408 lines
10 KiB
Plaintext
408 lines
10 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "bec47e64",
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"metadata": {
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"origin_pos": 0
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},
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"source": [
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"# 读写文件\n",
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"\n",
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"到目前为止，我们讨论了如何处理数据，\n",
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"以及如何构建、训练和测试深度学习模型。\n",
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"然而，有时我们希望保存训练的模型，\n",
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"以备将来在各种环境中使用（比如在部署中进行预测）。\n",
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"此外，当运行一个耗时较长的训练过程时，\n",
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"最佳的做法是定期保存中间结果，\n",
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"以确保在服务器电源被不小心断掉时，我们不会损失几天的计算结果。\n",
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"因此，现在是时候学习如何加载和存储权重向量和整个模型了。\n",
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"\n",
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"## (**加载和保存张量**)\n",
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"\n",
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"对于单个张量，我们可以直接调用`load`和`save`函数分别读写它们。\n",
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"这两个函数都要求我们提供一个名称，`save`要求将要保存的变量作为输入。\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": 1,
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"id": "9b319fd3",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T06:56:42.668559Z",
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"iopub.status.busy": "2023-08-18T06:56:42.667248Z",
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"iopub.status.idle": "2023-08-18T06:56:43.728764Z",
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"shell.execute_reply": "2023-08-18T06:56:43.727885Z"
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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 torch.nn import functional as F\n",
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"\n",
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"x = torch.arange(4)\n",
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"torch.save(x, 'x-file')"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e4f44ac7",
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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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"我们现在可以将存储在文件中的数据读回内存。\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": "1ab53461",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T06:56:43.733002Z",
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"iopub.status.busy": "2023-08-18T06:56:43.732347Z",
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"iopub.status.idle": "2023-08-18T06:56:43.741208Z",
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"shell.execute_reply": "2023-08-18T06:56:43.740416Z"
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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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{
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"data": {
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"text/plain": [
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"tensor([0, 1, 2, 3])"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"x2 = torch.load('x-file')\n",
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"x2"
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]
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},
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{
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"cell_type": "markdown",
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"id": "44d4a111",
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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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"我们可以[**存储一个张量列表，然后把它们读回内存。**]\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": "81027fe1",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T06:56:43.744676Z",
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"iopub.status.busy": "2023-08-18T06:56:43.744140Z",
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"iopub.status.idle": "2023-08-18T06:56:43.751376Z",
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"shell.execute_reply": "2023-08-18T06:56:43.750630Z"
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},
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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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{
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"data": {
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"text/plain": [
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"(tensor([0, 1, 2, 3]), tensor([0., 0., 0., 0.]))"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"y = torch.zeros(4)\n",
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"torch.save([x, y],'x-files')\n",
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"x2, y2 = torch.load('x-files')\n",
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"(x2, y2)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b060dd48",
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"metadata": {
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"origin_pos": 15
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},
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"source": [
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"我们甚至可以(**写入或读取从字符串映射到张量的字典**)。\n",
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"当我们要读取或写入模型中的所有权重时，这很方便。\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": 4,
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"id": "fde1cb33",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T06:56:43.754777Z",
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"iopub.status.busy": "2023-08-18T06:56:43.754313Z",
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"iopub.status.idle": "2023-08-18T06:56:43.761150Z",
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"shell.execute_reply": "2023-08-18T06:56:43.760369Z"
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},
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"origin_pos": 17,
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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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{
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"data": {
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"text/plain": [
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"{'x': tensor([0, 1, 2, 3]), 'y': tensor([0., 0., 0., 0.])}"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"mydict = {'x': x, 'y': y}\n",
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"torch.save(mydict, 'mydict')\n",
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"mydict2 = torch.load('mydict')\n",
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"mydict2"
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]
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},
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{
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"cell_type": "markdown",
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"id": "afa857bf",
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"metadata": {
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"origin_pos": 20
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},
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"source": [
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"## [**加载和保存模型参数**]\n",
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"\n",
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"保存单个权重向量（或其他张量）确实有用，\n",
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"但是如果我们想保存整个模型，并在以后加载它们，\n",
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"单独保存每个向量则会变得很麻烦。\n",
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"毕竟，我们可能有数百个参数散布在各处。\n",
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"因此，深度学习框架提供了内置函数来保存和加载整个网络。\n",
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"需要注意的一个重要细节是，这将保存模型的参数而不是保存整个模型。\n",
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"例如，如果我们有一个3层多层感知机，我们需要单独指定架构。\n",
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"因为模型本身可以包含任意代码，所以模型本身难以序列化。\n",
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"因此，为了恢复模型，我们需要用代码生成架构，\n",
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"然后从磁盘加载参数。\n",
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"让我们从熟悉的多层感知机开始尝试一下。\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": 5,
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"id": "2672b5c2",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T06:56:43.764609Z",
