#coding:utf-8
from mxnet import ndarray as nd
from mxnet import autograd
from mxnet import gluon
def transform(data,label):
return data.astype('float32') / 255,label.astype('float32')
# 定义精度计算
def accuracy(output,label):
return nd.mean(output.argmax(axis=1) == label).asscalar()
# 估计模型精度
def evaluate_accuracy(data_iterator,net):
acc = 0
for data,label in data_iterator:
output = net(data)
acc += accuracy(output,label)
return acc / len(data_iterator)
mnist_train = gluon.data.vision.FashionMNIST(train=True,transform=transform)
mnist_test = gluon.data.vision.FashionMNIST(train=False,transform=transform)
# 读取数据
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)
# 定义和初始化模型
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
softmax_cross_entropy = gluon.loss.SoftmaxCrossEntropyLoss()
# 优化器
trainer = gluon.Trainer(net.collect_params(),'sgd',{'learning_rate':0.1})
# 训练
epochs = 5
for epoch in range(epochs):
train_loss = 0.0
train_acc = 0.0
for data,label in train_data:
with autograd.record():
output = net(data)
loss = softmax_cross_entropy(output,label)
loss.backward()
trainer.step(batch_size)
train_loss += nd.mean(loss).asscalar()
train_acc += accuracy(output,label)
print('epoch:%d,loss = %f,acc = %f'% (epoch,train_loss/ len(train_data),
train_acc/ len(train_data)))
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 ))
import matplotlib.pyplot as plt
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-shirt','trouser','pullover','dress','coat',
'sandal','shirt','sneaker','bag','ankle boot'
]
return [text_labels[int(i)] for i in label]
# 预测
data, label = mnist_test[0:9]
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()))
# show_images(data)