

import numpy





class     snake_case_			:


							"""simple docstring"""





							def __init__( self					,						_A					,						_A							):
												__lowerCAmelCase					=							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.
												__lowerCAmelCase					=							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.
												__lowerCAmelCase					=							numpy.random.rand(
												    4					,						3							)

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

												# Real output values provided.
												__lowerCAmelCase					=							output_array

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




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




							def        A__  ( self					,						_A							):
												__lowerCAmelCase					=							input_arr

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

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

												__lowerCAmelCase					=							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    (  UpperCAmelCase__      ):

					"""simple docstring"""






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


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






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


def        __lowercase    (  ):

					"""simple docstring"""






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

					# Calling neural network class.
					__lowerCAmelCase					=							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()









import warnings

from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor


lowerCamelCase      				=     logging.get_logger(__name__)





class     snake_case_			(				_a							):


							"""simple docstring"""





							def __init__( self					,						*_A					,						**_A							):
												warnings.warn(
												    'The class PoolFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
												    ' Please use PoolFormerImageProcessor instead.'					,						_A					,						)
												super().__init__(*_A					,						**_A							)






