






'''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)
     SCREAMING_SNAKE_CASE						=	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')

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

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

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

     SCREAMING_SNAKE_CASE						=	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

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











'''simple docstring'''



from __future__ import annotations

import math


def 							lowercase_				(    __A :       float					,      __A :       int			)       ->      float:



 """simple docstring"""




 lowercase					:   str				  =u
 for i in range(1					,      __A			):
  lowercase					:   Any				  =temp * (u - i)
 return temp


def 							lowercase_				(    )       ->      None:



 """simple docstring"""




 lowercase					:   List[str]				  =int(input('''enter the numbers of values: '''			)			)
 lowercase					:   list[list[float]]				  =[]
 for _ in range(__A			):
  y.append([]			)
 for i in range(__A			):
  for j in range(__A			):
   y[i].append(__A			)
   lowercase					:   List[Any]				  =0

 print('''enter the values of parameters in a list: '''			)
 lowercase					:   Optional[int]				  =list(map(__A					,      input().split()			)			)

 print('''enter the values of corresponding parameters: '''			)
 for i in range(__A			):
  lowercase					:   str				  =float(input()			)

 lowercase					:   int				  =int(input('''enter the value to interpolate: '''			)			)
 lowercase					:   Union[str, Any]				  =(value - x[0]) / (x[1] - x[0])

 # for calculating forward difference table

 for i in range(1					,      __A			):
  for j in range(n - i			):
   lowercase					:   str				  =y[j + 1][i - 1] - y[j][i - 1]

 lowercase					:   Any				  =y[0][0]
 for i in range(1					,      __A			):
  summ += (ucal(__A					,      __A			) * y[0][i]) / math.factorial(__A			)

 print(F'the value at {value} is {summ}'			)


if __name__ == "__main__":
      main()



