


'''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)
						lowerCamelCase__    	=     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')

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

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

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

						lowerCamelCase__    	=     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

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







'''simple docstring'''


from math import factorial






class 	lowerCAmelCase__			:




			def __init__(		self  :     int	,  lowerCamelCase__  :     Union[str, Any]	,  lowerCamelCase__  :     str )   ->Dict:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[Any]								=      real
					if isinstance(lowerCamelCase__	,  lowerCamelCase__ ):
							_UpperCAmelCase		:       Any								=      [1] * rank
					else:
							_UpperCAmelCase		:       Dict								=      rank




			def __repr__(		self  :     str )   ->List[str]:



					'''simple docstring'''







					return (
					    F"""{self.real}+"""
					    F"""{'+'.join(str(lowerCamelCase__ )+'E'+str(n+1 )for n,dual in enumerate(self.duals ) )}"""
					)




			def 							lowerCAmelCase__    (		self  :     Dict )   ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase		:       Union[str, Any]								=      self.duals.copy()
					while cur[-1] == 0:
							cur.pop(-1 )
					return Dual(self.real	,  lowerCamelCase__ )




			def __add__(		self  :     Dict	,  lowerCamelCase__  :     List[Any] )   ->Any:



					'''simple docstring'''







					if not isinstance(lowerCamelCase__	,  lowerCamelCase__ ):
							return Dual(self.real + other	,  self.duals )
					_UpperCAmelCase		:       Optional[int]								=      self.duals.copy()
					_UpperCAmelCase		:       Optional[int]								=      other.duals.copy()
					if len(lowerCamelCase__ ) > len(lowerCamelCase__ ):
							o_dual.extend([1] * (len(lowerCamelCase__ ) - len(lowerCamelCase__ )) )
					elif len(lowerCamelCase__ ) < len(lowerCamelCase__ ):
							s_dual.extend([1] * (len(lowerCamelCase__ ) - len(lowerCamelCase__ )) )
					_UpperCAmelCase		:       Union[str, Any]								=      []
					for i in range(len(lowerCamelCase__ ) ):
							new_duals.append(s_dual[i] + o_dual[i] )
					return Dual(self.real + other.real	,  lowerCamelCase__ )

			lowerCAmelCase :	Tuple          =     __add__




			def __sub__(		self  :     List[Any]	,  lowerCamelCase__  :     Union[str, Any] )   ->Dict:



					'''simple docstring'''







					return self + other * -1




			def __mul__(		self  :     List[str]	,  lowerCamelCase__  :     Optional[Any] )   ->Union[str, Any]:



					'''simple docstring'''







					if not isinstance(lowerCamelCase__	,  lowerCamelCase__ ):
							_UpperCAmelCase		:       Optional[int]								=      []
							for i in self.duals:
									new_duals.append(i * other )
							return Dual(self.real * other	,  lowerCamelCase__ )
					_UpperCAmelCase		:       Union[str, Any]								=      [0] * (len(self.duals ) + len(other.duals ) + 1)
					for i, item in enumerate(self.duals ):
							for j, jtem in enumerate(other.duals ):
									new_duals[i + j + 1] += item * jtem
					for k in range(len(self.duals ) ):
							new_duals[k] += self.duals[k] * other.real
					for index in range(len(other.duals ) ):
							new_duals[index] += other.duals[index] * self.real
					return Dual(self.real * other.real	,  lowerCamelCase__ )

			lowerCAmelCase :	Union[str, Any]          =     __mul__




			def __truediv__(		self  :     Optional[Any]	,  lowerCamelCase__  :     List[Any] )   ->Union[str, Any]:



					'''simple docstring'''







					if not isinstance(lowerCamelCase__	,  lowerCamelCase__ ):
							_UpperCAmelCase		:       Union[str, Any]								=      []
							for i in self.duals:
									new_duals.append(i / other )
							return Dual(self.real / other	,  lowerCamelCase__ )
					raise ValueError




			def __floordiv__(		self  :     str	,  lowerCamelCase__  :     str )   ->List[str]:



					'''simple docstring'''







					if not isinstance(lowerCamelCase__	,  lowerCamelCase__ ):
							_UpperCAmelCase		:       Tuple								=      []
							for i in self.duals:
									new_duals.append(i // other )
							return Dual(self.real // other	,  lowerCamelCase__ )
					raise ValueError






			def __pow__(		self  :     Tuple	,  lowerCamelCase__  :     Optional[Any] )   ->Optional[int]:



					'''simple docstring'''







					if n < 0 or isinstance(lowerCamelCase__	,  lowerCamelCase__ ):
							raise ValueError("power must be a positive integer" )
					if n == 0:
							return 1
					if n == 1:
							return self
					_UpperCAmelCase		:       str								=      self
					for _ in range(n - 1 ):
							x *= self
					return x






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase   ):
		if not callable(__lowerCAmelCase   ):
				raise ValueError("differentiate() requires a function as input for func"   )
		if not isinstance(__lowerCAmelCase      , (float, int)   ):
				raise ValueError("differentiate() requires a float as input for position"   )
		if not isinstance(__lowerCAmelCase      , __lowerCAmelCase   ):
				raise ValueError("differentiate() requires an int as input for order"   )
		_UpperCAmelCase		:       int								=      Dual(__lowerCAmelCase      , 1   )
		_UpperCAmelCase		:       Optional[int]								=      func(__lowerCAmelCase   )
		if order == 0:
				return result.real
		return result.duals[order - 1] * factorial(__lowerCAmelCase   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()






						def  __lowerCAmelCase (__lowerCAmelCase   ):
								return y**2 * y**4





						print(differentiate(f, 9, 2))



