Sentence examples for layer weight from inspiring English sources

In addition to the rank of the hidden layer weight matrices, we can also examine the distribution of singular values.

Representative plots of RIC are shown in Fig. 4 for the fourth and third hidden layer weight matrices of the 6 ×2048 and 5 ×512 networks, respectively.

We can see that being trained to generalize like the teacher network using soft targets produces hidden layer weight matrices having fuller rank than training with hard targets.

Several aspects relevant to bilayer tablet manufacturing are addressed including material properties, lubrication, layer ordering, layer thickness, layer weight control, as well as first and final compression forces.

The ranks of the hidden layer weight matrices for the 6 hidden layer, 2048 hidden unit (6 ×2048) and 5 hidden layer, 512 hidden unit (5 ×512) networks are shown in Table 8.

An investigation into the hidden layer weight matrices finds that soft target-trained networks tend to produce weight matrices having fuller rank and slower decay in singular values than their hard target-trained counterparts, suggesting that more of the network's capacity is utilized for learning additional information giving better accuracy.

Fig. 4 Distribution of singular values of hidden layer weights.

Error backpropagation and weight modification (in hidden layers): Weights and thresholds in hidden layers are calculated in a similar way to the output layer weights.

The output layer weights are adapted using the numerically robust Householder transform.

Only the 4,000 (80 inputs × 50 nodes) hidden layer weights are shown in the figure.

Specifically, as we go backwards from output layer weights learn at decreasing speed.