def run():
fruits = CData(gyumpath, gyumindeps, feature=TAXLEVEL, cross_val=CROSSVAL)
fruits.transformation = (TRANSFORMATION, TRANSFORMATION_PARAM)
network = build_net(*fruits.neurons_required)
testing = fruits.table("testing")
zsind = CData(zsindpath, zsindeps, cross_val=0.0, feature=TAXLEVEL)
vx, vy = zsind.learning, zsind.lindeps
vx = fruits.transformation(vx)
vy = fruits.embed(vy)
initc, initacc = network.evaluate(*testing, verbose=0)
initc, initacc = round(initc, 5), round(initacc, 5)
print("Initial cost: {}\tacc: {}".format(initc, initacc))
X, y = fruits.table("learning")
network.fit(X,
y,
batch_size=20,
nb_epoch=400,
validation_data=testing,
verbose=0)
tacc = network.evaluate(*testing, batch_size=fruits.n_testing,
verbose=0)[-1]
vacc = network.evaluate(vx, vy, verbose=0)[-1]
# batchgen = fruits.batchgen(100, infinite=True)
# log = network.fit_generator(batchgen, fruits.N, nb_epoch=15, validation_data=valid, verbose=verbose)
print("T: {}\tV: {}".format(tacc, vacc))
return tacc, vacc
def full_training(validate=True, dump_weights=False):
fruits = CData(gyumpath, gyumindeps, feature=TAXLEVEL, cross_val=0.0)
fruits.transformation = (TRANSFORMATION, TRANSFORMATION_PARAM)
network = build_net(*fruits.neurons_required)
X, y = fruits.table("learning")
network.fit(X, y, batch_size=30, nb_epoch=500, verbose=0)
if dump_weights:
weights = network.layers[0].get_weights()
wghts = open("weights.csv", "w")
wghts.write("\n".join([
"\t".join([str(float(cell)) for cell in line])
for line in weights[0].T
]).replace(".", ","))
wghts.close()
if validate:
vx, vy = CData(zsindpath, zsindeps, cross_val=0.0, feature=TAXLEVEL)
vx = fruits.transformation(vx)
vy = fruits.embed(vy)
vacc = network.evaluate(vx, vy, batch_size=len(vy), verbose=0)[-1]
probs = network.predict_proba(vx, verbose=0)
preds = network.predict_classes(vx, verbose=0)
print("ANN validation accuracy:", vacc)
return probs, preds, vy, fruits
