


'''simple docstring'''

import numpy





class      snake_case    :





  """simple docstring"""




  def __init__(    self			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   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   snake_case		(    self     ):







   """simple docstring"""
   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   snake_case		(    self     ):







   """simple docstring"""
   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   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   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   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   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 __snake_case					(      UpperCAmelCase_						:						numpy.ndarray		):
 return 1 / (1 + numpy.exp(-value		))





def __snake_case					(      UpperCAmelCase_						:						numpy.ndarray		):
 return (value) * (1 - (value))





def __snake_case					(      ):
 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()








"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from argparse import ArgumentParser

from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser

def       _snake_case						(						):
 UpperCAmelCase	:					List[str]	=	ArgumentParser("""Accelerate CLI tool"""			,		usage="""accelerate <command> [<args>]"""			,		allow_abbrev=UpperCamelCase						)
 UpperCAmelCase	:					Dict	=	parser.add_subparsers(help="""accelerate command helpers"""						)

 # Register commands
 get_config_parser(subparsers=UpperCamelCase						)
 env_command_parser(subparsers=UpperCamelCase						)
 launch_command_parser(subparsers=UpperCamelCase						)
 tpu_command_parser(subparsers=UpperCamelCase						)
 test_command_parser(subparsers=UpperCamelCase						)

 # Let's go
 UpperCAmelCase	:					Optional[int]	=	parser.parse_args()

 if not hasattr(UpperCamelCase			,		"""func"""						):
  parser.print_help()
  exit(1						)

 # Run
 args.func(UpperCamelCase						)


if __name__ == "__main__":
      main()
