def main_circles():
# init
data = CirclesData()
data.plot_data()
np.random.seed(42)
N = data.Xtrain.shape[0]
inds = np.arange(0, N)
np.random.shuffle(inds)
Xtrain = data.Xtrain[inds]
Ytrain = data.Ytrain[inds]
Nbatch = 15
nx = data.Xtrain.shape[1]
nh = 10
ny = data.Ytrain.shape[1]
eta = 0.03
# Premiers tests, code à modifier
model, loss, optim = init_model(nx, nh, ny, eta)
writer = SummaryWriter()
L, acc = 0, 0
# TODO apprentissage
Nepochs = 200
for i in range(Nepochs):
for j in range(0, N, Nbatch):
Xbatch = Xtrain[j:j + Nbatch]
Ybatch = Ytrain[j:j + Nbatch]
Yhat = model(Xbatch)
L, acc = loss_accuracy(loss, Yhat, Ybatch)
# Calcule les gradients
optim.zero_grad()
L.backward()
optim.step()
# Loss and Accuracy on Test
Yhat_test = model(data.Xtest)
L_test, acc_test = loss_accuracy(loss, Yhat_test, data.Ytest)
data.plot_loss(L, L_test, acc, acc_test)
Ygrid = torch.nn.Softmax(dim=1)(model(data.Xgrid))
data.plot_data_with_grid(Ygrid.detach())
# attendre un appui sur une touche pour garder les figures
input("done")
# Premiers tests, code à modifier
model, loss = init_model(nx, nh, ny)
writer = SummaryWriter()
L, acc = 0, 0
# TODO apprentissage
Nepochs = 200
for i in range(Nepochs):
for j in range(0, N, Nbatch):
Xbatch = Xtrain[j:j + Nbatch]
Ybatch = Ytrain[j:j + Nbatch]
Yhat = model(Xbatch)
L, acc = loss_accuracy(loss, Yhat, Ybatch)
# Calcule les gradients
L.backward()
params = sgd(model, eta)
# Loss and Accuracy on Test
Yhat_test = model(data.Xtest)
L_test, acc_test = loss_accuracy(loss, Yhat_test, data.Ytest)
data.plot_loss(L, L_test, acc, acc_test)
Ygrid = torch.nn.Softmax(dim=1)(model(data.Xgrid))
data.plot_data_with_grid(Ygrid.detach())
# attendre un appui sur une touche pour garder les figures
input("done")
