tensors/2_birds_airplanes.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qG81h9_eSjcp",
        "outputId": "a81c9ad1-98be-4214-aa3f-7a4e08f2992e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<torch._C.Generator at 0x7d33880a8330>"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "%matplotlib inline\n",
        "from matplotlib import pyplot as plt\n",
        "import numpy as np\n",
        "import torch\n",
        "\n",
        "torch.set_printoptions(edgeitems=2)\n",
        "torch.manual_seed(123)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "oLQcsQw4Sjcv"
      },
      "outputs": [],
      "source": [
        "class_names = ['airplane','automobile','bird','cat','deer',\n",
        "               'dog','frog','horse','ship','truck']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n4Q7RkRLSjcx",
        "outputId": "af313626-3c0a-4f1e-d2f2-7ad6bfd3fe20"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ../data-unversioned/p1ch7/cifar-10-python.tar.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 170498071/170498071 [00:02<00:00, 58731254.54it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ../data-unversioned/p1ch7/cifar-10-python.tar.gz to ../data-unversioned/p1ch7/\n"
          ]
        }
      ],
      "source": [
        "from torchvision import datasets, transforms\n",
        "data_path = '../data-unversioned/p1ch7/'\n",
        "cifar10 = datasets.CIFAR10(\n",
        "    data_path, train=True, download=True,\n",
        "    transform=transforms.Compose([\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize((0.4915, 0.4823, 0.4468),\n",
        "                             (0.2470, 0.2435, 0.2616))\n",
        "    ]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "ioj3f13DSjcy"
      },
      "outputs": [],
      "source": [
        "cifar10_val = datasets.CIFAR10(\n",
        "    data_path, train=False, download=False,\n",
        "    transform=transforms.Compose([\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize((0.4915, 0.4823, 0.4468),\n",
        "                             (0.2470, 0.2435, 0.2616))\n",
        "    ]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "Rs5DhDbGSjcz"
      },
      "outputs": [],
      "source": [
        "label_map = {0: 0, 2: 1}\n",
        "class_names = ['airplane', 'bird']\n",
        "cifar2 = [(img, label_map[label])\n",
        "          for img, label in cifar10\n",
        "          if label in [0, 2]]\n",
        "cifar2_val = [(img, label_map[label])\n",
        "              for img, label in cifar10_val\n",
        "              if label in [0, 2]]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "qJyR5EkCSjc0"
      },
      "outputs": [],
      "source": [
        "import torch.nn as nn\n",
        "\n",
        "n_out = 2\n",
        "\n",
        "model = nn.Sequential(\n",
        "            nn.Linear(\n",
        "                3072,  # <1>\n",
        "                512,   # <2>\n",
        "            ),\n",
        "            nn.Tanh(),\n",
        "            nn.Linear(\n",
        "                512,   # <2>\n",
        "                n_out, # <3>\n",
        "            )\n",
        "        )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "FDlCE5Z-Sjc1"
      },
      "outputs": [],
      "source": [
        "def softmax(x):\n",
        "    return torch.exp(x) / torch.exp(x).sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O-kD9iGiSjc2",
        "outputId": "0647d5aa-9916-4d33-c932-8af741df01c8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([0.0900, 0.2447, 0.6652])"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "x = torch.tensor([1.0, 2.0, 3.0])\n",
        "\n",
        "softmax(x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HoF0TEmDSjc3",
        "outputId": "663b3ac8-4d54-438f-b872-bc29273137a2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(1.)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "softmax(x).sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3ohECrKQSjc4",
        "outputId": "91eb1093-a1d8-42eb-bc61-af42e47edcf9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.0900, 0.2447, 0.6652],\n",
              "        [0.0900, 0.2447, 0.6652]])"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "softmax = nn.Softmax(dim=1)\n",
        "\n",
        "x = torch.tensor([[1.0, 2.0, 3.0],\n",
        "                  [1.0, 2.0, 3.0]])\n",
        "\n",
        "softmax(x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "Kgc8jHk3Sjc5"
      },
      "outputs": [],
      "source": [
        "model = nn.Sequential(\n",
        "            nn.Linear(3072, 512),\n",
        "            nn.Tanh(),\n",
        "            nn.Linear(512, 2),\n",
        "            nn.Softmax(dim=1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "rd578COSSjc5",
        "outputId": "855e5bd3-a152-44de-c445-c774d000ee89"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "\n",
        "img, _ = cifar2[0]\n",
        "\n",
        "plt.imshow(img.permute(1, 2, 0))\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "O-eXIpT4Sjc6"
      },
      "outputs": [],
      "source": [
