


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(3_2, (3, 3), input_shape=(6_4, 6_4, 3), activation='relu')
       )

       # Step 2 - Pooling
       classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))

       # Adding a second convolutional layer
       classifier.add(layers.ConvaD(3_2, (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_2_8, 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 / 2_5_5, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
       )

       lowercase_			       =      tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 2_5_5)

       lowercase_			       =      train_datagen.flow_from_directory(
           'dataset/training_set', target_size=(6_4, 6_4), batch_size=3_2, class_mode='binary'
       )

       lowercase_			       =      test_datagen.flow_from_directory(
           'dataset/test_set', target_size=(6_4, 6_4), batch_size=3_2, class_mode='binary'
       )

       classifier.fit_generator(
           training_set, steps_per_epoch=5, epochs=3_0, 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=(6_4, 6_4)
       )
       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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


a__: str							   =						{
    'configuration_mega': ['MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegaConfig', 'MegaOnnxConfig'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__: Union[str, Any]							   =						[
           'MEGA_PRETRAINED_MODEL_ARCHIVE_LIST',
           'MegaForCausalLM',
           'MegaForMaskedLM',
           'MegaForMultipleChoice',
           'MegaForQuestionAnswering',
           'MegaForSequenceClassification',
           'MegaForTokenClassification',
           'MegaModel',
           'MegaPreTrainedModel',
       ]

if TYPE_CHECKING:
       from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mega import (
                  MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MegaForCausalLM,
                  MegaForMaskedLM,
                  MegaForMultipleChoice,
                  MegaForQuestionAnswering,
                  MegaForSequenceClassification,
                  MegaForTokenClassification,
                  MegaModel,
                  MegaPreTrainedModel,
              )

else:
       import sys

       a__: Union[str, Any]							   =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





