


'''simple docstring'''


import numpy







class 							__magic_name__	:

     """simple docstring"""






     def __init__(   self			,     lowerCamelCase			,     lowerCamelCase		):


          '''simple docstring'''





          __A	:       List[Any]      =							input_array

          # Random initial weights are assigned where first argument is the
          # number of nodes in previous layer and second argument is the
          # number of nodes in the next layer.

          # Random initial weights are assigned.
          # self.input_array.shape[1] is used to represent number of nodes in input layer.
          # First hidden layer consists of 4 nodes.
          __A	:       Dict      =							numpy.random.rand(
              self.input_array.shape[1]			,     4		)

          # Random initial values for the first hidden layer.
          # First hidden layer has 4 nodes.
          # Second hidden layer has 3 nodes.
          __A	:       List[str]      =							numpy.random.rand(
              4			,     3		)

          # Random initial values for the second hidden layer.
          # Second hidden layer has 3 nodes.
          # Output layer has 1 node.
          __A	:       Union[str, Any]      =							numpy.random.rand(3			,     1		)

          # Real output values provided.
          __A	:       Optional[Any]      =							output_array

          # Predicted output values by the neural network.
          # Predicted_output array initially consists of zeroes.
          __A	:       str      =							numpy.zeros(output_array.shape		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Tuple      =							sigmoid(
              numpy.dot(self.input_array			,     self.input_layer_and_first_hidden_layer_weights		)		)

          # layer_between_first_hidden_layer_and_second_hidden_layer is the layer
          # connecting the first hidden set of nodes with the second hidden set of nodes.
          __A	:       Dict      =							sigmoid(
              numpy.dot(
                  self.layer_between_input_and_first_hidden_layer			,     self.first_hidden_layer_and_second_hidden_layer_weights			,     )		)

          # layer_between_second_hidden_layer_and_output is the layer connecting
          # second hidden layer with the output node.
          __A	:       str      =							sigmoid(
              numpy.dot(
                  self.layer_between_first_hidden_layer_and_second_hidden_layer			,     self.second_hidden_layer_and_output_layer_weights			,     )		)

          return self.layer_between_second_hidden_layer_and_output


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       int      =							numpy.dot(
              self.layer_between_first_hidden_layer_and_second_hidden_layer.T			,     2
              * (self.output_array - self.predicted_output)
              * sigmoid_derivative(self.predicted_output		)			,     )
          __A	:       Optional[int]      =							numpy.dot(
              self.layer_between_input_and_first_hidden_layer.T			,     numpy.dot(
                  2
                  * (self.output_array - self.predicted_output)
                  * sigmoid_derivative(self.predicted_output		)			,     self.second_hidden_layer_and_output_layer_weights.T			,     )
              * sigmoid_derivative(
                  self.layer_between_first_hidden_layer_and_second_hidden_layer		)			,     )
          __A	:       Optional[int]      =							numpy.dot(
              self.input_array.T			,     numpy.dot(
                  numpy.dot(
                      2
                      * (self.output_array - self.predicted_output)
                      * sigmoid_derivative(self.predicted_output		)			,     self.second_hidden_layer_and_output_layer_weights.T			,     )
                  * sigmoid_derivative(
                      self.layer_between_first_hidden_layer_and_second_hidden_layer		)			,     self.first_hidden_layer_and_second_hidden_layer_weights.T			,     )
              * sigmoid_derivative(self.layer_between_input_and_first_hidden_layer		)			,     )

          self.input_layer_and_first_hidden_layer_weights += (
              updated_input_layer_and_first_hidden_layer_weights
          )
          self.first_hidden_layer_and_second_hidden_layer_weights += (
              updated_first_hidden_layer_and_second_hidden_layer_weights
          )
          self.second_hidden_layer_and_output_layer_weights += (
              updated_second_hidden_layer_and_output_layer_weights
          )


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase		):


          '''simple docstring'''





          for iteration in range(1			,     iterations + 1		):
               __A	:       List[Any]      =							self.feedforward()
               self.back_propagation()
               if give_loss:
                    __A	:       Optional[Any]      =							numpy.mean(numpy.square(output - self.feedforward()		)		)
                    print(f"Iteration {iteration} Loss: {loss}"		)


     def lowerCAmelCase__  (   self			,     lowerCamelCase		):


          '''simple docstring'''





          __A	:       int      =							input_arr

          __A	:       Optional[Any]      =							sigmoid(
              numpy.dot(self.array			,     self.input_layer_and_first_hidden_layer_weights		)		)

          __A	:       int      =							sigmoid(
              numpy.dot(
                  self.layer_between_input_and_first_hidden_layer			,     self.first_hidden_layer_and_second_hidden_layer_weights			,     )		)

          __A	:       str      =							sigmoid(
              numpy.dot(
                  self.layer_between_first_hidden_layer_and_second_hidden_layer			,     self.second_hidden_layer_and_output_layer_weights			,     )		)

          return int(self.layer_between_second_hidden_layer_and_output > 0.6		)


def 		_lowercase (SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     return 1 / (1 + numpy.exp(-value							))


def 		_lowercase (SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     return (value) * (1 - (value))


def 		_lowercase ():




     '''simple docstring'''





     __A	:       List[str]      =							numpy.array(
         (
             [0, 0, 0],
             [0, 0, 1],
             [0, 1, 0],
             [0, 1, 1],
             [1, 0, 0],
             [1, 0, 1],
             [1, 1, 0],
             [1, 1, 1],
         )       ,					dtype=numpy.floataa       ,					)

     # True output values for the given input values.
     __A	:       List[str]      =							numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])       ,					dtype=numpy.floataa							)

     # Calling neural network class.
     __A	:       Dict      =							TwoHiddenLayerNeuralNetwork(
         input_array=SCREAMING_SNAKE_CASE       ,					output_array=SCREAMING_SNAKE_CASE							)

     # Calling training function.
     # Set give_loss to True if you want to see loss in every iteration.
     neural_network.train(output=SCREAMING_SNAKE_CASE       ,					iterations=10       ,					give_loss=SCREAMING_SNAKE_CASE							)

     return neural_network.predict(numpy.array(([1, 1, 1])       ,					dtype=numpy.floataa							)							)


if __name__ == "__main__":
      example()





'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCamelCase 							=  {
    """configuration_upernet""": ["""UperNetConfig"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _UpperCamelCase 							=  [
          """UperNetForSemanticSegmentation""",
          """UperNetPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_upernet import UperNetConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel


else:
      import sys

      _UpperCamelCase 							=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