        "img_batch = img.view(-1).unsqueeze(0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yoqfX_KbSjc6",
        "outputId": "98198dda-796e-4beb-f68b-3f5140f00ede"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.4784, 0.5216]], grad_fn=<SoftmaxBackward0>)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "out = model(img_batch)\n",
        "out"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "mkTZBqtXSjc6",
        "outputId": "6d58ff75-f374-481e-ebf9-14ad04fd8e32",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([1])"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "_, index = torch.max(out, dim=1)\n",
        "\n",
        "index"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "heC5SoPYSjc7",
        "outputId": "89deb7db-608b-460a-dad5-01510ff8f521",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[1., 0.],\n",
              "        [1., 0.],\n",
              "        [0., 1.],\n",
              "        [0., 1.]])"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "out = torch.tensor([\n",
        "    [0.6, 0.4],\n",
        "    [0.9, 0.1],\n",
        "    [0.3, 0.7],\n",
        "    [0.2, 0.8],\n",
        "])\n",
        "class_index = torch.tensor([0, 0, 1, 1]).unsqueeze(1)\n",
        "\n",
        "truth = torch.zeros((4,2))\n",
        "truth.scatter_(dim=1, index=class_index, value=1.0)\n",
        "truth"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GRGuzPhISjc7",
        "outputId": "1048a058-cc30-4cd4-f0ac-15aaeb3a1ab9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor(0.1500)"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "def mse(out):\n",
        "    return ((out - truth) ** 2).sum(dim=1).mean()\n",
        "mse(out)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "j7u7xiLRSjc8",
        "outputId": "fdebf816-8fc4-494d-a2ba-e1876d9bb517"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([[0.6000],\n",
              "        [0.9000],\n",
              "        [0.7000],\n",
              "        [0.8000]])"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "out.gather(dim=1, index=class_index)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "K3E0SYG4Sjc8",
        "outputId": "56292d60-6e4b-45ea-ef64-6b4bdbf98a50"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([0.3024])"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "def likelihood(out):\n",
        "    prod = 1.0\n",
        "    for x in out.gather(dim=1, index=class_index):\n",
        "        prod *= x\n",
        "    return prod\n",
        "\n",
        "likelihood(out)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0lx7ewf7Sjc9",
        "outputId": "cfd7d1f5-7422-41ae-bdf4-ceb473362b05"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([1.1960])"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "def neg_log_likelihood(out):\n",
        "    return -likelihood(out).log()\n",
        "\n",
        "neg_log_likelihood(out)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JvneFI6uSjc9",
        "outputId": "d2b38895-b69a-47eb-c556-0ce140515b15"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([0.0750, 0.1500, 0.2500, 0.4750])"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "out0 = out.clone().detach()\n",
        "out0[0] = torch.tensor([0.9, 0.1]) # more right\n",
        "\n",
        "out2 = out.clone().detach()\n",
        "out2[0] = torch.tensor([0.4, 0.6]) # slightly wrong\n",
        "\n",
        "out3 = out.clone().detach()\n",
        "out3[0] = torch.tensor([0.1, 0.9]) # very wrong\n",
        "\n",
        "mse_comparison = torch.tensor([mse(o) for o in [out0, out, out2, out3]])\n",
        "mse_comparison"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WKuqH6XrSjc-",
        "outputId": "22092d93-8665-49a8-a374-d669210d38af"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([-50.0000,   0.0000,  66.6667, 216.6667])"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "((mse_comparison / mse_comparison[1]) - 1) * 100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8pru9TjvSjc-",
        "outputId": "9eebc220-26a5-4313-8edf-9c7d852c2c01"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([0.7905, 1.1960, 1.6015, 2.9878])"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "nll_comparison = torch.tensor([neg_log_likelihood(o)\n",
        "                               for o in [out0, out, out2, out3]])\n",
        "nll_comparison"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yLsfgPurSjc_",
        "outputId": "5c9aa39d-4282-4005-d918-cab9bcfc76e4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([-33.9016,   0.0000,  33.9016, 149.8121])"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "((nll_comparison / nll_comparison[1]) - 1) * 100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tV6c2Hm0Sjc_",
        "outputId": "44875cd8-4838-4dc5-d046-e02d8b5fccfc"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([[0., 1.]])"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "softmax = nn.Softmax(dim=1)\n",
        "\n",
        "log_softmax = nn.LogSoftmax(dim=1)\n",
        "\n",
        "x = torch.tensor([[0.0, 104.0]])\n",
        "\n",
        "softmax(x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rVQe3tUJSjdA",
