win10 + gluon + GPU

1. 下载教程

可以用浏览器下载zip格式并解压，在解压目录文件资源管理器的地址栏输入cmd进入命令行模式。

也可以

git pull https://github.com/mli/gluon-tutorials-zh

2.安装gluon CPU

添加源：

# 优先使用清华conda镜像
conda config --prepend channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/

# 也可选用科大conda镜像
conda config --prepend channels http://mirrors.ustc.edu.cn/anaconda/pkgs/free/

cmd中安装

conda env create -f environment.yml
activate gluon # 注意Windows下不需要 source

可更新教程：

conda env update -f environment.yml

3.安装GPU版本

先卸载CPU

pip uninstall mxnet

然后

pip install --pre mxnet-cu75 # CUDA 7.5
pip install --pre mxnet-cu80 # CUDA 8.0

【可选项】国内用户可使用豆瓣pypi镜像加速下载:

pip install --pre mxnet-cu75 -i https://pypi.douban.com/simple # CUDA 7.5
pip install --pre mxnet-cu80 -i https://pypi.douban.com/simple # CUDA 8.0

查看安装

import pip
for pkg in ['mxnet', 'mxnet-cu75', 'mxnet-cu80']:
    pip.main(['show', pkg])

4.查看教程

然后安装notedown，运行Jupyter并加载notedown插件：

pip install https://github.com/mli/notedown/tarball/master
jupyter notebook --generate-config
jupyter notebook --NotebookApp.contents_manager_class='notedown.NotedownContentsManager'

5.教程简记

跟NumPy的转换

from mxnet import ndarray as nd

import numpy as np
x = np.ones((2,3))
y = nd.array(x)  # numpy -> mxnet
z = y.asnumpy()  # mxnet -> numpy
print([z, y])

自动求导

import mxnet.autograd as ag

假设我们想对函数 $f = 2*x^2$ 求关于 $x$的导数。

1.创建变量

x = nd.array([[1, 2], [3, 4]])

2.通过NDArray的方法attach_grad()来要求系统申请梯度空间

x.attach_grad()

3.定义函数 f

with ag.record():
    y = x * 2
    z = y * x

4.反向传播，求梯度

z.backward()

5.梯度：

print('x.grad: ', x.grad)

线性回归，从零开始

#coding=utf-8
"""线性回归，从零开始"""

from mxnet import ndarray as nd
from mxnet import autograd
import matplotlib.pyplot as plt
import random

# 1.创建数据集
# y[i] = 2 * X[i][0] - 3.4 * X[i][1] + 4.2 + noise
# y = X*w + b + n
num_inputs = 2
num_examples = 1000

true_w = [2, -3.4]
true_b = 4.2

X = nd.random_normal(shape=(num_examples, num_inputs))
y = true_w[0] * X[:,0] + true_w[1] * X[:,1] + true_b
y += 0.01 * nd.random_normal(shape=y.shape)

# plt.scatter(X[:,1].asnumpy(), y.asnumpy())
# plt.show()

# 2.数据读取
batch_size = 10
def data_iter():
    # 产生一个随机索引
    idx = list(range(num_examples))
    random.shuffle(idx)
    for i in range(0, num_examples, batch_size):
        j = nd.array(idx[i:min(i+batch_size, num_examples)])
        yield nd.take(X, j), nd.take(y, j)

# for data, label in data_iter():
#     print (data, label)
#     break

# 3.初始化模型参数
w = nd.random_normal(shape=(num_inputs,1))
b = nd.zeros((1,))
params = [w, b]

# print (params)
# 创建梯度空间
for param in params:
    param.attach_grad()

# 4.定义模型
def net(X):
    return nd.dot(X, w) + b

# 5.定义损失函数
def square_loss(yhat, y):
    # 注意这里将y变形成yhat的形状来避免矩阵的broadcasting
    return (yhat - y.reshape(yhat.shape)) ** 2

# 6.优化
def SGD(params, lr):
    for param in params:
        param[:] = param - lr * param.grad

# 7.训练
# 模型函数
def real_fn(X):
    return 2 * X[:, 0] - 3.4 * X[:, 1] + 4.2
# 绘制损失随训练次数降低的折线图，以及预测值和真实值的散点图
def plot(losses, X, sample_size=100):
    xs = list(range(len(losses)))
    fig, axes = plt.subplots(1, 2)
    axes[0].set_title('Loss during training')
    axes[0].plot(xs, losses, '-r')
    axes[1].set_title('Estimated vs real function')
    axes[1].plot(X[:sample_size, 1].asnumpy(),
             net(X[:sample_size, :]).asnumpy(), 'or', label='Estimated')
    axes[1].plot(X[:sample_size, 1].asnumpy(),
             real_fn(X[:sample_size, :]).asnumpy(), '*g', label='Real')
    axes[1].legend()
    plt.show()

epochs = 5
learning_rate = 0.001
niter = 0
losses = []
moving_loss = 0
smoothing_constant = 0.01

# 训练
for e in range(epochs):
    total_loss = 0
    # 每个epoch
    for data, label in data_iter():
        with autograd.record():
            output = net(data) # 前向传播
            loss = square_loss(output, label)
        loss.backward() # 反向传播
        SGD(params, learning_rate) # 更新参数
        iter_loss = nd.sum(loss).asscalar() / batch_size
        total_loss += nd.sum(loss).asscalar()

        # 记录损失变化
        niter += 1
        curr_loss = nd.mean(loss).asscalar()
        moving_loss = (1 - smoothing_constant) * moving_loss + smoothing_constant * curr_loss

        losses.append(iter_loss)
        if (niter + 1) % 100 == 0:
            print("Epoch %s, batch %s. Average loss: %f" % (e, niter, total_loss / num_examples))
            plot(losses, X)

使用GPU

a = nd.array([1,2,3], ctx=mx.gpu())
b = nd.zeros((3,2), ctx=mx.gpu())

可以通过copyto和as_in_context来在设备直接传输数据。

y = x.copyto(mx.gpu())
z = x.as_in_context(mx.gpu())

这两个函数的主要区别是，如果源和目标的context一致，as_in_context不复制，而copyto总是会新建内存：

这类似与caffe中的cuda操作

float* tmp_transform_bbox = NULL;
CUDA_CHECK(cudaMalloc(&tmp_transform_bbox,  * sizeof(Dtype) * rpn_pre_nms_top_n));//修改retained_anchor_num
cudaMemcpy(tmp_transform_bbox, &transform_bbox_[transform_bbox_begin], rpn_pre_nms_top_n * sizeof(Dtype) * , cudaMemcpyDeviceToDevice);

参数获取

w = net[0].weight
b = net[0].bias
print 'name: ', net[0].name, '\nweight: ', w, '\nbias: ', b

print('weight:', w.data())
print('weight gradient', w.grad())
print('bias:', b.data())
print('bias gradient', b.grad())

params = net.collect_params()
print(params)
print(params['sequential0_dense0_bias'].data())
print(params.get('dense0_weight').data())

参数初始化

from mxnet import init
params = net.collect_params()
params.initialize(init=init.Normal(sigma=0.02), force_reinit=True)
print(net[0].weight.data(), net[0].bias.data())

6.使用中错误解决

1.python2打印权重报错

w = net[0].weight
b = net[0].bias
print('name: ', net[0].name, '\nweight: ', w, '\nbias: ', b)

把C:\Anaconda2\envs\gluon\Lib\site-packages\mxnet\gluon\parameter.py 119行改为

s = 'Parameter {name} (shape={_shape}, dtype={dtype})'

同时，如果是Python2需要把print后去掉括号。。。