


'''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'''

import math
import os

import cva
import numpy as np

lowerCamelCase							:       Optional[Any]  						=     2_5_5.0

def _SCREAMING_SNAKE_CASE				(A  ,			A	)	->	float:






       """simple docstring"""







       lowercase__				      =			np.mean((original - contrast) ** 2	)
       if mse == 0:
              return 100

       return 20 * math.logaa(PIXEL_MAX / math.sqrt(A	)	)

def _SCREAMING_SNAKE_CASE				()	->	None:






       """simple docstring"""







       lowercase__				      =			os.path.dirname(os.path.realpath(A	)	)
       # Loading images (original image and compressed image)
       lowercase__				      =			cva.imread(os.path.join(A  ,			'''image_data/original_image.png'''	)	)
       lowercase__				      =			cva.imread(os.path.join(A  ,			'''image_data/compressed_image.png'''	)  ,			1	)

       lowercase__				      =			cva.imread(os.path.join(A  ,			'''image_data/PSNR-example-base.png'''	)	)
       lowercase__				      =			cva.imread(
           os.path.join(A  ,			'''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(A  ,			A	)} dB"	)

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


if __name__ == "__main__":
 main()




