def main():
#return testLr()
X, y = preparDataSet(N=200)
net = RegressionNet() #RegressionNet(hidden=10)
#print(net)
optimizer = optimizerTorch(net.parameters(), lr=1e-1)
lossFuc = lossFunction() #mean suqare error
lambda1 = lambda epoch: 0.5**(epoch // 200) #decay 0.5 every times
scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda1)
EPOCHS = 800
for epoch in range(EPOCHS):
t = time.time()
net.zero_grad()
pred = net(X)
loss = lossFuc(pred, y)
#print('loss=',type(loss),loss)
loss.backward()
optimizer.step()
scheduler.step()
if epoch % 50 == 0:
log = f'epoch[{epoch+1}/{EPOCHS}] loss={round(float(loss),4)},run in {round(time.time()-t,4)}s'
print(log)
#print('epoch:',epoch, 'lr:',optimizer.param_groups[0]["lr"])
predict(net)
def main():
X, y = preparDataSetMul(N=2000, gamma=0.00001)
net = RegressionNet2(input=2, hidden=20,
hiddenlayers=2) #RegressionNet(hidden=10)
print(net)
optimizer = optimizerTorch(net.parameters(), lr=1e-3)
lossFuc = lossFunction() #mean suqare error
lambda1 = lambda epoch: 0.8**(epoch // 100) #decay 0.5 every times
scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda1)
EPOCHS = 2800
for epoch in range(EPOCHS):
t = time.time()
net.zero_grad()
pred = net(X)
#print(pred)
loss = lossFuc(pred, y)
#print('loss=', float(loss))
loss.backward()
optimizer.step()
#scheduler.step()
if epoch % 100 == 0:
lr = optimizer.param_groups[0]["lr"]
log = f'epoch[{epoch+1}/{EPOCHS}] loss={round(float(loss),4)},lr={float(lr)},run in {round(time.time()-t,4)}s'
print(log)
predict(net, a=[0.1, 0.2])
predict(net, a=[0.3, 0.1])
def trainNet(net, X, y, optimizer, EPOCHS=400, lossFuc=lossFunction()):
losses = []
for epoch in range(EPOCHS):
t = time.time()
net.zero_grad()
pred = net(X)
#print('X.shape, pred.shape, y.shape=', X.shape, pred.shape, y.shape)
#print('pred.dtype, y,dtype=', pred.dtype, y.dtype)
loss = lossFuc(pred, y)
loss.backward()
optimizer.step()
if epoch % (EPOCHS // 10) == 0:
log = f'epoch[{epoch+1}/{EPOCHS}] loss={round(float(loss),4)},run in {round(time.time()-t,4)}s'
print(log)
losses.append(float(loss))
return losses
def main():
X, y = preparDataSet(N=200)
net = RegressionNet() #RegressionNet(hidden=10)
#print(net)
optimizer = optimizerTorch(net.parameters(), lr=1e-2)
lossFuc = lossFunction() #mean suqare error
EPOCHS = 800
for epoch in range(EPOCHS):
t = time.time()
net.zero_grad()
pred = net(X)
loss = lossFuc(pred, y)
#print('loss=',type(loss),loss)
loss.backward()
optimizer.step()
if epoch % 50 == 0:
log = f'epoch[{epoch+1}/{EPOCHS}] loss={round(float(loss),4)},run in {round(time.time()-t,4)}s'
print(log)
predict(net)
def main():
#testLayers()
X, y = preparDataSet(gamma=0.1)
net = RegressionNet(hidden=50)
optimizer = optimizerTorch(net.parameters(), lr=1e-2)
lossFuc = lossFunction()
my_images = []
fig, ax = plt.subplots()
EPOCHS = 400
for epoch in range(EPOCHS):
t = time.time()
net.zero_grad()
pred = net(X)
#print(X.shape, pred.shape, y.shape)
loss = lossFuc(pred, y)
loss.backward()
optimizer.step()
# plot and show learning process
plt.cla()
ax.set_title('Regression Analysis', fontsize=12)
ax.set_xlabel('X /Feature /Independent variable', fontsize=10)
ax.set_ylabel('Y /Label /Dependent variable', fontsize=10)
#ax.set_xlim(-1.05, 1.5)
#ax.set_ylim(-0.25, 1.25)
ax.scatter(X.data.numpy(), y.data.numpy(), color="orange")
ax.plot(X.data.numpy(), pred.data.numpy(), 'g-', lw=3)
ax.text(0.75,
0.16,
'Epoch = %d' % epoch,
transform=ax.transAxes,
fontdict={
'size': 10,
'color': 'red'
})
ax.text(0.75,
0.12,
'Loss = %.4f' % loss.data.numpy(),
transform=ax.transAxes,
fontdict={
'size': 10,
'color': 'red'
})
# Used to return the plot as an image array
# (https://ndres.me/post/matplotlib-animated-gifs-easily/)
fig.canvas.draw() # draw the canvas, cache the renderer
image = np.frombuffer(fig.canvas.tostring_rgb(), dtype='uint8')
image = image.reshape(fig.canvas.get_width_height()[::-1] + (3, ))
if EPOCHS < 200 or (EPOCHS < 500 and epoch % 2 == 0) or (epoch % 4
== 0):
my_images.append(image)
if epoch % 50 == 0:
log = f'epoch[{epoch+1}/{EPOCHS}] loss={round(float(loss),4)},run in {round(time.time()-t,4)}s'
print(log)
#plt.show()
#break
# save images as a gif
imageio.mimsave('./res/curve.gif', my_images, fps=20)