        "outputId": "3c19b375-6002-4839-c461-ed948ef2ff49"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([[0., 1.]])"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "softmax = nn.Softmax(dim=1)\n",
        "\n",
        "log_softmax = nn.LogSoftmax(dim=1)\n",
        "\n",
        "x = torch.tensor([[0.0, 104.0]])\n",
        "\n",
        "softmax(x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JP_DZiC_SjdA",
        "outputId": "218107e7-fc73-4be7-da80-c12be0686cf5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([[-inf, 0.]])"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "torch.log(softmax(x))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ha9nqrzLSjdB",
        "outputId": "84638c8e-a842-4e09-81ca-fa43719079dd"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([[-104.,    0.]])"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "log_softmax(x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GDIvQ2OcSjdB",
        "outputId": "b709bcf8-ebf8-42a0-dd54-9a25066a9505"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([[0., 1.]])"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "torch.exp(log_softmax(x))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SM14-96GSjdB"
      },
      "outputs": [],
      "source": [
        "model = nn.Sequential(\n",
        "            nn.Linear(3072, 512),\n",
        "            nn.Tanh(),\n",
        "            nn.Linear(512, 2),\n",
        "            nn.LogSoftmax(dim=1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "byJh-C3LSjdC"
      },
      "outputs": [],
      "source": [
        "loss = nn.NLLLoss()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UtkgyAeVSjdC",
        "outputId": "0c006b8d-7d6b-49b9-f00d-fd8bfc151a9e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor(0.5077, grad_fn=<NllLossBackward>)"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "img, label = cifar2[0]\n",
        "\n",
        "out = model(img.view(-1).unsqueeze(0))\n",
        "\n",
        "loss(out, torch.tensor([label]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "E5rgE3d1SjdD",
        "outputId": "0340fb13-3c2f-4cdf-87ce-35fe2f6c04d1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 0, Loss: 5.347057\n",
            "Epoch: 1, Loss: 7.705317\n",
            "Epoch: 2, Loss: 6.510838\n",
            "Epoch: 3, Loss: 9.557189\n",
            "Epoch: 4, Loss: 4.151933\n",
            "Epoch: 5, Loss: 5.636873\n",
            "Epoch: 6, Loss: 6.531207\n",
            "Epoch: 7, Loss: 20.450516\n",
            "Epoch: 8, Loss: 5.072948\n",
            "Epoch: 9, Loss: 4.941860\n",
            "Epoch: 10, Loss: 6.445535\n",
            "Epoch: 11, Loss: 4.580799\n",
            "Epoch: 12, Loss: 6.660308\n",
            "Epoch: 13, Loss: 9.436373\n",
            "Epoch: 14, Loss: 16.786476\n",
            "Epoch: 15, Loss: 8.349138\n",
            "Epoch: 16, Loss: 8.176860\n",
            "Epoch: 17, Loss: 5.862664\n",
            "Epoch: 18, Loss: 8.218906\n",
            "Epoch: 19, Loss: 13.296558\n",
            "Epoch: 20, Loss: 7.313433\n",
            "Epoch: 21, Loss: 4.585245\n",
            "Epoch: 22, Loss: 11.706884\n",
            "Epoch: 23, Loss: 18.208710\n",
            "Epoch: 24, Loss: 0.343157\n",
            "Epoch: 25, Loss: 9.255491\n",
            "Epoch: 26, Loss: 10.466807\n",
            "Epoch: 27, Loss: 12.226366\n",
            "Epoch: 28, Loss: 12.728527\n",
            "Epoch: 29, Loss: 9.777843\n",
            "Epoch: 30, Loss: 6.128856\n",
            "Epoch: 31, Loss: 13.284330\n",
            "Epoch: 32, Loss: 10.321814\n",
            "Epoch: 33, Loss: 2.928349\n",
            "Epoch: 34, Loss: 8.623670\n",
            "Epoch: 35, Loss: 12.719531\n",
            "Epoch: 36, Loss: 4.030444\n",
            "Epoch: 37, Loss: 4.621825\n",
            "Epoch: 38, Loss: 13.210777\n",
            "Epoch: 39, Loss: 14.217413\n",
            "Epoch: 40, Loss: 3.880259\n",
            "Epoch: 41, Loss: 13.189833\n",
            "Epoch: 42, Loss: 17.787762\n",
            "Epoch: 43, Loss: 3.953930\n",
            "Epoch: 44, Loss: 0.640078\n",
            "Epoch: 45, Loss: 9.262226\n",
            "Epoch: 46, Loss: 7.383645\n",
            "Epoch: 47, Loss: 5.352252\n",
            "Epoch: 48, Loss: 11.515299\n",
            "Epoch: 49, Loss: 12.266010\n",
            "Epoch: 50, Loss: 12.210896\n",
            "Epoch: 51, Loss: 3.987965\n",
            "Epoch: 52, Loss: 12.570765\n",
            "Epoch: 53, Loss: 13.025002\n",
            "Epoch: 54, Loss: 13.747946\n",
            "Epoch: 55, Loss: 6.783926\n",
            "Epoch: 56, Loss: 11.822943\n",
            "Epoch: 57, Loss: 8.200066\n",
            "Epoch: 58, Loss: 9.206728\n",
            "Epoch: 59, Loss: 7.715425\n",
            "Epoch: 60, Loss: 5.571069\n",
            "Epoch: 61, Loss: 13.017315\n",
            "Epoch: 62, Loss: 10.307802\n",
            "Epoch: 63, Loss: 2.660404\n",
            "Epoch: 64, Loss: 11.096642\n",
            "Epoch: 65, Loss: 5.284830\n",
            "Epoch: 66, Loss: 8.374750\n",
            "Epoch: 67, Loss: 1.418676\n",
