"""Stands for Nicotiana Tabacum Geographic [Analysis]..."""
import time
from AI.Architecture.FFNN import FFLayerBrain
from Wrappers.DataModel import RData
start = time.time()
RATE = 2
HIDDENS = (15, 8)
REPEATS = 5000
ACTIVATION = "sigmoid"
myData = RData("TestData/NTGeo/Data.csv", 0.2, 2, True, sep=";", end="\n")
INPUTS, OUTPUTS = myData.neurons_required()
LAYOUT = [INPUTS] + list(HIDDENS) + [OUTPUTS]
fi = FFLayerBrain(rate=RATE, layout=LAYOUT)
print("Brain born with layout:", fi.layout)
for epoch in range(REPEATS):
fi.learn(myData.table("learning"))
if epoch % 100 == 0:
print("Error @{}:".format(epoch),
myData.test(fi), sep="\t")
print("Learning done in {} seconds. Final Error: {}"
.format(round(time.time() - start, 2), myData.test(fi)))
from AI.Architecture.FFNN import FFLayerBrain
from Wrappers.DataModel import CData
HIDDENS = (30,)
RATE = 3
REPEATS = 1
BATCHSIZE = 10
start = time.time()
myData = CData("TestData/MNIST/mnist.pkl.gz", cross_val=0.2, header=False)
INPUTS, OUTPUTS = myData.neurons_required()
LAYOUT = [INPUTS] + list(HIDDENS) + [OUTPUTS]
archimedes = FFLayerBrain(RATE, layout=LAYOUT)
for epoch in range(REPEATS):
batches = myData.batchgen(BATCHSIZE)
for index, batch in enumerate(batches):
if index % 200 == 0:
right = myData.test(archimedes, "testing")
print(index * BATCHSIZE, right, sep="\t")
archimedes.learn(batch)
rrate = myData.test(archimedes) # Right-rate
print("Test @{}:".format(epoch), rrate, sep="\t")
print("Learning is done! Time required: {} seconds.".format(int(time.time() - start)))
print("----------------------------------------------")
print("Final test:")
