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- {
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# 动态图和静态图\n",
- "目前神经网络框架分为[静态图框架和动态图框架](https://blog.csdn.net/qq_36653505/article/details/87875279),PyTorch 和 TensorFlow、Caffe 等框架最大的区别就是他们拥有不同的计算图表现形式。 TensorFlow 使用静态图,这意味着我们先定义计算图,然后不断使用它,而在 PyTorch 中,每次都会重新构建一个新的计算图。通过这次课程,我们会了解静态图和动态图之间的优缺点。\n",
- "\n",
- "对于使用者来说,两种形式的计算图有着非常大的区别,同时静态图和动态图都有他们各自的优点,比如动态图比较方便debug,使用者能够用任何他们喜欢的方式进行debug,同时非常直观,而静态图是通过先定义后运行的方式,之后再次运行的时候就不再需要重新构建计算图,所以速度会比动态图更快。"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## PyTorch"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "# pytorch\n",
- "import torch\n",
- "first_counter = torch.Tensor([0])\n",
- "second_counter = torch.Tensor([10])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "while (first_counter < second_counter):\n",
- " first_counter += 2\n",
- " second_counter += 1"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "tensor([20.])\n",
- "tensor([20.])\n"
- ]
- }
- ],
- "source": [
- "print(first_counter)\n",
- "print(second_counter)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "可以看到 PyTorch 的写法跟 Python 的写法是完全一致的,没有任何额外的学习成本\n",
- "\n",
- "上面的例子展示如何使用静态图和动态图构建 while 循环,看起来动态图的方式更加简单且直观,你觉得呢?"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.9"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
- }
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