            "Epoch: 68, Loss: 9.891462\n",
            "Epoch: 69, Loss: 9.079073\n",
            "Epoch: 70, Loss: 6.453581\n",
            "Epoch: 71, Loss: 8.293860\n",
            "Epoch: 72, Loss: 4.585221\n",
            "Epoch: 73, Loss: 14.174129\n",
            "Epoch: 74, Loss: 6.072280\n",
            "Epoch: 75, Loss: 5.925417\n",
            "Epoch: 76, Loss: 0.260600\n",
            "Epoch: 77, Loss: 3.055498\n",
            "Epoch: 78, Loss: 0.347163\n",
            "Epoch: 79, Loss: 3.497080\n",
            "Epoch: 80, Loss: 6.615281\n",
            "Epoch: 81, Loss: 8.944511\n",
            "Epoch: 82, Loss: 10.230938\n",
            "Epoch: 83, Loss: 6.776264\n",
            "Epoch: 84, Loss: 10.169885\n",
            "Epoch: 85, Loss: 7.014330\n",
            "Epoch: 86, Loss: 3.467798\n",
            "Epoch: 87, Loss: 3.772486\n",
            "Epoch: 88, Loss: 13.495383\n",
            "Epoch: 89, Loss: 11.781836\n",
            "Epoch: 90, Loss: 6.853724\n",
            "Epoch: 91, Loss: 3.313806\n",
            "Epoch: 92, Loss: 7.867707\n",
            "Epoch: 93, Loss: 16.117371\n",
            "Epoch: 94, Loss: 15.077475\n",
            "Epoch: 95, Loss: 17.807060\n",
            "Epoch: 96, Loss: 16.376089\n",
            "Epoch: 97, Loss: 9.348265\n",
            "Epoch: 98, Loss: 18.044790\n",
            "Epoch: 99, Loss: 15.565783\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "\n",
        "model = nn.Sequential(\n",
        "            nn.Linear(3072, 512),\n",
        "            nn.Tanh(),\n",
        "            nn.Linear(512, 2),\n",
        "            nn.LogSoftmax(dim=1))\n",
        "\n",
        "learning_rate = 1e-2\n",
        "\n",
        "optimizer = optim.SGD(model.parameters(), lr=learning_rate)\n",
        "\n",
        "loss_fn = nn.NLLLoss()\n",
        "\n",
        "n_epochs = 100\n",
        "\n",
        "for epoch in range(n_epochs):\n",
        "    for img, label in cifar2:\n",
        "        out = model(img.view(-1).unsqueeze(0))\n",
        "        loss = loss_fn(out, torch.tensor([label]))\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "    print(\"Epoch: %d, Loss: %f\" % (epoch, float(loss)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Yy2sMNM9SjdE"
      },
      "outputs": [],
      "source": [
        "train_loader = torch.utils.data.DataLoader(cifar2, batch_size=64,\n",
        "                                           shuffle=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9nXFcty_SjdE",
        "outputId": "fb9d5a5c-a6b9-420b-b02d-d8282eedca09"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 0, Loss: 0.604063\n",
            "Epoch: 1, Loss: 0.597974\n",
            "Epoch: 2, Loss: 0.271415\n",
            "Epoch: 3, Loss: 0.451056\n",
            "Epoch: 4, Loss: 0.629758\n",
            "Epoch: 5, Loss: 0.458762\n",
            "Epoch: 6, Loss: 0.277813\n",
            "Epoch: 7, Loss: 0.406921\n",
            "Epoch: 8, Loss: 0.951961\n",
            "Epoch: 9, Loss: 0.433738\n",
            "Epoch: 10, Loss: 0.351960\n",
            "Epoch: 11, Loss: 0.355687\n",
            "Epoch: 12, Loss: 0.518611\n",
            "Epoch: 13, Loss: 0.262623\n",
            "Epoch: 14, Loss: 0.221969\n",
            "Epoch: 15, Loss: 0.774132\n",
            "Epoch: 16, Loss: 0.324406\n",
            "Epoch: 17, Loss: 0.447701\n",
            "Epoch: 18, Loss: 0.299780\n",
            "Epoch: 19, Loss: 0.267090\n",
            "Epoch: 20, Loss: 0.279828\n",
            "Epoch: 21, Loss: 0.197123\n",
            "Epoch: 22, Loss: 0.196783\n",
            "Epoch: 23, Loss: 0.328715\n",
            "Epoch: 24, Loss: 0.334952\n",
            "Epoch: 25, Loss: 0.500689\n",
            "Epoch: 26, Loss: 0.186956\n",
            "Epoch: 27, Loss: 0.138649\n",
            "Epoch: 28, Loss: 0.239988\n",
            "Epoch: 29, Loss: 0.495020\n",
            "Epoch: 30, Loss: 0.251347\n",
            "Epoch: 31, Loss: 0.088298\n",
            "Epoch: 32, Loss: 0.175127\n",
            "Epoch: 33, Loss: 0.208338\n",
            "Epoch: 34, Loss: 0.145656\n",
            "Epoch: 35, Loss: 0.129570\n",
            "Epoch: 36, Loss: 0.200110\n",
            "Epoch: 37, Loss: 0.133076\n",
            "Epoch: 38, Loss: 0.230561\n",
            "Epoch: 39, Loss: 0.241688\n",
            "Epoch: 40, Loss: 0.106870\n",
            "Epoch: 41, Loss: 0.281168\n",
            "Epoch: 42, Loss: 0.175034\n",
            "Epoch: 43, Loss: 0.073779\n",
            "Epoch: 44, Loss: 0.171294\n",
            "Epoch: 45, Loss: 0.112456\n",
            "Epoch: 46, Loss: 0.132553\n",
            "Epoch: 47, Loss: 0.048826\n",
            "Epoch: 48, Loss: 0.076014\n",
            "Epoch: 49, Loss: 0.122317\n",
            "Epoch: 50, Loss: 0.103442\n",
            "Epoch: 51, Loss: 0.201585\n",
            "Epoch: 52, Loss: 0.145637\n",
            "Epoch: 53, Loss: 0.055844\n",
            "Epoch: 54, Loss: 0.046278\n",
            "Epoch: 55, Loss: 0.081562\n",
            "Epoch: 56, Loss: 0.058857\n",
            "Epoch: 57, Loss: 0.197200\n",
            "Epoch: 58, Loss: 0.044184\n",
            "Epoch: 59, Loss: 0.043374\n",
