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Next, we trained using a learning rate of 0.001, which decreases to 1/10 of its value every 20,000 iterations.
Using a learning rate of (alpha = frac {1}{(1+t)^{2}}) brings down the convergence rate to a polynomial in (frac {1}{ 1-gamma)}), where γ is the discount factor [38].
In the model, the weight ωX,Y of an adaptive connection from state X to state Y is updated after time step Δt using a learning rate ηH > 0 and extinction rate ζH ≥ 0, and the activation levels X t) and X t) of the states X and Y.
BP was either not used at all, or used at initialization and again at generations 100 and 200, using a learning rate of 0.3.
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Training a simple classifier uses a learning rate of 0.001, reducing by 10% every 5000 iterations.
For VGG-16 and ResNet-50, we used a learning rate of 0.001 for the spatial streams.
Specifically, we use a learning rate of 0.000001 for the spatial stream because it tends to converge significantly faster than the motion stream.
We use a learning rate of (alpha = frac {1}{(1+to^{2}}) to ensure that our proposal complies with the dynamic nature of the environment.
For the motion stream, we use a learning rate of 0.0001 because this combination of parameters is sufficient to stabilize the procedure so that the gating stream is able to train enough data.
We then contribute a new reinforcement learning technique using a variable learning rate to overcome these shortcomings.
We have also considered some additional weights of the network as free parameters, by applying fine tuning (a smaller learning rate value) to the weights of (some of) the convolutional layers of the ResNet network, while using a normal learning rate value for the FC part of it.
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CEO of Professional Science Editing for Scientists @ prosciediting.com