



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__      :				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')

       a__      :				str   	=			tf.keras.preprocessing.image.ImageDataGenerator(
           rescale=1.0 / 2_55, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
       )

       a__      :				List[str]   	=			tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 2_55)

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





def   _lowerCAmelCase   (						A__						,  A__						,  A__	):
      if principal <= 0:
            raise Exception('Principal borrowed must be > 0'	)
      if rate_per_annum < 0:
            raise Exception('Rate of interest must be >= 0'	)
      if years_to_repay <= 0 or not isinstance(A__						,  A__	):
            raise Exception('Years to repay must be an integer > 0'	)

      # Yearly rate is divided by 12 to get monthly rate
      lowercase__     =  rate_per_annum / 12

      # Years to repay is multiplied by 12 to get number of payments as payment is monthly
      lowercase__     =  years_to_repay * 12

      return (
          principal
          * rate_per_month
          * (1 + rate_per_month) ** number_of_payments
          / ((1 + rate_per_month) ** number_of_payments - 1)
      )


if __name__ == "__main__":
       import doctest

       doctest.testmod()
