mxnet-保存模型参数

#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Fri Aug 10 16:13:29 2018
@author: myhaspl
"""

import mxnet as mx
import mxnet.ndarray as nd
from mxnet import nd, autograd, gluon
from mxnet.gluon.data.vision import datasets, transforms
from time import time

def build_lenet(net):    
    with net.name_scope():
        net.add(gluon.nn.Conv2D(channels=6,kernel_size=5,activation="relu"),
            gluon.nn.MaxPool2D(pool_size=2, strides=2),
            gluon.nn.Conv2D(channels=16, kernel_size=3, activation="relu"),
            gluon.nn.MaxPool2D(pool_size=2, strides=2),
            gluon.nn.Flatten(),
            gluon.nn.Dense(120, activation="relu"),
            gluon.nn.Dense(84, activation="relu"),
            gluon.nn.Dense(10))
        return net

mnist_train = datasets.FashionMNIST(train=True)
X, y = mnist_train[0]
print ('X shape: ', X.shape, 'X dtype', X.dtype, 'y:', y,'Y dtype', y.dtype)
#x:(height, width, channel)
#y:numpy.scalar，标签
text_labels = [
            't-shirt', 'trouser', 'pullover', 'dress', 'coat',
            'sandal', 'shirt', 'sneaker', 'bag', 'ankle boot'
]

#转换图像为(channel, height, weight)格式，并且为floating数据类型，通过transforms.ToTensor。
#另外，normalize所有像素值 使用 transforms.Normalize平均值0.13和标准差0.31. 
transformer = transforms.Compose([
            transforms.ToTensor(),
            transforms.Normalize(0.13, 0.31)])
#只转换第一个元素，图像部分。第二个元素为标签。
mnist_train = mnist_train.transform_first(transformer)
#加载批次数据
batch_size = 200
train_data = gluon.data.DataLoader(mnist_train, batch_size=batch_size, shuffle=True)
#读取本批数据
i=1
for data, label in train_data:
    print i
    print data,label
    break#没有这一行，会以每批次200个数据来读取。
mnist_valid = gluon.data.vision.FashionMNIST(train=False)
valid_data = gluon.data.DataLoader(mnist_valid.transform_first(transformer),batch_size=batch_size)

#定义网络
net = build_lenet(gluon.nn.Sequential())
net.initialize(init=mx.init.Xavier())
print net

#输出softmax与误差
softmax_cross_entropy = gluon.loss.SoftmaxCrossEntropyLoss()
#定义训练器
trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.1})

def acc(output, label):
    # output: (batch, num_output) float32 ndarray
    # label: (batch, ) int32 ndarray
    return (output.argmax(axis=1) == label.astype('float32')).mean().asscalar()

for epoch in range(10):
    train_loss, train_acc, valid_acc = 0., 0., 0.
    tic = time()
    for data, label in train_data:
        # 前向与反馈
        with autograd.record():
            output = net(data)
            loss = softmax_cross_entropy(output, label)
        loss.backward()
    # 换一批样本数据，更新参数
    trainer.step(batch_size)
    # 计算训练误差和正确率
    train_loss += loss.mean().asscalar()
    train_acc += acc(output, label)
    print "."

    #测试正确率
    for data, label in valid_data: 
        predict_data=net(data)
        valid_acc += acc(predict_data, label)

        print("Epoch %d: Loss: %.3f, Train acc %.3f, Test acc %.3f, \ Time %.1f sec" % (
                     epoch, train_loss/len(train_data),
                     train_acc/len(train_data),
                     valid_acc/len(valid_data), time()-tic))

#保存模型参数,非模型结构

file_name = "net.params"
net.save_parameters(file_name)