mxnet-讀取模型參數
阿新 • • 發佈：2018-11-15
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#!/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‘
]
#定義網絡
#定義網絡
net = build_lenet(gluon.nn.Sequential())
net.initialize(init=mx.init.Xavier())
print net
#加載模型參數
file_name = "net.params"
net.load_params(file_name)
#轉換圖像為(channel, height, weight)格式，並且為floating數據類型，通過transforms.ToTensor。
#另外，normalize所有像素值 使用 transforms.Normalize平均值0.13和標準差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()mxnet-讀取模型參數