




import numpy







class a :
						"""simple docstring"""

						def __init__( self :     Any				,							lowerCamelCase__ :     numpy.ndarray				,							lowerCamelCase__ :     numpy.ndarray				)    ->						None:
													"""simple docstring"""


													__lowercase      =    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.
													__lowercase      =    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.
													__lowercase      =    numpy.random.rand(
													    4				,							3				)

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

													# Real output values provided.
													__lowercase      =    output_array

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






						def     UpperCAmelCase_      ( self :     Dict				)    ->						numpy.ndarray:
													"""simple docstring"""


													__lowercase      =    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.
													__lowercase      =    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.
													__lowercase      =    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 :     str				)    ->						None:
													"""simple docstring"""


													__lowercase      =    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				)				,							)
													__lowercase      =    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				)				,							)
													__lowercase      =    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 :     Optional[int]				,							lowerCamelCase__ :     numpy.ndarray				,							lowerCamelCase__ :     int				,							lowerCamelCase__ :     bool				)    ->						None:
													"""simple docstring"""


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






						def     UpperCAmelCase_      ( self :     int				,							lowerCamelCase__ :     numpy.ndarray				)    ->						int:
													"""simple docstring"""


													__lowercase      =    input_arr

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

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

													__lowercase      =    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 		_A(       UpperCamelCase__							:			numpy.ndarray				)    -> numpy.ndarray:
							'''simple docstring'''


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







def 		_A(       UpperCamelCase__							:			numpy.ndarray				)    -> numpy.ndarray:
							'''simple docstring'''


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







def 		_A(       )    -> int:
							'''simple docstring'''


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

							# Calling neural network class.
							__lowercase      =    TwoHiddenLayerNeuralNetwork(
							    input_array=UpperCamelCase__				,       output_array=UpperCamelCase__				)

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

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


if __name__ == "__main__":
			example()












from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels






