82a059d89d5f5ebc77c6a3f0b8ac4072a3979104,tensorforce/core/networks/layer.py,Conv2d,__init__,#Conv2d#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#,936

Before Change

            l1_regularization: L1 regularization weight
        
        self.size = size
        if isinstance(window, int):
            self.window = (window, window)
        elif len(window) == 2:
            self.window = tuple(window)
        else:
            raise TensorForceError("Invalid window {} for conv2d layer, must be of size 2".format(window))
        self.stride = stride
        self.padding = padding
        self.bias = bias
        self.l2_regularization = l2_regularization

After Change

            summary_labels=summary_labels
        )
        if self.skip:
            self.linear_skip = Linear(
                size=size,
                bias=bias,
                l2_regularization=l2_regularization,
                l1_regularization=l1_regularization,
                summary_labels=summary_labels
            )
        // TODO: Consider creating two nonlinearity variables when skip is used and learning beta
        //       Right now, only a single beta can be learned
        self.nonlinearity = Nonlinearity(summary_labels=summary_labels, **util.prepare_kwargs(activation))
        super(Dense, self).__init__(named_tensors=named_tensors, scope=scope, summary_labels=summary_labels)

Instances