multiml.task.keras.modules.conv2d module

class multiml.task.keras.modules.conv2d.Conv2DBlock(*args, **kwargs)

Bases: Model

__init__(layers_conv2d=None, conv2d_padding='valid', *args, **kwargs)

Constructor.

Parameters:

• layers_conv2d (list(tuple(str, dict))) – configs of conv2d layer. list of tuple(op_name, op_args).

• conv2d_padding (str) – padding option of conv2d (valid or same)

• *args – Variable length argument list

• **kwargs – Arbitrary keyword arguments

call(input_tensor, training=False)

Calls the model on new inputs.

In this case call just reapplies all ops in the graph to the new inputs (e.g. build a new computational graph from the provided inputs).

Parameters:

• inputs – A tensor or list of tensors.

• training – Boolean or boolean scalar tensor, indicating whether to run the Network in training mode or inference mode.

• mask – A mask or list of masks. A mask can be either a tensor or None (no mask).

Returns:

A tensor if there is a single output, or a list of tensors if there are more than one outputs.