def __init__(self, model_dir=None):
"""Programming language guesser.
``model_dir`` -- Guesslang machine learning model directory.
"""
model_data = model_info(model_dir)
#: `tensorflow` model directory
self.model_dir = model_data[0]
#: tells if current model is the default model
self.is_default = model_data[1]
#: supported languages with associated extensions
self.languages = config_dict('languages.json')
n_classes = len(self.languages)
feature_columns = [
tf.contrib.layers.real_valued_column('', dimension=CONTENT_SIZE)]
self._classifier = tf.contrib.learn.DNNLinearCombinedClassifier(
linear_feature_columns=feature_columns,
dnn_feature_columns=feature_columns,
dnn_hidden_units=_NEURAL_NETWORK_HIDDEN_LAYERS,
n_classes=n_classes,
linear_optimizer=tf.train.RMSPropOptimizer(_OPTIMIZER_STEP),
dnn_optimizer=tf.train.RMSPropOptimizer(_OPTIMIZER_STEP),
model_dir=self.model_dir)
def __init__(self, model_dir: Optional[str] = None) -> None:
model_data = model_info(model_dir)
#: `tensorflow` model directory
self.model_dir: str = model_data[0]
#: Tells if the current model is the default model
self.is_default: bool = model_data[1]
#: Supported languages associated with their extensions
self.languages: Dict[str, List[str]] = config_dict('languages.json')
n_classes = len(self.languages)
feature_columns = [
tf.contrib.layers.real_valued_column('', dimension=CONTENT_SIZE)
]
self._classifier = tf.contrib.learn.DNNLinearCombinedClassifier(
linear_feature_columns=feature_columns,
dnn_feature_columns=feature_columns,
dnn_hidden_units=NEURAL_NETWORK_HIDDEN_LAYERS,
n_classes=n_classes,
linear_optimizer=tf.train.RMSPropOptimizer(OPTIMIZER_STEP),
dnn_optimizer=tf.train.RMSPropOptimizer(OPTIMIZER_STEP),
model_dir=self.model_dir)