





'''simple docstring'''





import copy
import re



class 							_UpperCamelCase					:
       _UpperCamelCase				:					Dict         =   '''hp'''
       _UpperCamelCase				:					Dict         =   {}
       _UpperCamelCase				:					Any         =   None

       @classmethod
       def lowercase (		cls: int					,   _SCREAMING_SNAKE_CASE: Union[str, Any]					,   _SCREAMING_SNAKE_CASE: str		)       -> Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_          =  prefix
              UpperCamelCase_          =  defaults
              cls.build_naming_info()

       @staticmethod
       def lowercase (		_SCREAMING_SNAKE_CASE: Optional[int]					,   _SCREAMING_SNAKE_CASE: List[str]		)       -> List[Any]:




              """simple docstring"""


              if len(_SCREAMING_SNAKE_CASE		) == 0:
                     return ""
              UpperCamelCase_          =  None
              if any(char.isdigit() for char in word		):
                     raise Exception(f'''Parameters should not contain numbers: \'{word}\' contains a number'''		)
              if word in info["short_word"]:
                     return info["short_word"][word]
              for prefix_len in range(1					,   len(_SCREAMING_SNAKE_CASE		) + 1		):
                     UpperCamelCase_          =  word[:prefix_len]
                     if prefix in info["reverse_short_word"]:
                            continue
                     else:
                            UpperCamelCase_          =  prefix
                            break

              if short_word is None:
                     # Paranoid fallback
                     def int_to_alphabetic(_SCREAMING_SNAKE_CASE: Optional[Any]		):
                            UpperCamelCase_          =  ""
                            while integer != 0:
                                   UpperCamelCase_          =  chr(ord("A"		) + integer % 10		) + s
                                   integer //= 10
                            return s

                     UpperCamelCase_          =  0
                     while True:
                            UpperCamelCase_          =  word + "#" + int_to_alphabetic(_SCREAMING_SNAKE_CASE		)
                            if sword in info["reverse_short_word"]:
                                   continue
                            else:
                                   UpperCamelCase_          =  sword
                                   break

              UpperCamelCase_          =  short_word
              UpperCamelCase_          =  word
              return short_word

       @staticmethod
       def lowercase (		_SCREAMING_SNAKE_CASE: int					,   _SCREAMING_SNAKE_CASE: str		)       -> Optional[int]:




              """simple docstring"""


              UpperCamelCase_          =  param_name.split("_"		)

              UpperCamelCase_          =  [TrialShortNamer.shortname_for_word(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		) for word in words]

              # We try to create a separatorless short name, but if there is a collision we have to fallback
              # to a separated short name
              UpperCamelCase_          =  ["", "_"]

              for separator in separators:
                     UpperCamelCase_          =  separator.join(_SCREAMING_SNAKE_CASE		)
                     if shortname not in info["reverse_short_param"]:
                            UpperCamelCase_          =  shortname
                            UpperCamelCase_          =  param_name
                            return shortname

              return param_name

       @staticmethod
       def lowercase (		_SCREAMING_SNAKE_CASE: Union[str, Any]					,   _SCREAMING_SNAKE_CASE: Union[str, Any]		)       -> Dict:




              """simple docstring"""


              UpperCamelCase_          =  TrialShortNamer.shortname_for_key(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  short_name
              UpperCamelCase_          =  param_name

       @classmethod
       def lowercase (		cls: int		)       -> List[str]:




              """simple docstring"""


              if cls.NAMING_INFO is not None:
                     return

              UpperCamelCase_          =  {
                  "short_word": {},
                  "reverse_short_word": {},
                  "short_param": {},
                  "reverse_short_param": {},
              }

              UpperCamelCase_          =  list(cls.DEFAULTS.keys()		)

              for k in field_keys:
                     cls.add_new_param_name(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  info

       @classmethod
       def lowercase (		cls: int					,   _SCREAMING_SNAKE_CASE: Optional[Any]		)       -> Union[str, Any]:




              """simple docstring"""


              cls.build_naming_info()
              assert cls.PREFIX is not None
              UpperCamelCase_          =  [copy.copy(cls.PREFIX		)]

              for k, v in params.items():
                     if k not in cls.DEFAULTS:
                            raise Exception(f'''You should provide a default value for the param name {k} with value {v}'''		)
                     if v == cls.DEFAULTS[k]:
                            # The default value is not added to the name
                            continue

                     UpperCamelCase_          =  cls.NAMING_INFO["short_param"][k]

                     if isinstance(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		):
                            UpperCamelCase_          =  1 if v else 0

                     UpperCamelCase_          =  "" if isinstance(_SCREAMING_SNAKE_CASE					,   (int, float)		) else "-"
                     UpperCamelCase_          =  f'''{key}{sep}{v}'''
                     name.append(_SCREAMING_SNAKE_CASE		)

              return "_".join(_SCREAMING_SNAKE_CASE		)



       @classmethod
       def lowercase (		cls: str					,   _SCREAMING_SNAKE_CASE: int		)       -> List[Any]:




              """simple docstring"""


              UpperCamelCase_          =  repr[len(cls.PREFIX		) + 1 :]
              if repr == "":
                     UpperCamelCase_          =  []
              else:
                     UpperCamelCase_          =  repr.split("_"		)

              UpperCamelCase_          =  {}

              for value in values:
                     if "-" in value:
                            UpperCamelCase_          =  value.split("-"		)
                     else:
                            UpperCamelCase_          =  re.sub("[0-9.]"					,   ""					,   _SCREAMING_SNAKE_CASE		)
                            UpperCamelCase_          =  float(re.sub("[^0-9.]"					,   ""					,   _SCREAMING_SNAKE_CASE		)		)

                     UpperCamelCase_          =  cls.NAMING_INFO["reverse_short_param"][p_k]

                     UpperCamelCase_          =  p_v

              for k in cls.DEFAULTS:
                     if k not in parameters:
                            UpperCamelCase_          =  cls.DEFAULTS[k]

              return parameters








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)
     _UpperCAmelCase        =				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')

     _UpperCAmelCase        =				tf.keras.preprocessing.image.ImageDataGenerator(
         rescale=1.0 / 2_5_5, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
     )

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

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

     _UpperCAmelCase        =				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

     _UpperCAmelCase        =				tf.keras.preprocessing.image.load_img(
         'dataset/single_prediction/image.png', target_size=(6_4, 6_4)
     )
     _UpperCAmelCase        =				tf.keras.preprocessing.image.img_to_array(test_image)
     _UpperCAmelCase        =				np.expand_dims(test_image, axis=0)
     _UpperCAmelCase        =				classifier.predict(test_image)
     # training_set.class_indices
     if result[0][0] == 0:
          _UpperCAmelCase        =				'Normal'
     if result[0][0] == 1:
          _UpperCAmelCase        =				'Abnormality detected'

