
'''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)
     snake_case									=    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')

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

     snake_case									=    tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 2_55)

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

     snake_case									=    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

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







'''simple docstring'''

def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   str     		=	len(lowerCamelCase_			)
 lowerCAmelCase__     :   Optional[Any]     		=	len(matrix[0]			)
 lowerCAmelCase__     :   Any     		=	min(lowerCamelCase_      ,			lowerCamelCase_			)

 for row in range(lowerCamelCase_			):
  # Check if diagonal element is not zero
  if matrix[row][row] != 0:
   # Eliminate all the elements below the diagonal
   for col in range(row + 1      ,			lowerCamelCase_			):
    lowerCAmelCase__     :   Tuple     		=	matrix[col][row] / matrix[row][row]
    for i in range(lowerCamelCase_      ,			lowerCamelCase_			):
     matrix[col][i] -= multiplier * matrix[row][i]
  else:
   # Find a non-zero diagonal element to swap rows
   lowerCAmelCase__     :   Dict     		=	True
   for i in range(row + 1      ,			lowerCamelCase_			):
    if matrix[i][row] != 0:
     lowerCAmelCase__		,     lowerCAmelCase__     :   Optional[Any]     		=	matrix[i], matrix[row]
     lowerCAmelCase__     :   Optional[Any]     		=	False
     break
   if reduce:
    rank -= 1
    for i in range(lowerCamelCase_			):
     lowerCAmelCase__     :   Union[str, Any]     		=	matrix[i][rank]

            # Reduce the row pointer by one to stay on the same row
   row -= 1

 return rank


if __name__ == "__main__":
     import doctest

     doctest.testmod()





