def train_hybrid(in_feat=2):
'''
Train a hybrid Anfis based on the Iris data.
I use a 'resilient' BP optimiser here, as SGD was a little flakey.
'''
train_data = get_iris_data_one_hot(in_feat)
x, y_actual = train_data.dataset.tensors
model = make_anfis(x, num_mfs=3, num_out=3)
# optimizer = torch.optim.SGD(model.parameters(), lr=1e-4, momentum=0.99)
optimizer = torch.optim.Rprop(model.parameters(), lr=1e-4)
criterion = torch.nn.MSELoss(reduction='sum')
experimental.train_anfis_with(model, train_data, optimizer, criterion, 5)
experimental.plot_all_mfs(model, x)
nc, tot = num_cat_correct(model, x, y_actual)
print('{} of {} correct (={:5.2f}%)'.format(nc, tot, nc * 100 / tot))
return model
def train_non_hybrid(in_feat=2):
'''
Train a non-hybrid Anfis for the Iris data (so, no LSE).
Loss criterion is CrossEntropy, and expects target to be categories.
Note that the model still produces (float) scores for each category.
'''
train_data = get_iris_data(in_feat)
x, y_actual = train_data.dataset.tensors
model = make_anfis(x, num_mfs=3, num_out=3, hybrid=False)
optimizer = torch.optim.SGD(model.parameters(), lr=1e-2, momentum=0.99)
def criterion(input, target): # change the dim and type
return torch.nn.CrossEntropyLoss()(input, target.squeeze().long())
experimental.train_anfis_with(model, train_data, optimizer, criterion, 250)
y_pred = model(x)
nc = torch.sum(y_actual.squeeze().long() == torch.argmax(y_pred, dim=1))
tot = len(x)
experimental.plot_all_mfs(model, x)
print('{} of {} correct (={:5.2f}%)'.format(nc, tot, nc * 100 / tot))
return model
plt.plot(range(len(error)), error, 'r', label='error')
plt.xlabel('Time')
plt.ylabel('Error in y(t)')
plt.legend(loc='upper right')
plt.hlines(y=0, xmin=0, xmax=size, linestyle=':', color='grey')
plt.show()
mse, rmse, perc_loss = experimental.calc_error(torch.tensor(y_act),
torch.tensor(y_desired))
print('On {} test cases, MSE={:.5f}, RMSE={:.5f} ={:.2f}%'.format(
size, mse, rmse, perc_loss))
#model.layer.fuzzify.show()
return (y_act, u)
model = plant_model_untrained()
print('### TRAINING ###')
train_data = make_training_data()
experimental.plot_all_mfs(model, train_data.dataset.tensors[0])
optimizer = torch.optim.Rprop(model.parameters(), lr=1e-3, etas=(0.9, 1.2))
criterion = torch.nn.MSELoss(reduction='sum')
experimental.train_anfis_with(model,
train_data,
optimizer,
criterion,
epochs=150,
show_plots=True)
experimental.plot_all_mfs(model, train_data.dataset.tensors[0])
print('### TESTING ###')
test_control_model(model)