Model/Sequential

dllib.keras.models.Model

class bigdl.dllib.keras.models.Model(input, output, jvalue=None, **kwargs)

Container for a graph model.

# Arguments input: An input node or a list of input nodes. output: An output node or a list of output nodes. name: String to specify the name of the graph model. Default is None.

save_graph_topology(log_path, backward=False)

Save the current model graph to a folder, which can be displayed in TensorBoard by running the command: tensorboard –logdir log_path

# Arguments log_path: The path to save the model graph. backward: The name of the application.

new_graph(outputs)

freeze_up_to(names)

unfreeze(names)

Config layers that needed to be unfreeze

# Arguments names: Layers to unfreeze.

static from_jvalue(jvalue, bigdl_type='float')

Create a Python Model base on the given java value :param jvalue: Java object create by Py4j :return: A Python Model

dllib.keras.models.Sequential

class bigdl.dllib.keras.models.Sequential(jvalue=None, **kwargs)

Container for a sequential model.

# Arguments name: String to specify the name of the sequential model. Default is None.

>>> sequential = Sequential(name="seq1")
creating: createZooKerasSequential

is_built()

add(model)

static from_jvalue(jvalue, bigdl_type='float')

Create a Python Model base on the given java value :param jvalue: Java object create by Py4j :return: A Python Model