


'''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				:  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=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				:  str      					=							tf.keras.preprocessing.image.ImageDataGenerator(
           rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
       )

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



def      snake_case_						( __lowercase       ):
   return " ".join(
       ''''''.join(word[::-1]       ) if len(__lowercase       ) > 4 else word for word in sentence.split()       )


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       print(reverse_long_words('Hey wollef sroirraw'))