def start_validation(model_name, class_hash):
"""
:return:
"""
img_width = Constants.get_image_dimensions(model_name)
img_height = img_width
cph = PrintHelper()
validation_data_dir = os.path.dirname(os.path.realpath(__file__)) + test_data_dir + class_hash
base_validation_dir = os.path.dirname(os.path.realpath(__file__)) + test_data_dir
save_weights = os.path.dirname(
os.path.realpath(__file__)) + Constants.weights_directory_path + 'Weights#' + class_hash + '.h5'
save_model_path = os.path.dirname(
os.path.realpath(__file__)) + Constants.model_file_directory_path + 'Model_file#' + class_hash + '.h5'
batch_size = 5
number_of_files = ModelValidation.number_of_images(validation_data_dir)
cph.info_print('Loading Model')
model = load_model(save_model_path)
model.load_weights(save_weights)
models_result = SaveModelToMongo.validation_started(class_hash)
PrintHelper.info_print(' Model Loaded')
model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['accuracy'])
PrintHelper.info_print(' Model Compiled')
# prepare data augmentation configuration
PrintHelper.info_print('creating image data generator')
validation_generator = DataGeneratorUtility.validation_data_generator(img_height, img_width,
batch_size=batch_size,
validation_data_dir=validation_data_dir)
PrintHelper.info_print('Starting Evaluate Generator')
loss, accuracy = model.evaluate_generator(validation_generator,number_of_files)
PrintHelper.info_print('Loss: ', loss, ' Accuracy: ', accuracy)
shutil.rmtree(validation_data_dir)
SaveModelToMongo.validation_completed(class_hash=class_hash,
stats=cph.return_string('Accuracy: ', round(accuracy, 4), ' Loss: ',
round(loss, 4)),
models_result=models_result)
def __init__(self,
model_name,
images,
image_count,
model_hash=None,
base_model=None):
"""
:param model_name: name/hash of the model to predict/extract-features from
:param images: path for the images downlaoded
:param model_hash: hash saved in the database: required for loading weights and files
:param base_model: required only when a custom generated model is being evaluated, check what is the base model of the model being used
"""
self._images_path = images
self._model_name = model_name
size = Constants.get_image_dimensions(model_name)
self._target_size = (size, size)
self._image_count = image_count
self._model_path = None
self._model_weights = None
# only required for custom models, in which case base_model should be supplied
if base_model is not None and model_hash is not None:
try:
self._model_path = os.path.dirname(os.path.realpath(
__file__)) + Constants.saving_model_specific_file(
base_model
) + base_model + '----model_file' + model_hash + '.h5'
self._model_weights = os.path.dirname(
os.path.realpath(
__file__)) + Constants.saving_model_weights(
base_model
) + base_model + '----weights' + model_hash + '.h5'
except Exception as e:
PrintHelper.failure_print("model file creation", e)