





from __future__ import annotations

from decimal import Decimal

from numpy import array






def 	lowerCamelCase_		(							_UpperCamelCase					)						->		list[list[float]]:






				"""simple docstring"""


				snake_case_		:       Tuple           =    Decimal

				# Check if the provided matrix has 2 rows and 2 columns
				# since this implementation only works for 2x2 matrices
				if len(_UpperCamelCase					) == 2 and len(matrix[0]					) == 2 and len(matrix[1]					) == 2:
								# Calculate the determinant of the matrix
								snake_case_		:       Any           =    float(
								    d(matrix[0][0]					) * d(matrix[1][1]					) - d(matrix[1][0]					) * d(matrix[0][1]					)					)
								if determinant == 0:
												raise ValueError('''This matrix has no inverse.'''					)

								# Creates a copy of the matrix with swapped positions of the elements
								snake_case_		:       List[str]           =    [[0.0, 0.0], [0.0, 0.0]]
								snake_case_     ,				snake_case_		:       Tuple           =    matrix[1][1], matrix[0][0]
								snake_case_     ,				snake_case_		:       Optional[Any]           =    -matrix[1][0], -matrix[0][1]

								# Calculate the inverse of the matrix
								return [
								    [(float(d(_UpperCamelCase					)					) / determinant) or 0.0 for n in row] for row in swapped_matrix
								]
				elif (
				    len(_UpperCamelCase					) == 3
				    and len(matrix[0]					) == 3
				    and len(matrix[1]					) == 3
				    and len(matrix[2]					) == 3
				):
								# Calculate the determinant of the matrix using Sarrus rule
								snake_case_		:       Tuple           =    float(
								    (
								        (d(matrix[0][0]					) * d(matrix[1][1]					) * d(matrix[2][2]					))
								        + (d(matrix[0][1]					) * d(matrix[1][2]					) * d(matrix[2][0]					))
								        + (d(matrix[0][2]					) * d(matrix[1][0]					) * d(matrix[2][1]					))
								    )
								    - (
								        (d(matrix[0][2]					) * d(matrix[1][1]					) * d(matrix[2][0]					))
								        + (d(matrix[0][1]					) * d(matrix[1][0]					) * d(matrix[2][2]					))
								        + (d(matrix[0][0]					) * d(matrix[1][2]					) * d(matrix[2][1]					))
								    )					)
								if determinant == 0:
												raise ValueError('''This matrix has no inverse.'''					)

								# Creating cofactor matrix
								snake_case_		:       List[Any]           =    [
								    [d(0.0					), d(0.0					), d(0.0					)],
								    [d(0.0					), d(0.0					), d(0.0					)],
								    [d(0.0					), d(0.0					), d(0.0					)],
								]
								snake_case_		:       Optional[Any]           =    (d(matrix[1][1]					) * d(matrix[2][2]					)) - (
								    d(matrix[1][2]					) * d(matrix[2][1]					)
								)
								snake_case_		:       Optional[Any]           =    -(
								    (d(matrix[1][0]					) * d(matrix[2][2]					)) - (d(matrix[1][2]					) * d(matrix[2][0]					))
								)
								snake_case_		:       Any           =    (d(matrix[1][0]					) * d(matrix[2][1]					)) - (
								    d(matrix[1][1]					) * d(matrix[2][0]					)
								)
								snake_case_		:       str           =    -(
								    (d(matrix[0][1]					) * d(matrix[2][2]					)) - (d(matrix[0][2]					) * d(matrix[2][1]					))
								)
								snake_case_		:       List[str]           =    (d(matrix[0][0]					) * d(matrix[2][2]					)) - (
								    d(matrix[0][2]					) * d(matrix[2][0]					)
								)
								snake_case_		:       Any           =    -(
								    (d(matrix[0][0]					) * d(matrix[2][1]					)) - (d(matrix[0][1]					) * d(matrix[2][0]					))
								)
								snake_case_		:       str           =    (d(matrix[0][1]					) * d(matrix[1][2]					)) - (
								    d(matrix[0][2]					) * d(matrix[1][1]					)
								)
								snake_case_		:       Optional[Any]           =    -(
								    (d(matrix[0][0]					) * d(matrix[1][2]					)) - (d(matrix[0][2]					) * d(matrix[1][0]					))
								)
								snake_case_		:       int           =    (d(matrix[0][0]					) * d(matrix[1][1]					)) - (
								    d(matrix[0][1]					) * d(matrix[1][0]					)
								)

								# Transpose the cofactor matrix (Adjoint matrix)
								snake_case_		:       Tuple           =    array(_UpperCamelCase					)
								for i in range(3					):
												for j in range(3					):
																snake_case_		:       Tuple           =    cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
								snake_case_		:       Any           =    array(_UpperCamelCase					)
								for i in range(3					):
												for j in range(3					):
																inverse_matrix[i][j] /= d(_UpperCamelCase					)

        # Calculate the inverse of the matrix
								return [[float(d(_UpperCamelCase					)					) or 0.0 for n in row] for row in inverse_matrix]
				raise ValueError('''Please provide a matrix of size 2x2 or 3x3.'''					)








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