17.08.2020 · The Loss.call() method is just an interface that a subclass of Loss must implement. But we can see that the return value of this method is Loss values with the shape [batch_size, d0, .. dN-1].. Now let's see LossFunctionWrapper class.LossFunctionWrapper is a subclass of Loss.In its constructor, we should provide a loss function, which is stored in LossFunctionWrapper.fn.
I trained and saved a model that uses a custom loss function (Keras version: 2.0.2): model.compile(optimizer=adam, loss=SSD_Loss(neg_pos_ratio=neg_pos_ratio, alpha=alpha).compute_loss) When I try to load the model, I get this error: Valu...
Hi @hockeyjudson In custom_policy_loss you are calling batch_dot(y_true,out) For batch dot multiplication the shape of two parameters provided must be same whereas y_true has shape (None, 4) and out has shape (None, 2). According to your network state_dim and action_dim have to be equal for since they decide the shape y_true and out respectively. Hope this helps.
where you try to maximize the proximity between predictions and targets. If either `y_true` or `y_pred` is a zero vector, cosine similarity will be 0. regardless of the proximity between predictions and targets. `loss = -sum (l2_norm (y_true) * l2_norm (y_pred))`.
07.11.2017 · An alternative to this custom loss function method is to concatenate the vgg16 model at the end of your model, make it untrainable, and use …
Keras Result: b. Below is the snippet of custom loss function: def cornet_loss(params): def loss(y_true,y_pred): def cor(y1,y2,lamda): y1_mean = K.mean(y1, ...