MxNet新前端Gluon模型轉換到Symbol

1. 導入各種包

from mxnet import gluon
from mxnet.gluon import nn
import matplotlib.pyplot as plt
from mxnet import autograd as autograd
from mxnet import nd
import mxnet as mx
from collections import namedtuple
import random

2. 准備數據

使用和mnist很像的FashionMNIST數據集，使用Gluon下載

def transform(data,label):
    return data.astype('float32')/255,label.astype('float32')

fashion_train = gluon.data.vision.FashionMNIST(root='./',train=True,transform=transform)
fashion_test = gluon.data.vision.FashionMNIST(root='./',train=True, transform=transform)

batch_size = 256
train_data = gluon.data.DataLoader(fashion_train,batch_size,shuffle=True)
test_data = gluon.data.DataLoader(fashion_test,batch_size,shuffle=True)

用於顯示圖像和標簽

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]

看下數據集長啥樣

data,label = fashion_train[5:19]
show_images(data)
print(get_text_labels(label))

['coat', 'coat', 'sandal', 'coat', 'bag', 't-shirt', 'bag', 'ankle boot', 't-shirt', 'pullover', 'pullover', 'ankle boot', 'dress,', 'dress,']

3. 精度計算函數

def accuracy(output, label):
    return nd.mean(output.argmax(axis=1)==label).asscalar()

def evaluate_accuracy(data_iterator, net):
    acc = 0.
    for data, label in data_iterator:
        output = net(nd.transpose(data,(0,3,1,2)))
        acc += accuracy(output, label)
    return acc / len(data_iterator)

4. 定義網絡

4.1 自己定義的層

Gluon模型轉到Symbol下只能用HybridSequential模式，HybridSequential是靜態圖，會對計算有優化，不過HybridSequential和Sequential可以很方便的轉換，確切的就是一行代碼的事。同樣自定義的網絡，要使用HybridBlock，和Block沒有多大區別

class MyDense(nn.HybridBlock):
    def __init__(self,**kwargs):
        super(MyDense,self).__init__(**kwargs)
        with self.name_scope():
            self.dense0 = nn.Dense(256)
            self.dense1 = nn.Dense(10)
    def hybrid_forward(self,F,x):   #  這里要使用hybrid_forward而不是forward，並且多了個參數F
        return self.dense1(F.relu(self.dense0(x)))    #  F的作用就是替代 nd，如果是靜態圖，就是用 sym，否則使用 nd

4.2 使用自定義的層和自帶的層組成完整的網絡

網絡定義和動態圖一樣，只不過把Sequential替換成了HybridSequential，在最后使用hybridize()會對靜態圖進行優化

net = nn.HybridSequential()
with net.name_scope():
    net.add(gluon.nn.Conv2D(channels=20, kernel_size=5, activation='relu'))
    net.add(gluon.nn.MaxPool2D(pool_size=2, strides=2))
    net.add(gluon.nn.Conv2D(channels=50, kernel_size=3, activation='relu'))
    net.add(gluon.nn.MaxPool2D(pool_size=2, strides=2))
    net.add(gluon.nn.Flatten())
    net.add(MyDense())
net.initialize(init=mx.init.Xavier())
net.hybridize()
net

HybridSequential(
  (0): Conv2D(20, kernel_size=(5, 5), stride=(1, 1))
  (1): MaxPool2D(size=(2, 2), stride=(2, 2), padding=(0, 0), ceil_mode=False)
  (2): Conv2D(50, kernel_size=(3, 3), stride=(1, 1))
  (3): MaxPool2D(size=(2, 2), stride=(2, 2), padding=(0, 0), ceil_mode=False)
  (4): Flatten
  (5): MyDense(
    (dense0): Dense(256, linear)
    (dense1): Dense(10, linear)
  )
)

5. 訓練

使用Adam優化算法，訓練的速度會快點

softmax_cross_entropy = gluon.loss.SoftmaxCrossEntropyLoss()
trainer = gluon.Trainer(net.collect_params(), 'Adam', {'learning_rate': 0.008})

for epoch in range(5):
    train_loss = 0.
    train_acc = 0.
    test_acc = 0.
    for data, label in train_data:
        data = nd.transpose(data,(0,3,1,2))
        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)

    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))

Epoch 0. Loss: 0.498041, Train acc 0.817226, Test acc 0.865459
Epoch 1. Loss: 0.312128, Train acc 0.884813, Test acc 0.894265
Epoch 2. Loss: 0.274009, Train acc 0.898454, Test acc 0.898604
Epoch 3. Loss: 0.247741, Train acc 0.906521, Test acc 0.914910
Epoch 4. Loss: 0.226967, Train acc 0.913736, Test acc 0.914334

6. 保存成Symbol格式的網絡和參數（重點）

要注意保存網絡參數的時候，需要net.collect_params().save()這樣保存，而不是net.save_params()保存
最新版的mxnet已經有可以導出到symbol格式下的接口了。需要mxnet版本在20171015以上
下面示例代碼也已經改成新版的保存，加載方式

#新版本的保存方式
net.export('Gluon_FashionMNIST')

7. 使用Symbol加載網絡並綁定

symnet = mx.symbol.load('Gluon_FashionMNIST-symbol.json')
mod = mx.mod.Module(symbol=symnet, context=mx.cpu())
mod.bind(data_shapes=[('data', (1, 1, 28, 28))])
mod.load_params('Gluon_FashionMNIST-0000.params')
Batch = namedtuple('Batch', ['data'])

8. 預測試試看效果

img,label = fashion_test[random.randint(0, 60000)]
data = img.transpose([2,0,1])
data = data.reshape([1,1,28,28])

mod.forward(Batch([data]))
out = mod.get_outputs()
prob = out[0]
predicted_labels = prob.argmax(axis=1)

plt.imshow(img.reshape((28, 28)).asnumpy())
plt.axis('off')
plt.show()
print('predicted labels:',get_text_labels(predicted_labels.asnumpy()))

print('true labels:',get_text_labels([label]))

predicted labels: ['pullover']
true labels: ['pullover']