LeNet/LeNet.ipynb

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     "start_time": "2022-06-22T01:45:01.333655Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "from d2l import torch as d2l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "de4aa4da",
   "metadata": {
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   "source": [
    "net = nn.Sequential(nn.Conv2d(1, 6, kernel_size=5, padding=2, stride=1), nn.Sigmoid(),\n",
    "                    nn.AvgPool2d(kernel_size=2),\n",
    "                    nn.Conv2d(6, 16, kernel_size=5, padding=0, stride=1), nn.Sigmoid(),\n",
    "                    nn.AvgPool2d(kernel_size=2),\n",
    "                    nn.Flatten(),\n",
    "                    nn.Linear(16*5*5, 120), nn.Sigmoid(),\n",
    "                    nn.Linear(120, 84), nn.Sigmoid(),\n",
    "                    nn.Linear(84, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ca6b3839",
   "metadata": {
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     "end_time": "2022-06-22T01:45:02.584785Z",
     "start_time": "2022-06-22T01:45:02.574079Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Conv2d output shape: \t torch.Size([1, 6, 28, 28])\n",
      "Sigmoid output shape: \t torch.Size([1, 6, 28, 28])\n",
      "AvgPool2d output shape: \t torch.Size([1, 6, 14, 14])\n",
      "Conv2d output shape: \t torch.Size([1, 16, 10, 10])\n",
      "Sigmoid output shape: \t torch.Size([1, 16, 10, 10])\n",
      "AvgPool2d output shape: \t torch.Size([1, 16, 5, 5])\n",
      "Flatten output shape: \t torch.Size([1, 400])\n",
      "Linear output shape: \t torch.Size([1, 120])\n",
      "Sigmoid output shape: \t torch.Size([1, 120])\n",
      "Linear output shape: \t torch.Size([1, 84])\n",
      "Sigmoid output shape: \t torch.Size([1, 84])\n",
      "Linear output shape: \t torch.Size([1, 10])\n"
     ]
    }
   ],
   "source": [
    "X = torch.rand(size=(1, 1, 28, 28), dtype=torch.float32)\n",
    "for layer in net:\n",
    "    X = layer(X)\n",
    "    print(layer.__class__.__name__,'output shape: \\t',X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0581c5cf",
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     "start_time": "2022-06-22T01:45:02.586687Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "batch_size = 256\n",
    "train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size=batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "03aef1e3",
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    "ExecuteTime": {
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   "source": [
    "def evaluate_accuracy_gpu(net, data_iter):\n",
    "    net.eval()\n",
    "    # 正确预测的数量，总预测的数量\n",
    "    metric = d2l.Accumulator(2)\n",
    "    with torch.no_grad():\n",
    "        for X, y in data_iter:\n",
    "            metric.add(d2l.accuracy(net(X), y), y.numel())\n",
    "    return metric[0] / metric[1]\n",
    "\n",
    "def init_weights(m):\n",
    "    if type(m) == nn.Linear or type(m) == nn.Conv2d:\n",
    "        nn.init.xavier_uniform_(m.weight)\n",
    "            \n",
    "def train_ch6(net, train_iter, test_iter, num_epochs, lr):    \n",
    "    optimizer = torch.optim.SGD(net.parameters(), lr=lr)\n",
    "    loss = nn.CrossEntropyLoss()\n",
    "    \n",
    "    animator = d2l.Animator(xlabel='epoch', xlim=[1, num_epochs],\n",
    "                            legend=['train loss', 'train acc', 'test acc'])\n",
    "    for epoch in range(num_epochs):\n",
    "        # 训练损失之和，训练准确的个数之和，样本数\n",
    "        metric = d2l.Accumulator(3)\n",
    "        \n",
    "        net.train()\n",
    "        for i, (X, y) in enumerate(train_iter):\n",
    "            optimizer.zero_grad()\n",
    "            y_hat = net(X)\n",
    "            l = loss(y_hat, y)\n",
    "            l.backward()\n",
    "            optimizer.step()\n",
    "            with torch.no_grad():\n",
    "                metric.add(l * X.shape[0], d2l.accuracy(y_hat, y), X.shape[0])\n",
    "        train_l = metric[0] / metric[2]\n",
    "        train_acc = metric[1] / metric[2]\n",
    "        test_acc = evaluate_accuracy_gpu(net, test_iter)\n",
    "        animator.add(epoch + 1, (train_l, train_acc, test_acc))\n",
    "        \n",
    "    print(f'loss {train_l:.3f}, train acc {train_acc:.3f}, test acc {test_acc:.3f}')"
   ]
  },
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     "text": [
      "loss 0.478, train acc 0.820, test acc 0.783\n"
     ]
    },
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       "<Figure size 252x180 with 1 Axes>"
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   "source": [
    "lr, num_epochs = 0.9, 10\n",
    "net.apply(init_weights)\n",
    "train_ch6(net, train_iter, test_iter, num_epochs, lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6244a394",
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    "ExecuteTime": {
     "end_time": "2022-06-22T01:49:47.269887Z",
     "start_time": "2022-06-22T01:45:49.437100Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss 0.399, train acc 0.854, test acc 0.822\n"
     ]
    },
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      "text/plain": [
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   ],
   "source": [
    "lr, num_epochs = 0.2, 50\n",
    "net.apply(init_weights)\n",
    "train_ch6(net, train_iter, test_iter, num_epochs, lr)"
   ]
  }
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