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"iopub.status.busy": "2023-08-18T06:56:43.764090Z",
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"iopub.status.idle": "2023-08-18T06:56:43.773070Z",
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"shell.execute_reply": "2023-08-18T06:56:43.772277Z"
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},
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"origin_pos": 22,
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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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"class MLP(nn.Module):\n",
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" def __init__(self):\n",
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" super().__init__()\n",
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" self.hidden = nn.Linear(20, 256)\n",
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" self.output = nn.Linear(256, 10)\n",
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"\n",
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" def forward(self, x):\n",
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" return self.output(F.relu(self.hidden(x)))\n",
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"\n",
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"net = MLP()\n",
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"X = torch.randn(size=(2, 20))\n",
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"Y = net(X)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "697ceed0",
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"metadata": {
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"origin_pos": 25
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},
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"source": [
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"接下来，我们[**将模型的参数存储在一个叫做“mlp.params”的文件中。**]\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": 6,
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"id": "a53c1315",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T06:56:43.776452Z",
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"iopub.status.busy": "2023-08-18T06:56:43.775942Z",
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"iopub.status.idle": "2023-08-18T06:56:43.780387Z",
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"shell.execute_reply": "2023-08-18T06:56:43.779636Z"
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},
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"origin_pos": 27,
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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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"torch.save(net.state_dict(), 'mlp.params')"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b6df754a",
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"metadata": {
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"origin_pos": 30
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},
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"source": [
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"为了恢复模型，我们[**实例化了原始多层感知机模型的一个备份。**]\n",
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"这里我们不需要随机初始化模型参数，而是(**直接读取文件中存储的参数。**)\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": 7,
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"id": "da5e1b3f",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T06:56:43.783850Z",
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"iopub.status.busy": "2023-08-18T06:56:43.783240Z",
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"iopub.status.idle": "2023-08-18T06:56:43.789905Z",
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"shell.execute_reply": "2023-08-18T06:56:43.789164Z"
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},
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"origin_pos": 32,
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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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{
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"data": {
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"text/plain": [
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"MLP(\n",
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" (hidden): Linear(in_features=20, out_features=256, bias=True)\n",
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" (output): Linear(in_features=256, out_features=10, bias=True)\n",
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")"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"clone = MLP()\n",
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"clone.load_state_dict(torch.load('mlp.params'))\n",
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"clone.eval()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "65076662",
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"metadata": {
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"origin_pos": 35
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},
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"source": [
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"由于两个实例具有相同的模型参数，在输入相同的`X`时，\n",
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"两个实例的计算结果应该相同。\n",
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"让我们来验证一下。\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": 8,
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"id": "a25ba1f1",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T06:56:43.793400Z",
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"iopub.status.busy": "2023-08-18T06:56:43.792788Z",
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"iopub.status.idle": "2023-08-18T06:56:43.798329Z",
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"shell.execute_reply": "2023-08-18T06:56:43.797576Z"
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},
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"origin_pos": 37,
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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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{
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"data": {
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"text/plain": [
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"tensor([[True, True, True, True, True, True, True, True, True, True],\n",
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" [True, True, True, True, True, True, True, True, True, True]])"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"Y_clone = clone(X)\n",
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"Y_clone == Y"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7a65b1e2",
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"metadata": {
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"origin_pos": 39
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},
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"source": [
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"## 小结\n",
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"\n",
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"* `save`和`load`函数可用于张量对象的文件读写。\n",
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"* 我们可以通过参数字典保存和加载网络的全部参数。\n",
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"* 保存架构必须在代码中完成，而不是在参数中完成。\n",
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"\n",
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"## 练习\n",
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"\n",
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"1. 即使不需要将经过训练的模型部署到不同的设备上，存储模型参数还有什么实际的好处？\n",
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"1. 假设我们只想复用网络的一部分，以将其合并到不同的网络架构中。比如想在一个新的网络中使用之前网络的前两层，该怎么做？\n",
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"1. 如何同时保存网络架构和参数？需要对架构加上什么限制？\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d803f301",
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"metadata": {
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"origin_pos": 41,
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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/1839)\n"
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]
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}
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],
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"metadata": {
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"language_info": {
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"name": "python"
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},
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"required_libs": []
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},
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"nbformat": 4,
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"nbformat_minor": 5
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} |