



"""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 typing import TYPE_CHECKING

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


lowerCamelCase      =		{
    """configuration_perceiver""": ["""PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PerceiverConfig""", """PerceiverOnnxConfig"""],
    """tokenization_perceiver""": ["""PerceiverTokenizer"""],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase      =		["""PerceiverFeatureExtractor"""]
  lowerCamelCase      =		["""PerceiverImageProcessor"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase      =		[
      """PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """PerceiverForImageClassificationConvProcessing""",
      """PerceiverForImageClassificationFourier""",
      """PerceiverForImageClassificationLearned""",
      """PerceiverForMaskedLM""",
      """PerceiverForMultimodalAutoencoding""",
      """PerceiverForOpticalFlow""",
      """PerceiverForSequenceClassification""",
      """PerceiverLayer""",
      """PerceiverModel""",
      """PerceiverPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
  from .tokenization_perceiver import PerceiverTokenizer

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_perceiver import PerceiverFeatureExtractor
    from .image_processing_perceiver import PerceiverImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_perceiver import (
        PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
        PerceiverForImageClassificationConvProcessing,
        PerceiverForImageClassificationFourier,
        PerceiverForImageClassificationLearned,
        PerceiverForMaskedLM,
        PerceiverForMultimodalAutoencoding,
        PerceiverForOpticalFlow,
        PerceiverForSequenceClassification,
        PerceiverLayer,
        PerceiverModel,
        PerceiverPreTrainedModel,
    )

else:
  import sys

  lowerCamelCase      =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




