接上一篇机器学习笔记(3)：多类逻辑回归继续，这次改用gluton来实现关键处理，原文见这里 ，代码如下：
import matplotlib.pyplot as plt
import mxnet as mx
from mxnet import gluon
from mxnet import ndarray as nd
from mxnet import autograd
def transform(data, label):
return data.astype('float32')/255, label.astype('float32')
mnist_train = gluon.data.vision.FashionMNIST(train=True, transform=transform)
mnist_test = gluon.data.vision.FashionMNIST(train=False, transform=transform)
def show_images(images):
n = images.shape[0]
_, figs = plt.subplots(1, n, figsize=(15, 15))
for i in range(n):
figs[i].imshow(images[i].reshape((28, 28)).asnumpy())
figs[i].axes.get_xaxis().set_visible(False)
figs[i].axes.get_yaxis().set_visible(False)
plt.show()
def get_text_labels(label):
text_labels = [
'T 恤', '长 裤', '套头衫', '裙 子', '外 套',
'凉 鞋', '衬 衣', '运动鞋', '包 包', '短 靴'
]
return [text_labels[int(i)] for i in label]
data, label = mnist_train[0:10]
print('example shape: ', data.shape, 'label:', label)
show_images(data)
print(get_text_labels(label))
batch_size = 256
train_data = gluon.data.DataLoader(mnist_train, batch_size, shuffle=True)
test_data = gluon.data.DataLoader(mnist_test, batch_size, shuffle=False)
num_inputs = 784
num_outputs = 10
W = nd.random_normal(shape=(num_inputs, num_outputs))
b = nd.random_normal(shape=num_outputs)
params = [W, b]
for param in params:
param.attach_grad()
def accuracy(output, label):
return nd.mean(output.argmax(axis=1) == label).asscalar()
def _get_batch(batch):
if isinstance(batch, mx.io.DataBatch):
data = batch.data[0]
label = batch.label[0]
else:
data, label = batch
return data, label
def evaluate_accuracy(data_iterator, net):
acc = 0.
if isinstance(data_iterator, mx.io.MXDataIter):
data_iterator.reset()
for i, batch in enumerate(data_iterator):
data, label = _get_batch(batch)
output = net(data)
acc += accuracy(output, label)
return acc / (i+1)
#使用gluon定义计算模型
net = gluon.nn.Sequential()
with net.name_scope():
net.add(gluon.nn.Flatten())
net.add(gluon.nn.Dense(10))
net.initialize()
#损失函数(使用交叉熵函数)
softmax_cross_entropy = gluon.loss.SoftmaxCrossEntropyLoss()
#使用梯度下降法生成训练器，并设置学习率为0.1
trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.1})
for epoch in range(5):
train_loss = 0.
train_acc = 0.
for data, label in train_data:
with autograd.record():
output = net(data)
#计算损失
loss = softmax_cross_entropy(output, label)
loss.backward()
#使用sgd的trainer继续向前"走一步"
trainer.step(batch_size)
train_loss += nd.mean(loss).asscalar()
train_acc += accuracy(output, label)
test_acc = evaluate_accuracy(test_data, net)
print("Epoch %d. Loss: %f, Train acc %f, Test acc %f" % (
epoch, train_loss / len(train_data), train_acc / len(train_data), test_acc))
data, label = mnist_test[0:10]
show_images(data)
print('true labels')
print(get_text_labels(label))
predicted_labels = net(data).argmax(axis=1)
print('predicted labels')
print(get_text_labels(predicted_labels.asnumpy()))
相对上一版原始手动方法，使用gluon修改的地方都加了注释，不多解释。运行效果如下：
相对之前的版本可以发现，几乎相同的参数，但是准确度有所提升，从0.7几上升到0.8几，10个里错误的预测数从4个下降到3个，说明gluon在一些细节上做了更好的优化。关于优化的细节，这里有一些讨论，供参考