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")
accuracy,
sep='')
# Testing set
Yhat, _ = forward(params, torch.from_numpy(data._Xtest))
L, accuracy = loss_accuracy(Yhat, torch.from_numpy(data._Ytest))
print(' Test set:\n',
' loss: ',
L.item(),
'; accuracy: ',
accuracy,
sep='')
testLosses.append(L.item())
Ygrid, _ = forward(params, data.Xgrid)
data.plot_data_with_grid(Ygrid)
x = np.arange(0, Nepoch, printInterval)
trainLosses = np.array(trainLosses)
testLosses = np.array(testLosses)
plt.close()
plt.clf()
plt.ylabel('Losses')
plt.xlabel('Epoch')
plt.title('Train loss and Test loss with time')
plt.grid(True)
plt.plot(x, trainLosses, 'b--', x, testLosses, 'r--')
plt.legend(['Train loss', 'Test loss'], loc=1)
plt.show()
# attendre un appui sur une touche pour garder les figures
