3.9.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import d2lzh as d2l\n",
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 256\n",
    "train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_inputs, num_outputs, num_hiddens = 784, 10, 256\n",
    "\n",
    "W1 = torch.randn((num_inputs, num_hiddens)) * 0.01\n",
    "b1 = torch.zeros(num_hiddens, requires_grad=True)\n",
    "W2 = torch.randn((num_hiddens, num_outputs)) * 0.01\n",
    "b2 = torch.zeros(num_outputs, requires_grad=True)\n",
    "\n",
    "W1.requires_grad = True\n",
    "W2.requires_grad = True\n",
    "params = [W1, b1, W2, b2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def relu(X):\n",
    "    return torch.clamp(X, min=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def net(X):\n",
    "    X = X.reshape((-1, num_inputs))\n",
    "    H = relu(X @ W1 + b1)\n",
    "    return H @ W2 + b2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "loss = torch.nn.CrossEntropyLoss(reduction='sum')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 1, loss 0.8047, train acc 0.700, test acc 0.804\n",
      "epoch 2, loss 0.4938, train acc 0.818, test acc 0.830\n",
      "epoch 3, loss 0.4236, train acc 0.844, test acc 0.846\n",
      "epoch 4, loss 0.3963, train acc 0.853, test acc 0.859\n",
      "epoch 5, loss 0.3715, train acc 0.863, test acc 0.847\n"
     ]
    }
   ],
   "source": [
    "num_epochs, lr = 5, 0.5\n",
    "d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size, params, lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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