




import numpy


class        UpperCamelCase_					:

    '''simple docstring'''






    def __init__(    self   :			Any      ,				UpperCAmelCase__   :			numpy.ndarray      ,				UpperCAmelCase__   :			numpy.ndarray)		->None:





           '''simple docstring'''

           A__   				=					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__   				=					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__   				=					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__   				=					numpy.random.rand(3      ,				1)

           # Real output values provided.
           A__   				=					output_array

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


    def  SCREAMING_SNAKE_CASE							(    self   :			Any)		->numpy.ndarray:





           '''simple docstring'''

           A__   				=					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__   				=					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__   				=					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  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any])		->None:





           '''simple docstring'''

           A__   				=					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__   				=					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__   				=					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  SCREAMING_SNAKE_CASE							(    self   :			str      ,				UpperCAmelCase__   :			numpy.ndarray      ,				UpperCAmelCase__   :			int      ,				UpperCAmelCase__   :			bool)		->None:





           '''simple docstring'''

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





    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any]      ,				UpperCAmelCase__   :			numpy.ndarray)		->int:





           '''simple docstring'''

           A__   				=					input_arr

           A__   				=					sigmoid(
               numpy.dot(self.array      ,				self.input_layer_and_first_hidden_layer_weights))

           A__   				=					sigmoid(
               numpy.dot(
                   self.layer_between_input_and_first_hidden_layer      ,				self.first_hidden_layer_and_second_hidden_layer_weights      ,				))

           A__   				=					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 SCREAMING_SNAKE_CASE       ( lowercase_		)     -> numpy.ndarray:



       """simple docstring"""

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







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> numpy.ndarray:



       """simple docstring"""

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







def SCREAMING_SNAKE_CASE       ( )     -> int:



       """simple docstring"""

       A__   				=					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__   				=					numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])				,       dtype=numpy.floataa		)

       # Calling neural network class.
       A__   				=					TwoHiddenLayerNeuralNetwork(
           input_array=lowercase_				,       output_array=lowercase_		)

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

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


if __name__ == "__main__":
  example()








from __future__ import annotations

import numpy as np







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> List[Any]:



       """simple docstring"""

       return np.maximum(0				,       lowercase_		)


if __name__ == "__main__":
  print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]


