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All networks had 6 hidden layers and 2048 hidden units.
Recurrent networks have feedback loops, in which connections emanating from hidden units circle back to hidden units.
The architecture for the student model is a MLP having 2 hidden layers with 1024 sigmoid hidden units each.
Conversely, once we know the states of hidden units for target 1, hidden units then send messages to visible units for the connected drugs and update their corresponding states.
The network consisted of five layers of units: 3072 visual units, 60 hidden units, 105 grapheme units, 250 hidden units and 61 phoneme units.
Figure 3 Changing hidden units.
Two hundred hidden units are used.
Using additional hidden units means that more bases are used to describe the input signal.
From the pre-experiment, we see the effects of changing the number of softmax hidden units.
MLP structure is a standard combination of inputs, hidden units, and outputs.
In this experiment, we used 16 softmax hidden units for the SATBM and the ARBM.
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