            "Epoch: 60, Loss: 0.032936\n",
            "Epoch: 61, Loss: 0.072488\n",
            "Epoch: 62, Loss: 0.060811\n",
            "Epoch: 63, Loss: 0.029262\n",
            "Epoch: 64, Loss: 0.036435\n",
            "Epoch: 65, Loss: 0.058120\n",
            "Epoch: 66, Loss: 0.063329\n",
            "Epoch: 67, Loss: 0.020670\n",
            "Epoch: 68, Loss: 0.077189\n",
            "Epoch: 69, Loss: 0.060933\n",
            "Epoch: 70, Loss: 0.070848\n",
            "Epoch: 71, Loss: 0.036434\n",
            "Epoch: 72, Loss: 0.084855\n",
            "Epoch: 73, Loss: 0.044776\n",
            "Epoch: 74, Loss: 0.037828\n",
            "Epoch: 75, Loss: 0.024554\n",
            "Epoch: 76, Loss: 0.018965\n",
            "Epoch: 77, Loss: 0.033381\n",
            "Epoch: 78, Loss: 0.016183\n",
            "Epoch: 79, Loss: 0.020083\n",
            "Epoch: 80, Loss: 0.041192\n",
            "Epoch: 81, Loss: 0.015122\n",
            "Epoch: 82, Loss: 0.014245\n",
            "Epoch: 83, Loss: 0.018538\n",
            "Epoch: 84, Loss: 0.044791\n",
            "Epoch: 85, Loss: 0.034532\n",
            "Epoch: 86, Loss: 0.010175\n",
            "Epoch: 87, Loss: 0.021837\n",
            "Epoch: 88, Loss: 0.005545\n",
            "Epoch: 89, Loss: 0.012682\n",
            "Epoch: 90, Loss: 0.026414\n",
            "Epoch: 91, Loss: 0.021372\n",
            "Epoch: 92, Loss: 0.025901\n",
            "Epoch: 93, Loss: 0.025262\n",
            "Epoch: 94, Loss: 0.047044\n",
            "Epoch: 95, Loss: 0.016064\n",
            "Epoch: 96, Loss: 0.059213\n",
            "Epoch: 97, Loss: 0.017386\n",
            "Epoch: 98, Loss: 0.016215\n",
            "Epoch: 99, Loss: 0.016987\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "\n",
        "train_loader = torch.utils.data.DataLoader(cifar2, batch_size=64,\n",
        "                                           shuffle=True)\n",
        "\n",
        "model = nn.Sequential(\n",
        "            nn.Linear(3072, 128),\n",
        "            nn.Tanh(),\n",
        "            nn.Linear(128, 2),\n",
        "            nn.LogSoftmax(dim=1))\n",
        "\n",
        "learning_rate = 1e-2\n",
        "\n",
        "optimizer = optim.SGD(model.parameters(), lr=learning_rate)\n",
        "\n",
        "loss_fn = nn.NLLLoss()\n",
        "\n",
        "n_epochs = 100\n",
        "\n",
        "for epoch in range(n_epochs):\n",
        "    for imgs, labels in train_loader:\n",
        "        outputs = model(imgs.view(imgs.shape[0], -1))\n",
        "        loss = loss_fn(outputs, labels)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "    print(\"Epoch: %d, Loss: %f\" % (epoch, float(loss)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "R4Ob7JUGSjdF",
        "outputId": "42ac10a9-1dc8-4029-f7e2-1e4fe688b483"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 0, Loss: 0.732168\n",
            "Epoch: 1, Loss: 0.348352\n",
            "Epoch: 2, Loss: 0.318960\n",
            "Epoch: 3, Loss: 0.313264\n",
            "Epoch: 4, Loss: 0.378358\n",
            "Epoch: 5, Loss: 0.276529\n",
            "Epoch: 6, Loss: 0.443889\n",
            "Epoch: 7, Loss: 0.436946\n",
            "Epoch: 8, Loss: 0.324288\n",
            "Epoch: 9, Loss: 0.274647\n",
            "Epoch: 10, Loss: 0.291681\n",
            "Epoch: 11, Loss: 0.242894\n",
            "Epoch: 12, Loss: 0.301849\n",
            "Epoch: 13, Loss: 0.202063\n",
            "Epoch: 14, Loss: 0.389276\n",
            "Epoch: 15, Loss: 0.167129\n",
            "Epoch: 16, Loss: 0.135282\n",
            "Epoch: 17, Loss: 0.385485\n",
            "Epoch: 18, Loss: 0.453852\n",
            "Epoch: 19, Loss: 0.641304\n",
            "Epoch: 20, Loss: 0.287667\n",
            "Epoch: 21, Loss: 0.337029\n",
            "Epoch: 22, Loss: 0.393282\n",
            "Epoch: 23, Loss: 0.409480\n",
            "Epoch: 24, Loss: 0.138473\n",
            "Epoch: 25, Loss: 0.690729\n",
            "Epoch: 26, Loss: 0.572156\n",
            "Epoch: 27, Loss: 0.078534\n",
            "Epoch: 28, Loss: 0.324833\n",
            "Epoch: 29, Loss: 0.262829\n",
            "Epoch: 30, Loss: 0.430449\n",
            "Epoch: 31, Loss: 0.071872\n",
            "Epoch: 32, Loss: 0.058039\n",
            "Epoch: 33, Loss: 0.052903\n",
            "Epoch: 34, Loss: 0.065879\n",
            "Epoch: 35, Loss: 0.107696\n",
            "Epoch: 36, Loss: 0.305224\n",
            "Epoch: 37, Loss: 0.098637\n",
            "Epoch: 38, Loss: 0.139823\n",
            "Epoch: 39, Loss: 0.226455\n",
            "Epoch: 40, Loss: 0.117763\n",
            "Epoch: 41, Loss: 0.106498\n",
            "Epoch: 42, Loss: 0.086254\n",
            "Epoch: 43, Loss: 0.135652\n",
            "Epoch: 44, Loss: 0.070890\n",
            "Epoch: 45, Loss: 0.304346\n",
            "Epoch: 46, Loss: 0.016917\n",
            "Epoch: 47, Loss: 0.057929\n",
            "Epoch: 48, Loss: 0.131021\n",
            "Epoch: 49, Loss: 0.136299\n",
            "Epoch: 50, Loss: 0.048885\n",
            "Epoch: 51, Loss: 0.241048\n",
            "Epoch: 52, Loss: 0.092595\n",
            "Epoch: 53, Loss: 0.059137\n",
