





'''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)
  lowercase__       : Union[str, Any]         =       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=1_28, 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')

  lowercase__       : int         =       tf.keras.preprocessing.image.ImageDataGenerator(
      rescale=1.0 / 2_55, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
  )

  lowercase__       : Optional[Any]         =       tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 2_55)

  lowercase__       : Optional[Any]         =       train_datagen.flow_from_directory(
      '''dataset/training_set''', target_size=(64, 64), batch_size=32, class_mode='''binary'''
  )

  lowercase__       : List[str]         =       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

  lowercase__       : Optional[Any]         =       tf.keras.preprocessing.image.load_img(
      '''dataset/single_prediction/image.png''', target_size=(64, 64)
  )
  lowercase__       : Dict         =       tf.keras.preprocessing.image.img_to_array(test_image)
  lowercase__       : Dict         =       np.expand_dims(test_image, axis=0)
  lowercase__       : Tuple         =       classifier.predict(test_image)
  # training_set.class_indices
  if result[0][0] == 0:
    lowercase__       : Tuple         =       '''Normal'''
  if result[0][0] == 1:
    lowercase__       : Optional[Any]         =       '''Abnormality detected'''






'''simple docstring'''




from __future__ import annotations






def 			_lowerCAmelCase     (    __snake_case    :					list[int]					,				__snake_case    :					list[int]					,				__snake_case    :					int     )	->	tuple[float, list[float]]:
     __A		:					int						=			list(range(len(__snake_case     )     )     )
     __A		:					Optional[Any]						=			[v / w for v, w in zip(__snake_case					,				__snake_case     )]
     index.sort(key=lambda __snake_case     : ratio[i]					,				reverse=__snake_case     )

     __A		:					float						=			0
     __A		:					list[float]						=			[0] * len(__snake_case     )
     for i in index:
          if weight[i] <= capacity:
               __A		:					Optional[int]						=			1
               max_value += value[i]
               capacity -= weight[i]
          else:
               __A		:					List[Any]						=			capacity / weight[i]
               max_value += value[i] * capacity / weight[i]
               break

     return max_value, fractions


if __name__ == "__main__":
  import doctest

  doctest.testmod()