notebooks-1/7-softmax-regression-gluon.ipynb

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   "source": [
    "# Softmax 回归的简洁实现 \n"
   ]
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   "execution_count": 1,
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    "ExecuteTime": {
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     "start_time": "2019-07-03T22:07:03.435628Z"
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   "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"
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   "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"
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   },
   "source": [
    "损失函数，优化算法，和训练。"
   ]
  },
  {
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   "execution_count": 3,
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     "end_time": "2019-07-03T22:07:38.001921Z",
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      "text/plain": [
       "<Figure size 252x180 with 1 Axes>"
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     },
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   ],
   "source": [
    "loss = gluon.loss.SoftmaxCrossEntropyLoss()\n",
    "trainer = gluon.Trainer(net.collect_params(), \n",
    "                        'sgd', {'learning_rate': 0.1})\n",
    "d2l.train_ch3(net, train_iter, test_iter, loss, 10, trainer)"
   ]
  }
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