import d2l
from mxnet import autograd, np, npx, gluon
npx.set_np()
true_w = np.array([2, -3.4])
true_b = 4.2
features, labels = d2l.synthetic_data(true_w, true_b, 1000)
Reading Data
def load_array(data_arrays, batch_size, is_train=True):
dataset = gluon.data.ArrayDataset(*data_arrays)
return gluon.data.DataLoader(dataset, batch_size, shuffle=is_train)
batch_size = 10
data_iter = load_array((features, labels), batch_size)
for X, y in data_iter:
print('X =\n%sy =\n%s' %(X, y))
break
X = [[ 0.4015098 1.4096868 ] [ 0.65820086 -1.4260322 ] [ 0.00153129 -0.14330608] [-0.843129 0.6070013 ] [ 1.5080738 -0.27229312] [-0.01436996 0.50522786] [-0.2513225 -0.7733599 ] [-0.4892422 0.82852226] [ 0.19469471 0.26424283] [ 0.8269238 1.0562588 ]]y = [ 0.21267602 10.392891 4.7093544 0.45944032 8.140177 2.475876 6.332503 0.4059622 3.69898 2.2603266 ]
Define the Model and initialize Model Parameters
from mxnet.gluon import nn
from mxnet import init
net = nn.Sequential()
net.add(nn.Dense(1))
net.initialize(init.Normal(sigma=0.01))
Define the loss function and optimization algorithm
from mxnet import gluon
loss = gluon.loss.L2Loss()
trainer = gluon.Trainer(net.collect_params(),
'sgd', {'learning_rate': 0.03})
Training
for epoch in range(1, 4):
for X, y in data_iter:
with autograd.record():
l = loss(net(X), y)
l.backward()
trainer.step(batch_size)
l = loss(net(features), labels)
print('epoch %d, loss: %f' % (epoch, l.mean()))
w = net[0].weight.data()
print('Error in estimating w', true_w.reshape(w.shape) - w)
b = net[0].bias.data()
print('Error in estimating b', true_b - b)
epoch 1, loss: 0.040749 epoch 2, loss: 0.000152 epoch 3, loss: 0.000051 Error in estimating w [[ 0.00024056 -0.00077081]] Error in estimating b [0.00041628]