wehub-resource-sync
1312 lines
71 KiB
Plaintext
1312 lines
71 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# 3.6 softmax回归的从零开始实现"
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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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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0.4.1\n",
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"0.2.1\n"
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]
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}
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],
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"source": [
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"import torch\n",
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"import torchvision\n",
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"import numpy as np\n",
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"import sys\n",
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"sys.path.append(\"..\") # 为了导入上层目录的d2lzh_pytorch\n",
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"import d2lzh_pytorch as d2l\n",
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"\n",
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"print(torch.__version__)\n",
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"print(torchvision.__version__)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 3.6.1 获取和读取数据"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"batch_size = 256\n",
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"train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 3.6.2 初始化模型参数"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"num_inputs = 784\n",
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"num_outputs = 10\n",
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"\n",
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"W = torch.tensor(np.random.normal(0, 0.01, (num_inputs, num_outputs)), dtype=torch.float)\n",
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"b = torch.zeros(num_outputs, dtype=torch.float)"
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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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"metadata": {},
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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., 0., 0., 0., 0., 0., 0., 0., 0., 0.], requires_grad=True)"
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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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"W.requires_grad_(requires_grad=True)\n",
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"b.requires_grad_(requires_grad=True) "
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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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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"tensor([[5, 7, 9]])\n",
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"tensor([[ 6],\n",
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" [15]])\n"
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]
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}
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],
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"source": [
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"X = torch.tensor([[1, 2, 3], [4, 5, 6]])\n",
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"print(X.sum(dim=0, keepdim=True))\n",
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"print(X.sum(dim=1, keepdim=True))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 3.6.3 实现softmax运算"
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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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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"def softmax(X):\n",
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" X_exp = X.exp()\n",
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" partition = X_exp.sum(dim=1, keepdim=True)\n",
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" return X_exp / partition # 这里应用了广播机制"
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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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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"tensor([[0.1195, 0.2642, 0.2857, 0.1721, 0.1585],\n",
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" [0.1918, 0.1353, 0.1837, 0.3329, 0.1562]]) tensor([1., 1.])\n"
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]
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}
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],
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"source": [
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"X = torch.rand((2, 5))\n",
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"X_prob = softmax(X)\n",
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"print(X_prob, X_prob.sum(dim=1))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 3.6.4 定义模型"
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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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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"def net(X):\n",
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" return softmax(torch.mm(X.view((-1, num_inputs)), W) + b)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 3.6.5 定义损失函数"
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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": 9,
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"metadata": {},
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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([[0.1000],\n",
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" [0.5000]])"
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]
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},
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"execution_count": 9,
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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_hat = torch.tensor([[0.1, 0.3, 0.6], [0.3, 0.2, 0.5]])\n",
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"y = torch.LongTensor([0, 2])\n",
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"y_hat.gather(1, y.view(-1, 1))"
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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": 10,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"def cross_entropy(y_hat, y):\n",
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" return - torch.log(y_hat.gather(1, y.view(-1, 1)))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 3.6.6 计算分类准确率"
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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": 11,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"def accuracy(y_hat, y):\n",
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" return (y_hat.argmax(dim=1) == y).float().mean().item()"
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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": 12,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0.5\n"
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]
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}
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],
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"source": [
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"print(accuracy(y_hat, y))"
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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": 13,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# 本函数已保存在d2lzh_pytorch包中方便以后使用。该函数将被逐步改进：它的完整实现将在“图像增广”一节中描述\n",
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"def evaluate_accuracy(data_iter, net):\n",
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" acc_sum, n = 0.0, 0\n",
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" for X, y in data_iter:\n",
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" acc_sum += (net(X).argmax(dim=1) == y).float().sum().item()\n",
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" n += y.shape[0]\n",
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" return acc_sum / 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": 14,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0.0681\n"
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]
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}
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],
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"source": [
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"print(evaluate_accuracy(test_iter, net))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 3.6.7 训练模型"
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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": 15,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"epoch 1, loss 0.7878, train acc 0.749, test acc 0.794\n",
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"epoch 2, loss 0.5702, train acc 0.814, test acc 0.813\n",
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"epoch 3, loss 0.5252, train acc 0.827, test acc 0.819\n",
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"epoch 4, loss 0.5010, train acc 0.833, test acc 0.824\n",
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"epoch 5, loss 0.4858, train acc 0.836, test acc 0.815\n"
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]
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}
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],
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"source": [
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"num_epochs, lr = 5, 0.1\n",
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"\n",
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"# 本函数已保存在d2lzh_pytorch包中方便以后使用\n",
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"def train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size,\n",
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" params=None, lr=None, optimizer=None):\n",
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" for epoch in range(num_epochs):\n",
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" train_l_sum, train_acc_sum, n = 0.0, 0.0, 0\n",
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" for X, y in train_iter:\n",
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" y_hat = net(X)\n",
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" l = loss(y_hat, y).sum()\n",
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" \n",
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" # 梯度清零\n",
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" if optimizer is not None:\n",
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" optimizer.zero_grad()\n",
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" elif params is not None and params[0].grad is not None:\n",
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" for param in params:\n",
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" param.grad.data.zero_()\n",
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" \n",
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" l.backward()\n",
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" if optimizer is None:\n",
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" d2l.sgd(params, lr, batch_size)\n",
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" else:\n",
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" optimizer.step() # “softmax回归的简洁实现”一节将用到\n",
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" \n",
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" \n",
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" train_l_sum += l.item()\n",
|
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" train_acc_sum += (y_hat.argmax(dim=1) == y).sum().item()\n",
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" n += y.shape[0]\n",
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" test_acc = evaluate_accuracy(test_iter, net)\n",
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" print('epoch %d, loss %.4f, train acc %.3f, test acc %.3f'\n",
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" % (epoch + 1, train_l_sum / n, train_acc_sum / n, test_acc))\n",
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"\n",
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"train_ch3(net, train_iter, test_iter, cross_entropy, num_epochs, batch_size, [W, b], lr)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 3.6.8 预测"
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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": 16,
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"metadata": {},
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"outputs": [
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