f09b1d753bff6d85b32b52f01f4dee86511f3dbb,tensorforce/core/networks/layer.py,Conv2d,tf_regularization_loss,#Conv2d#,429

Before Change

    def tf_regularization_loss(self):
        if self.l2_regularization == 0.0:
            return super(Conv2d, self).tf_regularization_loss()

        if super(Conv2d, self).tf_regularization_loss() is None:
            losses = list()

After Change

            if self.bias is not None:
                losses.append(self.l2_regularization * tf.nn.l2_loss(t=self.bias))

        if self.l1_regularization > 0.0:
            losses.append(self.l1_regularization * tf.reduce_sum(input_tensor=tf.abs(x=self.filters)))
            if self.bias is not None:
                losses.append(self.l1_regularization * tf.reduce_sum(input_tensor=tf.abs(x=self.bias)))

        if len(losses) > 0:
            return tf.add_n(inputs=losses)
        else:
            return None

Instances