



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"""








from graphs.minimum_spanning_tree_kruskal import kruskal

def 	SCREAMING_SNAKE_CASE_							(	)  ->     Tuple:
   _A      =       9
   _A      =       [
       [0, 1, 4],
       [0, 7, 8],
       [1, 2, 8],
       [7, 8, 7],
       [7, 6, 1],
       [2, 8, 2],
       [8, 6, 6],
       [2, 3, 7],
       [2, 5, 4],
       [6, 5, 2],
       [3, 5, 14],
       [3, 4, 9],
       [5, 4, 10],
       [1, 7, 11],
   ]

   _A      =       kruskal(_snake_case	,   _snake_case				)

   _A      =       [
       [7, 6, 1],
       [2, 8, 2],
       [6, 5, 2],
       [0, 1, 4],
       [2, 5, 4],
       [2, 3, 7],
       [0, 7, 8],
       [3, 4, 9],
   ]

   assert sorted(_snake_case				) == sorted(_snake_case				)



