

import math
import random
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__ = False				)			->			float:
       if deriv:
              return value * (1 - value)
       return 1 / (1 + math.exp(-value				))


# Initial Value
__lowerCamelCase		       =    0.02
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__				)			->			float:
       A_						=      float(2 * (random.randint(1,       1_00				)) - 1				)

       for _ in range(UpperCAmelCase__				):
              # Forward propagation
              A_						=      sigmoid_function(INITIAL_VALUE * weight				)
              # How much did we miss?
              A_						=      (expected / 1_00) - layer_a
              # Error delta
              A_						=      layer_1_error * sigmoid_function(UpperCAmelCase__,       UpperCAmelCase__				)
              # Update weight
              weight += INITIAL_VALUE * layer_1_delta

       return layer_a * 1_00


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      __lowerCamelCase		       =    int(input('''Expected value: '''))
      __lowerCamelCase		       =    int(input('''Number of propagations: '''))
      print(forward_propagation(expected, number_propagations))



'''simple docstring'''







import warnings

from diffusers import StableDiffusionImgaImgPipeline  # noqa F401


warnings.warn(
    '''The `image_to_image.py` script is outdated. Please use directly `from diffusers import'''
    ''' StableDiffusionImg2ImgPipeline` instead.'''
)
