Contents
How are loss functions used in Generative Adversarial Networks?
The loss functions themselves are deceptively simple: The discriminator tries to maximize this function. In other words, it tries to maximize the difference between its output on real instances and its output on fake instances. The generator tries to maximize this function.
Is the validation loss the same as training loss?
Validation loss is the same metric as training loss, but it is not used to update the weights.
When is validation loss used in back propagation?
It’s my understanding that loss is calculated at the end of the feed forward in a NeuralNet and is used in back propagation to update the weights. But I also see validation loss as a metric and don’t understand fully what it is for other than observation, is validation loss used in updating weights?
When do neural networks use a validation set?
Neural networks (and other model types) typically use a validation set on every epoch, because training too long can cause over-fitting, and models don’t recover from that, they just get worse form that point on.
How is the discriminator trained in generative adversarial network?
The discriminator is trained directly on real and generated images and is responsible for classifying images as real or fake (generated). The generator is not trained directly and instead is trained via the discriminator model. Specifically, the discriminator is learned to provide the loss function for the generator.
What does min and Max mean in generative adversarial network?
The min and max refer to the minimization of the generator loss and the maximization of the discriminator’s loss. As stated above, the discriminator seeks to maximize the average of the log probability of real images and the log of the inverse probability for fake images.
Why does the generator loss connect to the discriminator?
The discriminator loss penalizes the discriminator for misclassifying a real instance as fake or a fake instance as real. The discriminator updates its weights through backpropagation from the discriminator loss through the discriminator network. In the next section we’ll see why the generator loss connects to the discriminator.