






'''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)
				a_		:						Dict             =    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=1_28, 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')

				a_		:						Union[str, Any]             =    tf.keras.preprocessing.image.ImageDataGenerator(
				    rescale=1.0 / 2_55, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
				)

				a_		:						str             =    tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 2_55)

				a_		:						int             =    train_datagen.flow_from_directory(
				    'dataset/training_set', target_size=(64, 64), batch_size=32, class_mode='binary'
				)

				a_		:						List[Any]             =    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

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












import unittest

from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script

@require_cpu
class 		_snake_case  (  unittest.TestCase					):






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
						debug_launcher(test_script.main)







			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Any:
						debug_launcher(test_ops.main)




