"""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     :		Optional[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=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')

       _UpperCamelCase     :		Optional[int]											=					tf.keras.preprocessing.image.ImageDataGenerator(
           rescale=1.0 / 2_55, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
       )

       _UpperCamelCase     :		Union[str, Any]											=					tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 2_55)

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


"""simple docstring"""





from collections.abc import Sequence





def        a_  (   _lowerCAmelCase						:				Sequence[float]     ,				_lowerCAmelCase						:				float  ):





       '''simple docstring'''

       return sum(c * (x**i) for i, c in enumerate(_lowerCAmelCase  )  )





def        a_  (   _lowerCAmelCase						:				Sequence[float]     ,				_lowerCAmelCase						:				float  ):





       '''simple docstring'''

       lowercase__    :	int     					=			0.0
       for coeff in reversed(_lowerCAmelCase  ):
              lowercase__    :	List[Any]     					=			result * x + coeff
       return result


if __name__ == "__main__":
       _UpperCamelCase     :		int											=					(0.0, 0.0, 5.0, 9.3, 7.0)
       _UpperCamelCase     :		Dict											=					1_0.0
       print(evaluate_poly(poly, x))
       print(horner(poly, x))

