




import numpy
class 			_UpperCamelCase      :
      def __init__(			self   :str ,      lowerCamelCase   :numpy.ndarray ,      lowerCamelCase   :numpy.ndarray      )     ->			None:
       UpperCAmelCase__							 =						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.
       UpperCAmelCase__							 =						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.
       UpperCAmelCase__							 =						numpy.random.rand(
           4 ,      3      )

       # Random initial values for the second hidden layer.
       # Second hidden layer has 3 nodes.
       # Output layer has 1 node.
       UpperCAmelCase__							 =						numpy.random.rand(3 ,      1      )

       # Real output values provided.
       UpperCAmelCase__							 =						output_array

       # Predicted output values by the neural network.
       # Predicted_output array initially consists of zeroes.
       UpperCAmelCase__							 =						numpy.zeros(output_array.shape      )
      def 	UpperCAmelCase_						(			self   :List[Any]      )     ->			numpy.ndarray:
       UpperCAmelCase__							 =						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.
       UpperCAmelCase__							 =						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.
       UpperCAmelCase__							 =						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 	UpperCAmelCase_						(			self   :Tuple      )     ->			None:
       UpperCAmelCase__							 =						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      ) ,      )
       UpperCAmelCase__							 =						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      ) ,      )
       UpperCAmelCase__							 =						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 	UpperCAmelCase_						(			self   :Tuple ,      lowerCamelCase   :numpy.ndarray ,      lowerCamelCase   :int ,      lowerCamelCase   :bool      )     ->			None:
       for iteration in range(1 ,      iterations + 1      ):
        UpperCAmelCase__							 =						self.feedforward()
        self.back_propagation()
        if give_loss:
         UpperCAmelCase__							 =						numpy.mean(numpy.square(output - self.feedforward()      )      )
         print(f'''Iteration {iteration} Loss: {loss}'''      )







      def 	UpperCAmelCase_						(			self   :List[Any] ,      lowerCamelCase   :numpy.ndarray      )     ->			int:
       UpperCAmelCase__							 =						input_arr

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

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

       UpperCAmelCase__							 =						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 						lowerCAmelCase		(		_lowerCAmelCase							:   numpy.ndarray	):




 """simple docstring"""







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



def 						lowerCAmelCase		(		_lowerCAmelCase							:   numpy.ndarray	):




 """simple docstring"""







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



def 						lowerCAmelCase		(		):




 """simple docstring"""







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

 # Calling neural network class.
 UpperCAmelCase__							 =						TwoHiddenLayerNeuralNetwork(
     input_array=_lowerCAmelCase						,							output_array=_lowerCAmelCase	)

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

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


if __name__ == "__main__":
  example()











import warnings

from ...utils import logging
from .image_processing_yolos import YolosImageProcessor


_lowerCAmelCase						:   int		=   logging.get_logger(__name__)
class 			_UpperCamelCase      (				lowerCAmelCase		):
      def __init__(			self   :Optional[Any] ,      *lowerCamelCase   :List[str] ,      **lowerCamelCase   :Tuple      )     ->			None:
       warnings.warn(
           "The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
           " use YolosImageProcessor instead." ,      lowerCamelCase ,      )
       super().__init__(*lowerCamelCase ,      **lowerCamelCase      )





