



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

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

							lowerCAmelCase_     :       Dict								=						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_     :       Any								=						tf.keras.preprocessing.image.img_to_array(test_image)
							lowerCAmelCase_     :       Tuple								=						np.expand_dims(test_image, axis=0)
							lowerCAmelCase_     :       Any								=						classifier.predict(test_image)
							# training_set.class_indices
							if result[0][0] == 0:
														lowerCAmelCase_     :       List[Any]								=						'Normal'
							if result[0][0] == 1:
														lowerCAmelCase_     :       str								=						'Abnormality detected'









'''simple docstring'''



import string
from math import logaa
def 					UpperCAmelCase   (						A  :				str					,		A  :				str				):
							SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			document.translate(
							    str.maketrans(''''''					,		''''''					,		string.punctuation				)				).replace('''\n'''					,		''''''				)
							SCREAMING_SNAKE_CASE					:      Tuple     					=			document_without_punctuation.split(''' '''				)  # word tokenization
							return len([word for word in tokenize_document if word.lower() == term.lower()]				)
def 					UpperCAmelCase   (						A  :				str					,		A  :				str				):
							SCREAMING_SNAKE_CASE					:      int     					=			corpus.lower().translate(
							    str.maketrans(''''''					,		''''''					,		string.punctuation				)				)  # strip all punctuation and replace it with ''
							SCREAMING_SNAKE_CASE					:      Any     					=			corpus_without_punctuation.split('''\n'''				)
							SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			term.lower()
							return (len([doc for doc in docs if term in doc]				), len(A				))
def 					UpperCAmelCase   (						A  :				int					,		A  :				int					,		A  :				str=False				):
							if smoothing:
														if n == 0:
																					raise ValueError('''log10(0) is undefined.'''				)
														return round(1 + logaa(n / (1 + df)				)					,		3				)

							if df == 0:
														raise ZeroDivisionError('''df must be > 0'''				)
							elif n == 0:
														raise ValueError('''log10(0) is undefined.'''				)
							return round(logaa(n / df				)					,		3				)






def 					UpperCAmelCase   (						A  :				int					,		A  :				int				):
							return round(tf * idf					,		3				)




