def fitness_func(ind, queue=None):
assert not ind.fitness, "Recalculation of existing fitness value -> This shouldn't have happened!"
data = CData("TestData/Dohany_ANN/full.csv", cross_val=.2, header=True, sep=";")
repeats = ind.genome[1]
ind.phenotype = get_brain(ind.genome, data)
for _ in range(repeats):
for batch in data.batchgen(5):
ind.phenotype.learn(batch)
fitness = data.test(ind.phenotype)
ind.fitness = fitness
# If parallelization is involved
if queue:
queue.put(ind)
import time
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)))
stop = time.time() - start
print("Learning phase took {} seconds.".format(round(stop, 2)))
print("Average speed per lesson: {} seconds.".format(round(stop, 2)))
print("!!!!!!!!!!!!!!!!!!!! Testing Archon !!!!!!!!!!!!!!!!!!!!!!!!")
print("Testing on <testing> data... ", sep="\t", end="")
t = myData.test(archon, "learning")
print("{}%".format(round(t, 4) * 100))
print("Testing on <learning> data...", sep="\t", end="")
l = myData.test(archon, "testing")
print("{}%".format(round(t, 4) * 100))
return t, l
myData = CData("TestData/Dohany_ANN/full.csv", cross_val=.20, header=True, sep=";")
INPUTS, OUTPUTS = myData.neurons_required()
LAYOUT = [INPUTS] + list(HIDDENS) + [OUTPUTS]
glob_start = time.time()
archon = None
result = 0
epoch = 1
while result < .99:
print("\n------------------------------------------------------------")
print("!!!!!!!!!!!!!!!!!!!!! {}. run started !!!!!!!!!!!!!!!!!!!!!!!"
.format(epoch))
print("------------------------------------------------------------")
from AI.Architecture.FFNN import *
from Wrappers.DataModel import CData
# ARCHITECTURE = FFNeuralBrain
ARCHITECTURE = FFLayerBrain
bob = ARCHITECTURE(1, (2, 3, 2))
bob.shuffle(1)
data = CData("TestData/LogicGates/AND.txt")
for i in range(10000):
if i % 1000 == 0:
print("Error @:", i, data.test(bob, "l"), sep="\t")
bob.learn(data.table())
