




"""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(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"""












"""simple docstring"""






def 							UpperCAmelCase		(		snake_case			:					int      ,       snake_case			:					int    ):
					return x if y == 0 else greatest_common_divisor(snake_case      ,       x % y    )





def 							UpperCAmelCase		(		snake_case			:					int      ,       snake_case			:					int    ):
					return (x * y) // greatest_common_divisor(snake_case      ,       snake_case    )





def 							UpperCAmelCase		(		snake_case			:					int = 20    ):
					_lowerCAmelCase:List[Any]									=  1
					for i in range(1      ,       n + 1    ):
										_lowerCAmelCase:List[str]									=  lcm(snake_case      ,       snake_case    )
					return g


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






