0e6f7cea4abae753c8e496bbbe6465af6818ad92,tensorforce/core/networks/layer.py,Conv2d,tf_regularization_loss,#Conv2d#,571

Before Change

        return x

    def tf_regularization_loss(self):
        if super(Conv2d, self).tf_regularization_loss() is None:
            losses = list()
        else:
            losses = [super(Conv2d, self).tf_regularization_loss()]

        if self.l2_regularization > 0.0:
            losses.append(self.l2_regularization * tf.nn.l2_loss(t=self.filters))
            if self.bias is not None:

After Change

        return x

    def tf_regularization_loss(self):
        regularization_loss = super(Conv2d, self).tf_regularization_loss()
        if regularization_loss is None:
            losses = list()
        else:
            losses = [regularization_loss]

        if self.l2_regularization > 0.0:
            losses.append(self.l2_regularization * tf.nn.l2_loss(t=self.filters))
            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)))

        regularization_loss = self.nonlinearity.regularization_loss()
        if regularization_loss is not None:
            losses.append(regularization_loss)

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

Instances