{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Softmax 回归的简洁实现 \n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2019-07-03T22:07:06.023766Z",
"start_time": "2019-07-03T22:07:03.435628Z"
},
"attributes": {
"classes": [],
"id": "",
"n": "1"
}
},
"outputs": [],
"source": [
"import d2l\n",
"from mxnet import gluon, init, npx\n",
"from mxnet.gluon import nn\n",
"npx.set_np()\n",
"\n",
"train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size=256)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"模型和初始化。"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"ExecuteTime": {
"end_time": "2019-07-03T22:07:06.034492Z",
"start_time": "2019-07-03T22:07:06.027213Z"
},
"attributes": {
"classes": [],
"id": "",
"n": "3"
}
},
"outputs": [],
"source": [
"net = nn.Sequential()\n",
"net.add(nn.Dense(10))\n",
"net.initialize(init.Normal(sigma=0.01))"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"损失函数,优化算法,和训练。"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"ExecuteTime": {
"end_time": "2019-07-03T22:07:38.001921Z",
"start_time": "2019-07-03T22:07:06.036621Z"
},
"attributes": {
"classes": [],
"id": "",
"n": "5"
},
"scrolled": true
},
"outputs": [
{
"data": {
"image/svg+xml": [
"\n",
"\n",
"\n",
"\n"
],
"text/plain": [
"