum(np.power((ydata - ydata_)   ,						2       )       )
							lowercase_			:			List[str]								=       2 * (ydata_ - ydata)
							# vector (shape is the same as _ydata.shape)
							return self.loss, self.loss_gradient






					def   A  (    self					:						List[str]       )  ->  Tuple:
							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   (    ):
		lowercase_			:			List[Any]								=       np.random.randn(1_0		, 1_0     )
		lowercase_			:			List[Any]								=       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_			:			List[Any]								=       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()











"""simple docstring"""



import math
import os

import cva
import numpy as np

__A		:	int      =		255.0



def 						lowercase   (    __snake_case    :			float		, __snake_case    :			float     ):
		lowercase_			:			Any								=       np.mean((original - contrast) ** 2     )
		if mse == 0:
				return 1_0_0

		return 2_0 * math.logaa(PIXEL_MAX / math.sqrt(__snake_case     )     )



def 						lowercase   (    ):
		lowercase_			:			Any								=       os.path.dirname(os.path.realpath(__snake_case     )     )
		# Loading images (original image and compressed image)
		lowercase_			:			Union[str, Any]								=       cva.imread(os.path.join(__snake_case		, '''image_data/original_image.png'''     )     )
		lowercase_			:			List[Any]								=       cva.imread(os.path.join(__snake_case		, '''image_data/compressed_image.png'''     )		, 1     )

		lowercase_			:			Tuple								=       cva.imread(os.path.join(__snake_case		, '''image_data/PSNR-example-base.png'''     )     )
		lowercase_			:			List[str]								=       cva.imread(
		    os.path.join(__snake_case		, '''image_data/PSNR-example-comp-10.jpg'''     )		, 1     )

		# Value expected: 29.73dB
		print('''-- First Test --'''     )
		print(F'''PSNR value is {peak_signal_to_noise_ratio(__snake_case		, __snake_case     )} dB'''     )

		# # Value expected: 31.53dB (Wikipedia Example)
		print('''\n-- Second Test --'''     )
		print(F'''PSNR value is {peak_signal_to_noise_ratio(__snake_case		, __snake_case     )} dB'''     )


if __name__ == "__main__":
						main()



