def regular_graph(degree, seed=1):
i2 = CompositeInitializer().with_seed(seed).uniform()
i = CompositeInitializer() \
.with_seed(seed) \
.uniform() \
.regular_graph(degree) \
.spectral_normalize() \
.scale(1.0)
w = WeightInitializer()
w.weight_hh_init = i
w.weight_ih_init = i2
return w
nrmse = NRMSELoss()
if norm:
X, X_test, y, y_test, centr, spread = dl.norm_loader__(sunspot)
y_test = spread * y_test + centr
else:
X, X_test, y, y_test = sunspot()
i = CompositeInitializer()\
.with_seed(12)\
.uniform()\
.regular_graph(4)\
.spectral_normalize()\
.scale(0.9)
w = WeightInitializer()
w.weight_hh_init = i
esn = DeepESN(initializer= w, hidden_size=500, num_layers=2)
start = time.time()
# esn.to_cuda()
esn.fit(X, y)
if norm:
output = spread * esn(X_test) + centr
else:
output = esn(X_test)
print(time.time()-start)
n = nrmse(output, y_test).item()
print(n)
last = 50
plt.plot(range(last), output.view(-1).detach().numpy()[-last:], 'r')