976 lines
34 KiB
Plaintext
976 lines
34 KiB
Plaintext
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"source": [
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"# 多层感知机的简洁实现\n",
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":label:`sec_mlp_concise`\n",
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"\n",
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"本节将介绍(**通过高级API更简洁地实现多层感知机**)。\n"
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]
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},
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"cell_type": "code",
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"id": "f4b9d183",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T07:04:20.711610Z",
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},
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"origin_pos": 2,
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"tab": [
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"pytorch"
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]
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},
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"outputs": [],
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"source": [
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"import torch\n",
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"from torch import nn\n",
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"from d2l import torch as d2l"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d1b8af0c",
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"metadata": {
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"origin_pos": 5
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},
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"source": [
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"## 模型\n",
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"\n",
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"与softmax回归的简洁实现（ :numref:`sec_softmax_concise`）相比，\n",
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"唯一的区别是我们添加了2个全连接层（之前我们只添加了1个全连接层）。\n",
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"第一层是[**隐藏层**]，它(**包含256个隐藏单元，并使用了ReLU激活函数**)。\n",
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"第二层是输出层。\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "a11cfbe9",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T07:04:22.719981Z",
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"iopub.status.busy": "2023-08-18T07:04:22.719298Z",
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"iopub.status.idle": "2023-08-18T07:04:22.748628Z",
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"shell.execute_reply": "2023-08-18T07:04:22.747813Z"
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},
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"origin_pos": 7,
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"tab": [
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"pytorch"
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]
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},
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"outputs": [],
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"source": [
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"net = nn.Sequential(nn.Flatten(),\n",
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" nn.Linear(784, 256),\n",
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" nn.ReLU(),\n",
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" nn.Linear(256, 10))\n",
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"\n",
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"def init_weights(m):\n",
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" if type(m) == nn.Linear:\n",
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" nn.init.normal_(m.weight, std=0.01)\n",
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"\n",
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"net.apply(init_weights);"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f5aceed6",
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"metadata": {
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"origin_pos": 10
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},
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"source": [
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"[**训练过程**]的实现与我们实现softmax回归时完全相同，\n",
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"这种模块化设计使我们能够将与模型架构有关的内容独立出来。\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "b23e8ab9",
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"metadata": {
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"execution": {
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"origin_pos": 12,
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"tab": [
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"pytorch"
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]
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},
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"outputs": [],
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"source": [
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"batch_size, lr, num_epochs = 256, 0.1, 10\n",
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"loss = nn.CrossEntropyLoss(reduction='none')\n",
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"trainer = torch.optim.SGD(net.parameters(), lr=lr)"
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]
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},
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{
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"id": "78ac9bf1",
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"metadata": {
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"origin_pos": 15,
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"tab": [
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"pytorch"
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"text/plain": [
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"source": [
|
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"train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)\n",
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"d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, trainer)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9b636c57",
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"metadata": {
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},
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"source": [
|
|
"## 小结\n",
|
|
"\n",
|
|
"* 我们可以使用高级API更简洁地实现多层感知机。\n",
|
|
"* 对于相同的分类问题，多层感知机的实现与softmax回归的实现相同，只是多层感知机的实现里增加了带有激活函数的隐藏层。\n",
|
|
"\n",
|
|
"## 练习\n",
|
|
"\n",
|
|
"1. 尝试添加不同数量的隐藏层（也可以修改学习率），怎么样设置效果最好？\n",
|
|
"1. 尝试不同的激活函数，哪个效果最好？\n",
|
|
"1. 尝试不同的方案来初始化权重，什么方法效果最好？\n"
|
|
]
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},
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{
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"cell_type": "markdown",
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"id": "36201fb3",
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"origin_pos": 18,
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"tab": [
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"pytorch"
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]
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},
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"source": [
|
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"[Discussions](https://discuss.d2l.ai/t/1802)\n"
|
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]
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}
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],
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