-5, -2)
convlayer = None
if conv:
convlayer = np.random.randint(1, 6)
print "Preparing AlexNet outputs:"
set = make_sets(alex_net(faces, convlayer), classes,
85 * len(act), 27 * len(act))
print "\n"
print "Model %d:" % i
print "\t", hu, funs, lmbda, convlayer
bench, cs, acs = train_neural_net(set,
hu,
fun,
dropout=0.5,
batch_size=50,
index=i,
lmbda=lmbda,
best=best_val_accuracy,
max_iter=1500,
conv_input=conv)
c, a = bench
# Found new model according to validation accuracy
if a[1] > best_val_accuracy:
best_val_accuracy = a[1]
print "Found new best model!"
print "\tCost (Accuracy):"
print "\t\t %f (%4.2f%%)" % (best_val_accuracy, a[1])
print "\tModel:"
print "\t\t", hu, funs, lmbda, "\n\n"
nnt.plot_curves(cs, acs, autotitle=True, conv=conv)
if conv:
funcs = np.array([tf.nn.relu, tf.nn.tanh])
hu = np.random.randint(200, 850, nlayer)
idx = np.random.choice(2, nlayer)
funs = funcs_n[idx]
fun = funcs[idx]
lmbda = np.random.randint(0,10) * 10 ** np.random.randint(-5,-2)
convlayer = None
if conv:
convlayer = np.random.randint(1,6)
print "Preparing AlexNet outputs:"
set = make_sets(alex_net(faces, convlayer), classes, 85*len(act), 27*len(act))
print "\n"
print "Model %d:" %i
print "\t", hu, funs, lmbda, convlayer
bench,cs,acs = train_neural_net(set, hu, fun, dropout=0.5, batch_size=50, index=i, lmbda=lmbda,
best=best_val_accuracy, max_iter=1500, conv_input=conv)
c,a = bench
# Found new model according to validation accuracy
if a[1] > best_val_accuracy:
best_val_accuracy = a[1]
print "Found new best model!"
print "\tCost (Accuracy):"
print "\t\t %f (%4.2f%%)" %(best_val_accuracy, a[1])
print "\tModel:"
print "\t\t", hu, funs, lmbda, "\n\n"
nnt.plot_curves(cs,acs,autotitle=True, conv=conv)
if conv:
with open('results/models_conv_best.csv', 'w+') as fl:
fl.write("%s, %s, %s, %d, %5.4f, "
"%5.4f, %5.4f, %5.2f, %5.2f, %5.2f\n" %(hu, funs, lmbda, convlayer, c[0], c[1], c[2],
a[0], a[1], a[2]))