





'''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)
  __lowerCAmelCase							:     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=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')

  __lowerCAmelCase							:     Optional[Any]		     =tf.keras.preprocessing.image.ImageDataGenerator(
      rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
  )

  __lowerCAmelCase							:     Tuple		     =tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

  __lowerCAmelCase							:     Any		     =train_datagen.flow_from_directory(
      "dataset/training_set", target_size=(64, 64), batch_size=32, class_mode="binary"
  )

  __lowerCAmelCase							:     Dict		     =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

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










'''simple docstring'''

def 						UpperCamelCase    (							_lowerCamelCase : int = 1_00_00_00	):
    A__    =      set(range(3						,	_lowerCamelCase						,	2	)	)
    primes.add(2	)
    for p in range(3						,	_lowerCamelCase						,	2	):
        if p not in primes:
            continue
        primes.difference_update(set(range(p * p						,	_lowerCamelCase						,	_lowerCamelCase	)	)	)

    A__    =      [float(_lowerCamelCase	) for n in range(limit + 1	)]

    for p in primes:
        for n in range(_lowerCamelCase						,	limit + 1						,	_lowerCamelCase	):
            phi[n] *= 1 - 1 / p

    return int(sum(phi[2:]	)	)


if __name__ == "__main__":
  print(f"""{solution() = }""")



