Assertion: neural network's weight | /r/HasProperty | optimized
Facets:
value: during the training period
type: TEMPORAL
Source sentences:
text: this essentially means that all the weights and parameters of the ConvNet have now been optimized to correctly classify images from the training set.
text: These weights are then optimised during the training period and the optimum weights are produced.
text: When a new (unseen) image is input into the ConvNet, the network would go through the forward propagation step and output a probability for each class (for a new image, the output probabilities are calculated using the weights which have been optimized to correctly classify all the previous training examples).
text: This can be improved slightly by weighting the predictions from each model, where the weights are optimized using a hold-out validation dataset.
text: The connections are weighted and weights are optimized using a learning rule.