examples/attention_models/first_glimpse_model.py
def get_network(model=None, std=0.005, disable_reinforce=False, random_glimpse=False):
    """
    Get baseline model.
    Parameters:
        model - model path
    Returns:
        network
    """
    network = NeuralClassifier(input_dim=28 * 28)
    network.stack_layer(FirstGlimpseLayer(std=std, disable_reinforce=disable_reinforce, random_glimpse=random_glimpse))
    if model and os.path.exists(model):
        network.load_params(model)
    return network
def get_network(model=None, std=0.005, disable_reinforce=False, random_glimpse=False):
    """
    Get baseline model.
    Parameters:
        model - model path
    Returns:
        network
    """
    network = NeuralClassifier(input_dim=28 * 28)
    network.stack_layer(FirstGlimpseLayer(std=std, disable_reinforce=disable_reinforce, random_glimpse=random_glimpse))
    if model and os.path.exists(model):
        network.load_params(model)
    return network
def get_network ( model = None , std = 0.005 , disable_reinforce = False , random_glimpse = False ) : network = NeuralClassifier ( input_dim = 28 * 28 ) network . stack_layer ( FirstGlimpseLayer ( std = std , disable_reinforce = disable_reinforce , random_glimpse = random_glimpse ) ) if model and os . path . exists ( model ) : network . load_params ( model ) return network
Get baseline model.
    Parameters:
        model - model path
    Returns:
        network
Get baseline model . Parameters : model - model path Returns : network