




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

		__lowerCAmelCase	:       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

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












__lowerCAmelCase	:       List[Any]     =	{str(digit): digit**5 for digit in range(10)}




def     __magic_name__       (				A	:      int     ):






			'''simple docstring'''







			return sum(DIGITS_FIFTH_POWER[digit] for digit in str(A     )     )




def     __magic_name__       (				):






			'''simple docstring'''







			return sum(
			    number
			    for number in range(1000,						1000000     )
			    if number == digits_fifth_powers_sum(A     )     )


if __name__ == "__main__":
		print(solution())






