

"""simple docstring"""




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(32, (3, 3), input_shape=(64, 64, 3), activation='relu')
      )

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

      # Adding a second convolutional layer
      classifier.add(layers.ConvaD(32, (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=128, 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 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
      )

      __UpperCAmelCase			=					tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

      __UpperCAmelCase			=					train_datagen.flow_from_directory(
          'dataset/training_set', target_size=(64, 64), batch_size=32, class_mode='binary'
      )

      __UpperCAmelCase			=					test_datagen.flow_from_directory(
          'dataset/test_set', target_size=(64, 64), batch_size=32, class_mode='binary'
      )

      classifier.fit_generator(
          training_set, steps_per_epoch=5, epochs=30, 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=(64, 64)
      )
      __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'






'''simple docstring'''


import math

def   A_      (       SCREAMING_SNAKE_CASE_						)			->int:

  if not isinstance(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						):
    lowercase_     =       f"""Input value of [number={number}] must be an integer"""
    raise TypeError(SCREAMING_SNAKE_CASE_						)

  if number < 1:
    lowercase_     =       f"""Input value of [number={number}] must be > 0"""
    raise ValueError(SCREAMING_SNAKE_CASE_						)
  elif number == 1:
    return 3
  elif number == 2:
    return 5
  else:
    lowercase_     =       int(math.log(number // 3 ,	2						)						) + 2

    lowercase_     =       [3, 5]
    lowercase_     =       2
    lowercase_     =       3
    for block in range(1 ,	SCREAMING_SNAKE_CASE_						):
      for _ in range(SCREAMING_SNAKE_CASE_						):
        proth_list.append(2 ** (block + 1) + proth_list[proth_index - 1]						)
        proth_index += 1
      increment *= 2

  return proth_list[number - 1]


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       for number in range(11):
              __snake_case						  =		0
              try:
                     __snake_case						  =		proth(number)
              except ValueError:
                     print(f'''ValueError: there is no {number}th Proth number''')
                     continue

              print(f'''The {number}th Proth number: {value}''')