            "Epoch: 54, Loss: 0.047421\n",
            "Epoch: 55, Loss: 0.102036\n",
            "Epoch: 56, Loss: 0.023338\n",
            "Epoch: 57, Loss: 0.054306\n",
            "Epoch: 58, Loss: 0.073878\n",
            "Epoch: 59, Loss: 0.031387\n",
            "Epoch: 60, Loss: 0.039865\n",
            "Epoch: 61, Loss: 0.022344\n",
            "Epoch: 62, Loss: 0.052310\n",
            "Epoch: 63, Loss: 0.059688\n",
            "Epoch: 64, Loss: 0.023977\n",
            "Epoch: 65, Loss: 0.010632\n",
            "Epoch: 66, Loss: 0.039090\n",
            "Epoch: 67, Loss: 0.080844\n",
            "Epoch: 68, Loss: 0.029650\n",
            "Epoch: 69, Loss: 0.027038\n",
            "Epoch: 70, Loss: 0.028515\n",
            "Epoch: 71, Loss: 0.021998\n",
            "Epoch: 72, Loss: 0.014992\n",
            "Epoch: 73, Loss: 0.019659\n",
            "Epoch: 74, Loss: 0.025150\n",
            "Epoch: 75, Loss: 0.017384\n",
            "Epoch: 76, Loss: 0.013249\n",
            "Epoch: 77, Loss: 0.009451\n",
            "Epoch: 78, Loss: 0.034637\n",
            "Epoch: 79, Loss: 0.114242\n",
            "Epoch: 80, Loss: 0.019007\n",
            "Epoch: 81, Loss: 0.016319\n",
            "Epoch: 82, Loss: 0.027428\n",
            "Epoch: 83, Loss: 0.022366\n",
            "Epoch: 84, Loss: 0.022583\n",
            "Epoch: 85, Loss: 0.006275\n",
            "Epoch: 86, Loss: 0.011964\n",
            "Epoch: 87, Loss: 0.018711\n",
            "Epoch: 88, Loss: 0.019636\n",
            "Epoch: 89, Loss: 0.018975\n",
            "Epoch: 90, Loss: 0.023520\n",
            "Epoch: 91, Loss: 0.016398\n",
            "Epoch: 92, Loss: 0.006638\n",
            "Epoch: 93, Loss: 0.013305\n",
            "Epoch: 94, Loss: 0.017126\n",
            "Epoch: 95, Loss: 0.021641\n",
            "Epoch: 96, Loss: 0.036945\n",
            "Epoch: 97, Loss: 0.004735\n",
            "Epoch: 98, Loss: 0.016781\n",
            "Epoch: 99, Loss: 0.012039\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "\n",
        "train_loader = torch.utils.data.DataLoader(cifar2, batch_size=64,\n",
        "                                           shuffle=True)\n",
        "\n",
        "model = nn.Sequential(\n",
        "            nn.Linear(3072, 512),\n",
        "            nn.Tanh(),\n",
        "            nn.Linear(512, 2),\n",
        "            nn.LogSoftmax(dim=1))\n",
        "\n",
        "learning_rate = 1e-2\n",
        "\n",
        "optimizer = optim.SGD(model.parameters(), lr=learning_rate)\n",
        "\n",
        "loss_fn = nn.NLLLoss()\n",
        "\n",
        "n_epochs = 100\n",
        "\n",
        "for epoch in range(n_epochs):\n",
        "    for imgs, labels in train_loader:\n",
        "        outputs = model(imgs.view(imgs.shape[0], -1))\n",
        "        loss = loss_fn(outputs, labels)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "    print(\"Epoch: %d, Loss: %f\" % (epoch, float(loss)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "K0V2WkiMSjdF",
        "outputId": "38674934-df48-4e49-9426-30ca174bbc43"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 0.997700\n"
          ]
        }
      ],
      "source": [
        "train_loader = torch.utils.data.DataLoader(cifar2, batch_size=64,\n",
        "                                           shuffle=False)\n",
        "\n",
        "correct = 0\n",
        "total = 0\n",
        "\n",
        "with torch.no_grad():\n",
        "    for imgs, labels in train_loader:\n",
        "        outputs = model(imgs.view(imgs.shape[0], -1))\n",
        "        _, predicted = torch.max(outputs, dim=1)\n",
        "        total += labels.shape[0]\n",
        "        correct += int((predicted == labels).sum())\n",
        "\n",
        "print(\"Accuracy: %f\" % (correct / total))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IEkbCSFsSjdG",
        "outputId": "48e14a1d-ccf0-4daf-f5d5-98e1d5f73293"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 0.821000\n"
          ]
        }
      ],
      "source": [
        "val_loader = torch.utils.data.DataLoader(cifar2_val, batch_size=64,\n",
        "                                         shuffle=False)\n",
        "\n",
        "correct = 0\n",
        "total = 0\n",
        "\n",
        "with torch.no_grad():\n",
        "    for imgs, labels in val_loader:\n",
        "        outputs = model(imgs.view(imgs.shape[0], -1))\n",
        "        _, predicted = torch.max(outputs, dim=1)\n",
        "        total += labels.shape[0]\n",
        "        correct += int((predicted == labels).sum())\n",
        "\n",
        "print(\"Accuracy: %f\" % (correct / total))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8hoBbKU1SjdG"
      },
      "outputs": [],
      "source": [
        "model = nn.Sequential(\n",
        "            nn.Linear(3072, 1024),\n",
        "            nn.Tanh(),\n",
        "            nn.Linear(1024, 512),\n",
        "            nn.Tanh(),\n",
        "            nn.Linear(512, 128),\n",
        "            nn.Tanh(),\n",
        "            nn.Linear(128, 2),\n",
        "            nn.LogSoftmax(dim=1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_FIYxxSDSjdI"
