fully connected neural network with four layers
Project description
Fully connected four-layer neural network
Solves a huge number of cases, classification and regression
The following sequence explains how to use with the help of two example files.
The first file contains the learning process, where the neural network finds its weights
The second file demonstrates the network's ability to make predictions on new, unseen data that is not part of the training set
#Manual = https://www.mediafire.com/file/xygt3o9zf7iw3id/Manual_Tupa123.pdf
#Quick Guide = https://www.mediafire.com/file/a0db7fb3lfsxvaj/Guia_Rapido.pdf/file
#Excel example data = https://www.mediafire.com/file/o2nzsmnvweh8w1a/ALETAS.xlsx
#Excel example (old version) = https://www.mediafire.com/file/0xmx5quakd21txu/ALETAS.xls
#-----FILE TO MACHINE LEARNING
import tupa123 as tu
X = tu.ExcelMatrix('ALETAS.xlsx', 'Plan1', Lineini=2, Columini=1, columnquantity=5, linesquantity=300)
y = tu.ExcelMatrix('ALETAS.xlsx', 'Plan1', Lineini=2, Columini=6, columnquantity=2, linesquantity=300)
model = tu.nnet4(nn1c=5, nn2c=7, nn3c=5, nn4c=2, namenet='tupa01')
model.Fit_ADAM(X, y)
model.Plotconv()
input('end')
#-----FILE TO APPLICATION OF MACHINE LEARNING
import tupa123 as tu
model = tu.nnet4(nn1c=5, nn2c=7, nn3c=5, nn4c=2, namenet='tupa01')
X_new = tu.ExcelMatrix('ALETAS.xlsx', 'Plan1', Lineini=2, Columini=1, columnquantity=5, linesquantity=1000)
y_resposta = tu.ExcelMatrix('ALETAS.xlsx', 'Plan1', Lineini=2, Columini=6, columnquantity=2, linesquantity=1000)
y_pred = model.Predict(X_new)
tu.Statistics(y_pred, y_resposta)
tu.PlotCorrelation(y_pred, y_resposta)
tu.PlotComparative(y_pred, y_resposta)
input('end')
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