






"""simple docstring"""







import numpy







class  lowerCamelCase__ :






    def __init__(			self     ,A     ,A ):
          UpperCAmelCase          =	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.
          UpperCAmelCase          =	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.
          UpperCAmelCase          =	numpy.random.rand(
              4     ,3 )

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

          # Real output values provided.
          UpperCAmelCase          =	output_array

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






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






    def     _UpperCamelCase							(			self     ,A ):
          UpperCAmelCase          =	input_arr

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

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

          UpperCAmelCase          =	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			(		_snake_case							):







      """simple docstring"""






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

def _a			(		_snake_case							):







      """simple docstring"""






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

def _a			(		):







      """simple docstring"""






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

      # Calling neural network class.
      UpperCAmelCase          =	TwoHiddenLayerNeuralNetwork(
          input_array=_snake_case				,	output_array=_snake_case							)

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

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


if __name__ == "__main__":
  example()











"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_UpperCamelCase					      =  {"""configuration_glpn""": ["""GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GLPNConfig"""]}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  ["""GLPNFeatureExtractor"""]
  _UpperCamelCase					      =  ["""GLPNImageProcessor"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  [
      """GLPN_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """GLPNForDepthEstimation""",
      """GLPNLayer""",
      """GLPNModel""",
      """GLPNPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_glpn import GLPNFeatureExtractor
    from .image_processing_glpn import GLPNImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_glpn import (
        GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
        GLPNForDepthEstimation,
        GLPNLayer,
        GLPNModel,
        GLPNPreTrainedModel,
    )


else:
  import sys

  _UpperCamelCase					      =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