      },
      "outputs": [],
      "source": [
        "model = nn.Sequential(\n",
        "            nn.Linear(3072, 1024),\n",
        "            nn.Tanh(),\n",
        "            nn.Linear(1024, 512),\n",
        "            nn.Tanh(),\n",
        "            nn.Linear(512, 128),\n",
        "            nn.Tanh(),\n",
        "            nn.Linear(128, 2))\n",
        "\n",
        "loss_fn = nn.CrossEntropyLoss()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3HcnpUjqSjdI",
        "outputId": "45e0a877-5b03-44f9-d920-c0f670f6b9dc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 0, Loss: 0.641261\n",
            "Epoch: 1, Loss: 0.525149\n",
            "Epoch: 2, Loss: 0.466143\n",
            "Epoch: 3, Loss: 0.451913\n",
            "Epoch: 4, Loss: 0.343860\n",
            "Epoch: 5, Loss: 0.309738\n",
            "Epoch: 6, Loss: 0.485261\n",
            "Epoch: 7, Loss: 0.283789\n",
            "Epoch: 8, Loss: 0.301561\n",
            "Epoch: 9, Loss: 0.408200\n",
            "Epoch: 10, Loss: 0.346715\n",
            "Epoch: 11, Loss: 0.358134\n",
            "Epoch: 12, Loss: 0.388485\n",
            "Epoch: 13, Loss: 0.378096\n",
            "Epoch: 14, Loss: 0.518019\n",
            "Epoch: 15, Loss: 0.359279\n",
            "Epoch: 16, Loss: 0.420371\n",
            "Epoch: 17, Loss: 0.366249\n",
            "Epoch: 18, Loss: 0.282639\n",
            "Epoch: 19, Loss: 0.468854\n",
            "Epoch: 20, Loss: 0.467920\n",
            "Epoch: 21, Loss: 0.237441\n",
            "Epoch: 22, Loss: 0.243472\n",
            "Epoch: 23, Loss: 0.566929\n",
            "Epoch: 24, Loss: 0.316143\n",
            "Epoch: 25, Loss: 0.336322\n",
            "Epoch: 26, Loss: 0.473064\n",
            "Epoch: 27, Loss: 0.407040\n",
            "Epoch: 28, Loss: 0.252989\n",
            "Epoch: 29, Loss: 0.195740\n",
            "Epoch: 30, Loss: 0.663084\n",
            "Epoch: 31, Loss: 0.659899\n",
            "Epoch: 32, Loss: 0.285113\n",
            "Epoch: 33, Loss: 0.212042\n",
            "Epoch: 34, Loss: 0.324017\n",
            "Epoch: 35, Loss: 0.097063\n",
            "Epoch: 36, Loss: 0.181754\n",
            "Epoch: 37, Loss: 0.091362\n",
            "Epoch: 38, Loss: 0.069348\n",
            "Epoch: 39, Loss: 0.085656\n",
            "Epoch: 40, Loss: 0.163399\n",
            "Epoch: 41, Loss: 0.064912\n",
            "Epoch: 42, Loss: 0.046740\n",
            "Epoch: 43, Loss: 0.029891\n",
            "Epoch: 44, Loss: 0.018157\n",
            "Epoch: 45, Loss: 0.103532\n",
            "Epoch: 46, Loss: 0.161911\n",
            "Epoch: 47, Loss: 0.238185\n",
            "Epoch: 48, Loss: 0.081116\n",
            "Epoch: 49, Loss: 0.040988\n",
            "Epoch: 50, Loss: 0.008668\n",
            "Epoch: 51, Loss: 0.012557\n",
            "Epoch: 52, Loss: 0.015967\n",
            "Epoch: 53, Loss: 0.020964\n",
            "Epoch: 54, Loss: 0.023478\n",
            "Epoch: 55, Loss: 0.012850\n",
            "Epoch: 56, Loss: 0.054703\n",
            "Epoch: 57, Loss: 0.014922\n",
            "Epoch: 58, Loss: 0.045488\n",
            "Epoch: 59, Loss: 0.122221\n",
            "Epoch: 60, Loss: 0.028012\n",
            "Epoch: 61, Loss: 0.029533\n",
            "Epoch: 62, Loss: 0.004758\n",
            "Epoch: 63, Loss: 0.080409\n",
            "Epoch: 64, Loss: 0.005409\n",
            "Epoch: 65, Loss: 0.020399\n",
            "Epoch: 66, Loss: 0.008184\n",
            "Epoch: 67, Loss: 0.013888\n",
            "Epoch: 68, Loss: 0.002199\n",
            "Epoch: 69, Loss: 0.001918\n",
            "Epoch: 70, Loss: 0.018765\n",
            "Epoch: 71, Loss: 0.004223\n",
            "Epoch: 72, Loss: 0.001795\n",
            "Epoch: 73, Loss: 0.102238\n",
            "Epoch: 74, Loss: 0.002482\n",
            "Epoch: 75, Loss: 0.005807\n",
            "Epoch: 76, Loss: 0.001742\n",
            "Epoch: 77, Loss: 0.012760\n",
            "Epoch: 78, Loss: 0.017469\n",
            "Epoch: 79, Loss: 0.002849\n",
            "Epoch: 80, Loss: 0.001452\n",
            "Epoch: 81, Loss: 0.002740\n",
            "Epoch: 82, Loss: 0.003317\n",
            "Epoch: 83, Loss: 0.002066\n",
            "Epoch: 84, Loss: 0.001952\n",
            "Epoch: 85, Loss: 0.010757\n",
            "Epoch: 86, Loss: 0.004866\n",
            "Epoch: 87, Loss: 0.003957\n",
            "Epoch: 88, Loss: 0.001295\n",
            "Epoch: 89, Loss: 0.004410\n",
            "Epoch: 90, Loss: 0.002952\n",
            "Epoch: 91, Loss: 0.000676\n",
            "Epoch: 92, Loss: 0.001835\n",
            "Epoch: 93, Loss: 0.000739\n",
            "Epoch: 94, Loss: 0.001102\n",
            "Epoch: 95, Loss: 0.000792\n",
            "Epoch: 96, Loss: 0.000515\n",
            "Epoch: 97, Loss: 0.001548\n",
            "Epoch: 98, Loss: 0.026913\n",
            "Epoch: 99, Loss: 0.000140\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "\n",
        "train_loader = torch.utils.data.DataLoader(cifar2, batch_size=64,\n",
        "                                           shuffle=True)\n",
        "\n",
        "model = nn.Sequential(\n",
        "            nn.Linear(3072, 1024),\n",
