
import numpy




class 						lowercase__  :
	def __init__(			self							:   Tuple       ,   UpperCAmelCase_							:   numpy.ndarray       ,   UpperCAmelCase_							:   numpy.ndarray							):
						SCREAMING_SNAKE_CASE__													= 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.
						SCREAMING_SNAKE_CASE__													= 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.
						SCREAMING_SNAKE_CASE__													= numpy.random.rand(
						    4       ,   3							)

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

						# Real output values provided.
						SCREAMING_SNAKE_CASE__													= output_array

						# Predicted output values by the neural network.
						# Predicted_output array initially consists of zeroes.
						SCREAMING_SNAKE_CASE__													= numpy.zeros(output_array.shape							)
	def      A_				(			self							:   Any							):
						SCREAMING_SNAKE_CASE__													= 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.
						SCREAMING_SNAKE_CASE__													= 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.
						SCREAMING_SNAKE_CASE__													= 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							:   int							):
						SCREAMING_SNAKE_CASE__													= 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							)       ,   )
						SCREAMING_SNAKE_CASE__													= 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							)       ,   )
						SCREAMING_SNAKE_CASE__													= 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							:   Any       ,   UpperCAmelCase_							:   numpy.ndarray       ,   UpperCAmelCase_							:   int       ,   UpperCAmelCase_							:   bool							):
						for iteration in range(1       ,   iterations + 1							):
											SCREAMING_SNAKE_CASE__													= self.feedforward()
											self.back_propagation()
											if give_loss:
																SCREAMING_SNAKE_CASE__													= numpy.mean(numpy.square(output - self.feedforward()							)							)
																print(F'Iteration {iteration} Loss: {loss}'							)





	def      A_				(			self							:   Union[str, Any]       ,   UpperCAmelCase_							:   numpy.ndarray							):
						SCREAMING_SNAKE_CASE__													= input_arr

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

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

						SCREAMING_SNAKE_CASE__													= 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_		)		->		numpy.ndarray:
					'''simple docstring'''


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






def 							_lowercase							(     UpperCamelCase_		)		->		numpy.ndarray:
					'''simple docstring'''


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






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


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

					# Calling neural network class.
					SCREAMING_SNAKE_CASE__													= 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 typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__snake_case								=  logging.get_logger(__name__)




class 						lowercase__  (						_UpperCAmelCase  ):
	A__    :   List[str]				       =["""pixel_values"""]
	def __init__(			self							:   Dict       ,   UpperCAmelCase_							:   bool = True       ,   UpperCAmelCase_							:   int = 32       ,   UpperCAmelCase_							:   int=PILImageResampling.BILINEAR       ,   UpperCAmelCase_							:   bool = True       ,   **UpperCAmelCase_							:   Any       ,   ):
						SCREAMING_SNAKE_CASE__													= do_resize
						SCREAMING_SNAKE_CASE__													= do_rescale
						SCREAMING_SNAKE_CASE__													= size_divisor
						SCREAMING_SNAKE_CASE__													= resample
						super().__init__(**UpperCAmelCase_							)
	def      A_				(			self							:   Optional[int]       ,   UpperCAmelCase_							:   np.ndarray       ,   UpperCAmelCase_							:   int       ,   UpperCAmelCase_							:   Dict       ,   UpperCAmelCase_							:   Optional[ChannelDimension] = None       ,   **UpperCAmelCase_							:   Dict							):
						SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= get_image_size(UpperCAmelCase_							)
						# Rounds the height and width down to the closest multiple of size_divisor
						SCREAMING_SNAKE_CASE__													= height // size_divisor * size_divisor
						SCREAMING_SNAKE_CASE__													= width // size_divisor * size_divisor
						SCREAMING_SNAKE_CASE__													= resize(UpperCAmelCase_       ,   (new_h, new_w)       ,   resample=UpperCAmelCase_       ,   data_format=UpperCAmelCase_       ,   **UpperCAmelCase_							)
						return image
	def      A_				(			self							:   Any       ,   UpperCAmelCase_							:   np.ndarray       ,   UpperCAmelCase_							:   float       ,   UpperCAmelCase_							:   Optional[ChannelDimension] = None       ,   **UpperCAmelCase_							:   List[str]							):
						return rescale(image=UpperCAmelCase_       ,   scale=UpperCAmelCase_       ,   data_format=UpperCAmelCase_       ,   **UpperCAmelCase_							)





	def      A_				(			self							:   Dict       ,   UpperCAmelCase_							:   Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]       ,   UpperCAmelCase_							:   Optional[bool] = None       ,   UpperCAmelCase_							:   Optional[int] = None       ,   UpperCAmelCase_							:   List[str]=None       ,   UpperCAmelCase_							:   Optional[bool] = None       ,   UpperCAmelCase_							:   Optional[Union[TensorType, str]] = None       ,   UpperCAmelCase_							:   ChannelDimension = ChannelDimension.FIRST       ,   **UpperCAmelCase_							:   List[Any]       ,   ):
						SCREAMING_SNAKE_CASE__													= do_resize if do_resize is not None else self.do_resize
						SCREAMING_SNAKE_CASE__													= do_rescale if do_rescale is not None else self.do_rescale
						SCREAMING_SNAKE_CASE__													= size_divisor if size_divisor is not None else self.size_divisor
						SCREAMING_SNAKE_CASE__													= resample if resample is not None else self.resample

						if do_resize and size_divisor is None:
											raise ValueError('size_divisor is required for resizing'							)

						SCREAMING_SNAKE_CASE__													= make_list_of_images(UpperCAmelCase_							)

						if not valid_images(UpperCAmelCase_							):
											raise ValueError('Invalid image(s)'							)

						# All transformations expect numpy arrays.
						SCREAMING_SNAKE_CASE__													= [to_numpy_array(UpperCAmelCase_							) for img in images]

						if do_resize:
											SCREAMING_SNAKE_CASE__													= [self.resize(UpperCAmelCase_       ,   size_divisor=UpperCAmelCase_       ,   resample=UpperCAmelCase_							) for image in images]

						if do_rescale:
											SCREAMING_SNAKE_CASE__													= [self.rescale(UpperCAmelCase_       ,   scale=1 / 255							) for image in images]

						SCREAMING_SNAKE_CASE__													= [to_channel_dimension_format(UpperCAmelCase_       ,   UpperCAmelCase_							) for image in images]

						SCREAMING_SNAKE_CASE__													= {'pixel_values': images}
						return BatchFeature(data=UpperCAmelCase_       ,   tensor_type=UpperCAmelCase_							)





