def error_rate(X_val, Y_val, nn):
error = 0
for i in range(X_val.shape[0]):
y = nn.predict(X_val[i, :])
if y != Y_val[i, 0]:
error += 1
return error / float(X_val.shape[0])
er = []
if train == 'stochastic':
for hdim in hdims:
nn = NeuralNet(X_train.shape[1], hdim, 6, lam)
for it in range(20):
LL = 0
for i in range(X_train.shape[0]):
pr = nn.update_stochastic(X_train[i], Y_train[i, 0], lrate)
LL += pr
LL /= float(X_train.shape[0])
er_val = error_rate(X_val, Y_val, nn)
er_train = error_rate(X_train, Y_train, nn)
er_test = error_rate(X_test, Y_test, nn)
er.append(er_val)
print "error rate on val db: hdim: {}, er rate: {}".format(hdim, er_val)
print "error rate on train db: hdim: {}, er rate: {}".format(hdim, er_train)
print "error rate on test db: hdim: {}, er rate: {}".format(hdim, er_test)
if train == 'batch':
for hdim in hdims:
nn = NeuralNet(X_train.shape[1], hdim, 6, lam)
