





import numpy



class 							_UpperCamelCase					:

       def __init__(		self: Tuple					,   _SCREAMING_SNAKE_CASE: numpy.ndarray					,   _SCREAMING_SNAKE_CASE: numpy.ndarray		)       -> None:




              """simple docstring"""


              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 lowercase (		self: Union[str, Any]		)       -> numpy.ndarray:




              """simple docstring"""


              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 lowercase (		self: Dict		)       -> None:




              """simple docstring"""


              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 lowercase (		self: Optional[int]					,   _SCREAMING_SNAKE_CASE: numpy.ndarray					,   _SCREAMING_SNAKE_CASE: int					,   _SCREAMING_SNAKE_CASE: bool		)       -> None:




              """simple docstring"""


              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 lowercase (		self: str					,   _SCREAMING_SNAKE_CASE: numpy.ndarray		)       -> int:




              """simple docstring"""


              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  lowerCAmelCase_ (							UpperCamelCase_  )     ->		numpy.ndarray:
       return 1 / (1 + numpy.exp(-value  ))




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		numpy.ndarray:
       return (value) * (1 - (value))




def  lowerCAmelCase_ (							)     ->		int:
       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=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 numpy as np

# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models

if __name__ == "__main__":
     # Initialising the CNN
     # (Sequential- Building the model layer by layer)
     _UpperCAmelCase        =				models.Sequential()

     # Step 1 - Convolution
     # Here 64,64 is the length & breadth of dataset images and 3 is for the RGB channel
     # (3,3) is the kernel size (filter matrix)
     classifier.add(
         layers.ConvaD(3_2, (3, 3), input_shape=(6_4, 6_4, 3), activation='relu')
     )

     # Step 2 - Pooling
     classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))

     # Adding a second convolutional layer
     classifier.add(layers.ConvaD(3_2, (3, 3), activation='relu'))
     classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))

     # Step 3 - Flattening
     classifier.add(layers.Flatten())

     # Step 4 - Full connection
     classifier.add(layers.Dense(units=1_2_8, activation='relu'))
     classifier.add(layers.Dense(units=1, activation='sigmoid'))

     # Compiling the CNN
     classifier.compile(
         optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']
     )

     # Part 2 - Fitting the CNN to the images

     # Load Trained model weights

     # from keras.models import load_model
     # regressor=load_model('cnn.h5')

     _UpperCAmelCase        =				tf.keras.preprocessing.image.ImageDataGenerator(
         rescale=1.0 / 2_5_5, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
     )

     _UpperCAmelCase        =				tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 2_5_5)

     _UpperCAmelCase        =				train_datagen.flow_from_directory(
         'dataset/training_set', target_size=(6_4, 6_4), batch_size=3_2, class_mode='binary'
     )

     _UpperCAmelCase        =				test_datagen.flow_from_directory(
         'dataset/test_set', target_size=(6_4, 6_4), batch_size=3_2, class_mode='binary'
     )

     classifier.fit_generator(
         training_set, steps_per_epoch=5, epochs=3_0, validation_data=test_set
     )

     classifier.save('cnn.h5')

     # Part 3 - Making new predictions

     _UpperCAmelCase        =				tf.keras.preprocessing.image.load_img(
         'dataset/single_prediction/image.png', target_size=(6_4, 6_4)
     )
     _UpperCAmelCase        =				tf.keras.preprocessing.image.img_to_array(test_image)
     _UpperCAmelCase        =				np.expand_dims(test_image, axis=0)
     _UpperCAmelCase        =				classifier.predict(test_image)
     # training_set.class_indices
     if result[0][0] == 0:
          _UpperCAmelCase        =				'Normal'
     if result[0][0] == 1:
          _UpperCAmelCase        =				'Abnormality detected'

