MXNet是基础，Gluon是封装，两者犹如TensorFlow和Keras，不过得益于动态图机制，两者交互比TensorFlow和Keras要方便得多，其基础操作和pytorch极为相似，但是方便不少，有pytorch基础入门会很简单。
库导入写法，
from mxnet import ndarray as ndfrom mxnet import autogradfrom mxnet import gluonimport mxnet as mx
MXNet
mxnet.ndarray是整个科学计算系统的基础，整体API和numpy的nparray一致，这一点类似于pytorch，不过不同于pytorch内置变量、张量等不同数据类型，mxnet简化了只有ndarray一种，通过mxnet.autograd可以直接实现求导，十分便捷.
自动求导
x = nd.arange(4).reshape((4, 1))# 标记需要自动求导的量x.attach_grad()# 有自动求导就需要记录计算图with autograd.record(): ???y = 2 * nd.dot(x.T, x)# 反向传播输出y.backward()# 获取梯度print(‘x.grad: ‘, x.grad)
nd转化为数字
nd.asscalar()
nd与np数组互化
y = nd.array(x) # NumPy转换成NDArray。
z = y.asnumpy() # NDArray转换成NumPy。
节约内存的加法
nd.elemwise_add(x, y, out=z)
层实现
relu激活
def relu(X): ???return nd.maximum(X, 0)
全连接层
# 变量生成w = nd.random.normal(scale=1, shape=(num_inputs, 1))b = nd.zeros(shape=(1,))params = [w, b]# 变量挂载梯度for param in params: ???param.attach_grad()# 实现全连接def net(X, w, b): ???return nd.dot(X, w) + b
SGD实现
def sgd(params, lr, batch_size): ???for param in params: ???????param[:] = param - lr * param.grad / batch_size
Gluon
内存数据集加载
import mxnet as mxfrom mxnet import autograd, ndimport numpy as npnum_inputs = 2num_examples = 1000true_w = [2, -3.4]true_b = 4.2features = nd.random.normal(scale=1, shape=(num_examples, num_inputs))labels = true_w[0] * features[:, 0] + true_w[1] * features[:, 1] + true_blabels += nd.random.normal(scale=0.01, shape=labels.shape)from mxnet.gluon import data as gdatabatch_size = 10dataset = gdata.ArrayDataset(features, labels)data_iter = gdata.DataLoader(dataset, batch_size, shuffle=True)for X, y in data_iter: ???print(X, y) ???break
[[-1.74047375 ?0.26071024] [ 0.65584248 -0.50490594] [-0.97745866 -0.01658815] [-0.55589193 ?0.30666101] [-0.61393601 -2.62473822] [ 0.82654613 -0.00791582] [ 0.29560572 -1.21692061] [-0.35985938 -1.37184834] [-1.69631028 -1.74014604] [ 1.31199837 -1.96280086]]<NDArray 10x2 @cpu(0)>[ -0.14842382 ??7.22247267 ??2.30917668 ??2.0601418 ??11.89551163 ??5.87866735 ??8.94194221 ??8.15139961 ??6.72600317 ?13.50252151]<NDArray 10 @cpu(0)>
模型定义
- 序列模型生成
- 层填充
- 初始化模型参数
net = gluon.nn.Sequential()with net.name_scope(): ???net.add(gluon.nn.Dense(1))net.collect_params().initialize(mx.init.Normal(sigma=1)) ?# 模型参数初始化选择normal分布
优化器
wd参数为模型添加了L2正则化，机制为：w = w - lr*grad - wd*w
trainer = gluon.Trainer(net.collect_params(), ‘sgd‘, { ???????‘learning_rate‘: learning_rate, ‘wd‘: weight_decay})trainer.step(batch_size)需要运行在每一次反向传播之后，会更新参数，一次模拟的训练过程如下，
for e in range(epochs): ???????for data, label in data_iter_train: ???????????with autograd.record(): ???????????????output = net(data) ???????????????loss = square_loss(output, label) ???????????loss.backward() ???????????trainer.step(batch_size) ???????train_loss.append(test(net, X_train, y_train)) ???????test_loss.append(test(net, X_test, y_test))
层函数API
拉伸
nn.Flatten()
全连接层
gluon.nn.Dense(256, activation="relu")
参数表示输出节点数
损失函数class API
交叉熵
loss = gloss.SoftmaxCrossEntropyLoss()
『MXNet』第一弹_基础操作以及常见层实现
原文地址：https://www.cnblogs.com/hellcat/p/9038649.html