






import numpy



class 				_UpperCamelCase      :


      def __init__(						self       ,    __UpperCamelCase       ,    __UpperCamelCase						)->						Union[str, Any]:
           __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 			__UpperCAmelCase		(						self						)->						Optional[Any]:
           __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 			__UpperCAmelCase		(						self						)->						Optional[Any]:
           __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 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase						)->						Dict:







           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 			__UpperCAmelCase		(						self       ,    __UpperCamelCase						)->						int:
           __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    __lowerCAmelCase   (    __snake_case							):
     return 1 / (1 + numpy.exp(-value							))





def    __lowerCAmelCase   (    __snake_case							):
     return (value) * (1 - (value))





def    __lowerCAmelCase   (    ):
     __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       _lowercase							(					):
 __A	:		Dict	  =      ArgumentParser('Accelerate CLI tool',					usage='accelerate <command> [<args>]',					allow_abbrev=UpperCamelCase__      )
 __A	:		Any	  =      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
 __A	:		Optional[Any]	  =      parser.parse_args()

 if not hasattr(UpperCamelCase__,					'func'      ):
  parser.print_help()
  exit(1      )

 # Run
 args.func(UpperCamelCase__      )


if __name__ == "__main__":
 main()






