
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"""



from __future__ import annotations

import matplotlib.pyplot as plt  # type: ignore
import numpy

# initial triangle of Koch snowflake
a__												=						numpy.array([0, 0])
a__												=						numpy.array([0.5, 0.8660254])
a__												=						numpy.array([1, 0])
a__												=						[VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]



def   __UpperCAmelCase					(      __a :     list[numpy.ndarray]						,__a :     int      )     ->	list[numpy.ndarray]:

					"""simple docstring"""

					_a      :       Tuple   =						initial_vectors
					for _ in range(__a      ):
										_a      :       int   =						iteration_step(__a      )
					return vectors



def   __UpperCAmelCase					(      __a :     list[numpy.ndarray]      )     ->	list[numpy.ndarray]:

					"""simple docstring"""

					_a      :       Tuple   =						[]
					for i, start_vector in enumerate(vectors[:-1]      ):
										_a      :       str   =						vectors[i + 1]
										new_vectors.append(__a      )
										_a      :       Optional[int]   =						end_vector - start_vector
										new_vectors.append(start_vector + difference_vector / 3      )
										new_vectors.append(
										    start_vector + difference_vector / 3 + rotate(difference_vector / 3						,60      )      )
										new_vectors.append(start_vector + difference_vector * 2 / 3      )
					new_vectors.append(vectors[-1]      )
					return new_vectors



def   __UpperCAmelCase					(      __a :     numpy.ndarray						,__a :     float      )     ->	numpy.ndarray:

					"""simple docstring"""

					_a      :       Tuple   =						numpy.radians(__a      )
					_a ,   _a      :       List[Any]   =						numpy.cos(__a      ), numpy.sin(__a      )
					_a      :       Dict   =						numpy.array(((c, -s), (s, c))      )
					return numpy.dot(__a						,__a      )



def   __UpperCAmelCase					(      __a :     list[numpy.ndarray]      )     ->	None:

					"""simple docstring"""

					_a      :       str   =						plt.gca()
					axes.set_aspect('''equal'''      )

					# matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
					# y-coordinates as inputs, which are constructed from the vector-list using
					# zip()
					_a ,   _a      :       Optional[int]   =						zip(*__a      )
					plt.plot(__a						,__a      )
					plt.show()


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			a__												=						iterate(INITIAL_VECTORS, 5)
			plot(processed_vectors)



