

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	:     Optional[Any]		       = 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')

					_UpperCAmelCase	:     Optional[int]		       = tf.keras.preprocessing.image.ImageDataGenerator(
					    rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
					)

					_UpperCAmelCase	:     Union[str, Any]		       = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

					_UpperCAmelCase	:     List[Any]		       = train_datagen.flow_from_directory(
					    """dataset/training_set""", target_size=(64, 64), batch_size=32, class_mode="""binary"""
					)

					_UpperCAmelCase	:     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

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







_UpperCAmelCase	:     str		       = [0, 2, 4, 6, 8]
_UpperCAmelCase	:     Any		       = [1, 3, 5, 7, 9]





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							if remaining_length == 0:
														if digits[0] == 0 or digits[-1] == 0:
																					return 0

														for i in range(length // 2 - 1     ,							-1     ,							-1   ):
																					remainder += digits[i] + digits[length - i - 1]

																					if remainder % 2 == 0:
																												return 0

																					remainder //= 10

														return 1

							if remaining_length == 1:
														if remainder % 2 == 0:
																					return 0

														snake_case_           =					0
														for digit in range(10   ):
																					snake_case_           =					digit
																					result += reversible_numbers(
																					    0     ,							(remainder + 2 * digit) // 10     ,							UpperCamelCase__     ,							UpperCamelCase__   )
														return result

							snake_case_           =					0
							for digita in range(10   ):
														snake_case_           =					digita

														if (remainder + digita) % 2 == 0:
																					snake_case_           =					ODD_DIGITS
														else:
																					snake_case_           =					EVEN_DIGITS

														for digita in other_parity_digits:
																					snake_case_           =					digita
																					result += reversible_numbers(
																					    remaining_length - 2     ,							(remainder + digita + digita) // 10     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							)
							return result





def       __lowerCamelCase			(				UpperCamelCase__ = 9   ):







							'''simple docstring'''






							snake_case_           =					0
							for length in range(1     ,							max_power + 1   ):
														result += reversible_numbers(UpperCamelCase__     ,							0     ,							[0] * length     ,							UpperCamelCase__   )
							return result


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




