
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)
   lowercase_ 				=			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')

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

   lowercase_ 				=			tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

   lowercase_ 				=			train_datagen.flow_from_directory(
       """dataset/training_set""", target_size=(64, 64), batch_size=32, class_mode="""binary"""
   )

   lowercase_ 				=			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

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




"""simple docstring"""


import baseaa


def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




			return baseaa.baaencode(string.encode("utf-8" ) )


def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




			return baseaa.baadecode(_UpperCamelCase ).decode("utf-8" )


if __name__ == "__main__":
	A	:  List[str]            =     "Hello World!"
	A	:  Any            =     baseaa_encode(test)
	print(encoded)

	A	:  List[Any]            =     baseaa_decode(encoded)
	print(decoded)




