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– Create and save a NN.
– To train the NN, use the BATCH DATA parameter, which allows you to set the parameters (preferences) for the iterations:
– Iters: How many times will the NN be trained? It’s advisable to make iterations of differents value. In the case of Sinapse Tool, you can put a value between 0 (train forever) and the number of inputs or the number of output you want to train (for example 10 inputs, with 10 outputs).
– Epochs: How many times will the NN be trained? It’s advisable to make iterations of differents value. In the case of Sinapse Tool, you can put a value between 0 (train forever) and the number of inputs or the number of output you want to train (for example 10 inputs, with 10 outputs).
– Add inputs, biases, output, and neurons to the NN.
– The output will be used in a rule-based system that uses some conventions to evaluate the output, like “if the output is between 1 and 10, the value of the input will be up-loaded to the server”
– Synapse 1, 2 and 3 are ways to get some insights about the NN and some metrics you can get out of it. Sinapse Tool now has 2 options: “synapse 1” and “synapse 2”.
– Synapse 1 is the “engine” used to calculate the output of the NN. It shows the differentiable rule from the inputs to the NN output.
– Synapse 2 is the “engine” used to calculate the “score” of the NN. It shows the rule that calculate the score.
Sinapse Tutorial:
Use the sinapse command with a nn file in which you have some data and some settings.
Use the parameters below:
nn file: name of the neural network file in which you have the data.
Size: size of the data (if it’s a matrix) or number of points you want the data to be divided (for example if it’s a vector).
Batch: number of inputs (1 input, 2 inputs or…) you want to use to train the NN.
Iters: number of times the NN will be trained. It’s advisable to make iterations of differents value. In the case of Sinapse Tool, you can put a value between 0 (train forever) and the number of 84e02134c1

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