





'''simple docstring'''



from typing import Any






class 							__lowercase       :







	'''simple docstring'''

	def __init__(self     ,_lowerCamelCase )							->							Optional[Any]:







				'''simple docstring'''





				__lowercase										=							data
				__lowercase										=							None






class 							__lowercase       :







	'''simple docstring'''

	def __init__(self )							->							Dict:







				'''simple docstring'''





				__lowercase										=							None


	def 						_UpperCAmelCase     (self )							->							Dict:







				'''simple docstring'''





				__lowercase										=							self.head
				while temp is not None:
							print(temp.data     ,end=''' ''' )
							__lowercase										=							temp.next
				print()


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							Any:







				'''simple docstring'''





				__lowercase										=							Node(_lowerCamelCase )
				__lowercase										=							self.head
				__lowercase										=							new_node


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase )							->							Tuple:







				'''simple docstring'''





				if node_data_a == node_data_a:
							return
				else:
							__lowercase										=							self.head
							while node_a is not None and node_a.data != node_data_a:
										__lowercase										=							node_a.next

							__lowercase										=							self.head
							while node_a is not None and node_a.data != node_data_a:
										__lowercase										=							node_a.next

							if node_a is None or node_a is None:
										return

							__lowercase						,		__lowercase										=							node_a.data, node_a.data


if __name__ == "__main__":
	_SCREAMING_SNAKE_CASE     :   Dict					=    LinkedList()
	for i in range(5, 0, -1):
		ll.push(i)

	ll.print_list()

	ll.swap_nodes(1, 4)
	print('''After swapping''')
	ll.print_list()












'''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)
	_SCREAMING_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(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')

	_SCREAMING_SNAKE_CASE					=    tf.keras.preprocessing.image.ImageDataGenerator(
	    rescale=1.0 / 2_5_5, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
	)

	_SCREAMING_SNAKE_CASE					=    tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 2_5_5)

	_SCREAMING_SNAKE_CASE					=    train_datagen.flow_from_directory(
	    '''dataset/training_set''', target_size=(6_4, 6_4), batch_size=3_2, class_mode='''binary'''
	)

	_SCREAMING_SNAKE_CASE					=    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

	_SCREAMING_SNAKE_CASE					=    tf.keras.preprocessing.image.load_img(
	    '''dataset/single_prediction/image.png''', target_size=(6_4, 6_4)
	)
	_SCREAMING_SNAKE_CASE					=    tf.keras.preprocessing.image.img_to_array(test_image)
	_SCREAMING_SNAKE_CASE					=    np.expand_dims(test_image, axis=0)
	_SCREAMING_SNAKE_CASE					=    classifier.predict(test_image)
	# training_set.class_indices
	if result[0][0] == 0:
		_SCREAMING_SNAKE_CASE					=    '''Normal'''
	if result[0][0] == 1:
		_SCREAMING_SNAKE_CASE					=    '''Abnormality detected'''





