mxnet-读取模型参数

#!/usr/bin/env python2 # -*- coding: utf-8 -*- """ Created on Fri Aug 10 16:13:29 2018 @author: myhaspl """ import mxnet as mx from mxnet import nd from mxnet import gluon from mxnet.gluon import nn from mxnet.gluon.data.vision import datasets, transforms import matplotlib.pyplot as plt 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 text_labels = [ ‘t-shirt‘, ‘trouser‘, ‘pullover‘, ‘dress‘, ‘coat‘, ‘sandal‘, ‘shirt‘, ‘sneaker‘, ‘bag‘, ‘ankle boot‘ ] #定义网络 #定义网络 net = build_lenet(gluon.nn.Sequential()) net.initialize(init=mx.init.Xavier()) print net #加载模型参数 file_name = "net.params" net.load_params(file_name) #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)]) mnist_valid = gluon.data.vision.FashionMNIST(train=False) X, y = mnist_valid[:6] preds = [] for x in X: x = transformer(x).expand_dims(axis=0) pred = net(x).argmax(axis=1) preds.append(pred.astype(‘int32‘).asscalar()) _, figs = plt.subplots(1, 6, figsize=(15, 15)) for f,x,yi,pyi in zip(figs, X, y, preds): f.imshow(x.reshape((28,28)).asnumpy()) ax = f.axes ax.set_title(text_labels[yi]+‘\n‘+text_labels[pyi]) ax.title.set_fontsize(20) ax.get_xaxis().set_visible(False) ax.get_yaxis().set_visible(False) plt.show()