






"""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=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')

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

				_lowerCAmelCase            =	tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 2_55)

				_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 dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Sequence, Value
from .base import TaskTemplate





@dataclass(frozen=lowerCAmelCase      )
class 					_A		(			lowerCAmelCase      ):
      # `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
      snake_case__     :  str				    =      field(default='question-answering-extractive'    ,       metadata={'include_in_asdict_even_if_is_default': True}      )
      snake_case__     :  ClassVar[Features]				    =      Features({'question': Value('string'      ), 'context': Value('string'      )}      )
      snake_case__     :  ClassVar[Features]				    =      Features(
          {
              'answers': Sequence(
                  {
                      'text': Value('string'      ),
                      'answer_start': Value('int32'      ),
                  }      )
          }      )
      snake_case__     :  str				    =      "question"
      snake_case__     :  str				    =      "context"
      snake_case__     :  str				    =      "answers"

      @property
      def 							A__			(		self		):







            """simple docstring"""



            return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}



