def __init__(self,
data,
model_export_format,
model_spec,
shuffle=True,
train_whole_model=False,
validation_ratio=0.1,
test_ratio=0.1,
hparams=lib.get_default_hparams()):
"""Init function for ImageClassifier class.
Including splitting the raw input data into train/eval/test sets and
selecting the exact NN model to be used.
Args:
data: Raw data that could be splitted for training / validation / testing.
model_export_format: Model export format such as saved_model / tflite.
model_spec: Specification for the model.
shuffle: Whether the data should be shuffled.
train_whole_model: If true, the Hub module is trained together with the
classification layer on top. Otherwise, only train the top
classification layer.
validation_ratio: The ratio of validation data to be splitted.
test_ratio: The ratio of test data to be splitted.
hparams: A namedtuple of hyperparameters. This function expects
.dropout_rate: The fraction of the input units to drop, used in dropout
layer.
"""
super(ImageClassifier,
self).__init__(data, model_export_format, model_spec, shuffle,
train_whole_model, validation_ratio, test_ratio)
# Gets pre_trained models.
if model_export_format != mef.ModelExportFormat.TFLITE:
raise ValueError('Model export mode %s is not supported currently.' %
str(model_export_format))
self.pre_trained_model_spec = model_spec
# Generates training, validation and testing data.
if validation_ratio + test_ratio >= 1.0:
raise ValueError(
'The total ratio for validation and test data should be less than 1.0.'
)
self.validation_data, rest_data = data.split(
validation_ratio, shuffle=shuffle)
self.test_data, self.train_data = rest_data.split(
test_ratio, shuffle=shuffle)
# Checks dataset parameter.
if self.train_data.size == 0:
raise ValueError('Training dataset is empty.')
# Creates the classifier model for retraining.
module_layer = hub.KerasLayer(
self.pre_trained_model_spec.uri, trainable=train_whole_model)
self.model = lib.build_model(module_layer, hparams,
self.pre_trained_model_spec.input_image_shape,
data.num_classes)
def _create_model(self, hparams=None):
"""Creates the classifier model for retraining."""
hparams = self._get_hparams_or_default(hparams)
module_layer = hub_loader.HubKerasLayerV1V2(
self.model_spec.uri, trainable=hparams.do_fine_tuning)
return lib.build_model(module_layer, hparams,
self.model_spec.input_image_shape, self.num_classes)
def _create_model(self, hparams=None):
"""Creates the classifier model for retraining."""
if hparams is None:
hparams = self.hparams
module_layer = hub.KerasLayer(
self.model_spec.uri, trainable=hparams.do_fine_tuning)
return lib.build_model(module_layer, hparams,
self.model_spec.input_image_shape,
self.data.num_classes)
def create_model(self, hparams=None, with_loss_and_metrics=False):
"""Creates the classifier model for retraining."""
hparams = self._get_hparams_or_default(hparams)
module_layer = hub_loader.HubKerasLayerV1V2(
self.model_spec.uri, trainable=hparams.do_fine_tuning)
self.model = hub_lib.build_model(module_layer, hparams,
self.model_spec.input_image_shape,
self.num_classes)
if with_loss_and_metrics:
# Adds loss and metrics in the keras model.
self.model.compile(loss=tf.keras.losses.CategoricalCrossentropy(
label_smoothing=0.1),
metrics=['accuracy'])
