]
inputTraining = inputData
targetTraining = targetData
# nn = NNet(sizes=[5, 3], bias=True)
nn = NNet(sizes=[6, 12, 2], bias=True)
# nn = NNet([[[-0.829638, 0.164111, 0.398885],
# [-0.603684, -0.603331, -0.819179],
# [-0.080592, -0.386044, -0.931615],
# [0.762514, -0.142887, -0.737862],
# [0.175430, 0.790112, -0.267367],
# [-0.732674, -0.825474, 0.232357]]], bias=True)
# ]])
nn.setActivations(['relu', 'linear'])
nn.setVerbose([])
nn.checkup(inputData, targetData)
verbosePrint.vIteration = -1
verbosePrint.stage = ''
cycles = 1000
report = cycles / 10
for iteration in range(cycles + 1):
vprint(iteration, '~~~~~~~~~~~ Iteration %d ~~~~~~~~~~~' % iteration)
combinedError = 0
for row_index in range(len(targetTraining)):
datain = inputTraining[row_index:row_index + 1]
goal_prediction = targetTraining[row_index:row_index + 1]
import numpy as np
from nnet import NNet
nn = NNet([[[0.1], [0.2], [-0.1]]])
nn.setAlpha(0.01)
nn.setVerbose(True)
datain = [[8.5, 0.65, 1.2]]
goal = [[1]]
for i in range(4):
output = nn.fire(datain)
print('Goal: ' + str(goal))
print(nn)
nn.learn(datain, goal)