recommender_wide_and_deep.py 文件源码

def __init__(self, model_type="wide+deep", verbose=None, name=None, tensorboard_verbose=3, 
                 wide_learning_rate=0.001, deep_learning_rate=0.001, checkpoints_dir=None):
        '''
        model_type = `str`: wide or deep or wide+deep
        verbose = `bool`
        name = `str` used for run_id (defaults to model_type)
        tensorboard_verbose = `int`: logging level for tensorboard (0, 1, 2, or 3)
        wide_learning_rate = `float`: defaults to 0.001
        deep_learning_rate = `float`: defaults to 0.001
        checkpoints_dir = `str`: where checkpoint files will be stored (defaults to "CHECKPOINTS")
        '''
        self.model_type = model_type or "wide+deep"
        assert self.model_type in self.AVAILABLE_MODELS
        self.verbose = verbose or 0
        self.tensorboard_verbose = tensorboard_verbose
        self.name = name or self.model_type # name is used for the run_id
        self.data_columns = COLUMNS
        self.continuous_columns = CONTINUOUS_COLUMNS
        self.categorical_columns = CATEGORICAL_COLUMNS  # dict with category_name: category_size
        self.label_column = LABEL_COLUMN
        self.checkpoints_dir = checkpoints_dir or "CHECKPOINTS"
        if not os.path.exists(self.checkpoints_dir):
            os.mkdir(self.checkpoints_dir)
            print("Created checkpoints directory %s" % self.checkpoints_dir)
        self.build_model([wide_learning_rate, deep_learning_rate])