        "            nn.Tanh(),\n",
        "            nn.Linear(1024, 512),\n",
        "            nn.Tanh(),\n",
        "            nn.Linear(512, 128),\n",
        "            nn.Tanh(),\n",
        "            nn.Linear(128, 2))\n",
        "\n",
        "learning_rate = 1e-2\n",
        "\n",
        "optimizer = optim.SGD(model.parameters(), lr=learning_rate)\n",
        "\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "\n",
        "n_epochs = 100\n",
        "\n",
        "for epoch in range(n_epochs):\n",
        "    for imgs, labels in train_loader:\n",
        "        outputs = model(imgs.view(imgs.shape[0], -1))\n",
        "        loss = loss_fn(outputs, labels)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "    print(\"Epoch: %d, Loss: %f\" % (epoch, float(loss)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VOeiwgKMSjdJ",
        "outputId": "6823c968-27c6-46a7-91e7-00ed00413aa1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 0.999700\n"
          ]
        }
      ],
      "source": [
        "train_loader = torch.utils.data.DataLoader(cifar2, batch_size=64,\n",
        "                                           shuffle=False)\n",
        "\n",
        "correct = 0\n",
        "total = 0\n",
        "\n",
        "with torch.no_grad():\n",
        "    for imgs, labels in train_loader:\n",
        "        outputs = model(imgs.view(imgs.shape[0], -1))\n",
        "        _, predicted = torch.max(outputs, dim=1)\n",
        "        total += labels.shape[0]\n",
        "        correct += int((predicted == labels).sum())\n",
        "\n",
        "print(\"Accuracy: %f\" % (correct / total))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3xdO12EYSjdJ",
        "outputId": "08e9069a-5109-41af-9ead-65f25b8432f6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 0.801000\n"
          ]
        }
      ],
      "source": [
        "val_loader = torch.utils.data.DataLoader(cifar2_val, batch_size=64,\n",
        "                                         shuffle=False)\n",
        "\n",
        "correct = 0\n",
        "total = 0\n",
        "\n",
        "with torch.no_grad():\n",
        "    for imgs, labels in val_loader:\n",
        "        outputs = model(imgs.view(imgs.shape[0], -1))\n",
        "        _, predicted = torch.max(outputs, dim=1)\n",
        "        total += labels.shape[0]\n",
        "        correct += int((predicted == labels).sum())\n",
        "\n",
        "print(\"Accuracy: %f\" % (correct / total))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-Ty3kiLTSjdK",
        "outputId": "573488c2-6e03-486f-e354-5892b144904d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "3737474"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sum([p.numel() for p in model.parameters()])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FnjP1EsrSjdK",
        "outputId": "9fb20fbf-9054-47a6-da1a-34e2b13d3f45"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "3737474"
            ]
          },
          "execution_count": 45,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sum([p.numel() for p in model.parameters() if p.requires_grad == True])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nSNMkX3_SjdL",
        "outputId": "356565b9-c6d7-4dcc-bd48-325fd316ad8c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1574402"
            ]
          },
          "execution_count": 46,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "first_model = nn.Sequential(\n",
        "                nn.Linear(3072, 512),\n",
        "                nn.Tanh(),\n",
        "                nn.Linear(512, 2),\n",
        "                nn.LogSoftmax(dim=1))\n",
        "\n",
        "sum([p.numel() for p in first_model.parameters()])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "To_It-moSjdL",
        "outputId": "a5091155-7665-44b4-c677-bb9d61d19e0d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1573376"
            ]
          },
          "execution_count": 47,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sum([p.numel() for p in nn.Linear(3072, 512).parameters()])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FSJJgi9hSjdM",
        "outputId": "1a6bd514-a4b1-4c1f-c814-f94f5eef3c9b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "3146752"
            ]
          },
          "execution_count": 48,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sum([p.numel() for p in nn.Linear(3072, 1024).parameters()])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HzJu0hBsSjdN",
        "outputId": "3f01c783-eb1b-4a59-bfe1-4d3dea04d8e7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(torch.Size([1024, 3072]), torch.Size([1024]))"
            ]
          },
          "execution_count": 49,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "linear = nn.Linear(3072, 1024)\n",
        "\n",
        "linear.weight.shape, linear.bias.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XUk4mp9LSjdN"
      },
      "outputs": [],
      "source": [
        "conv = nn.Conv2d(3, 16, kernel_size=3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vqiqSE3PSjdN",
        "outputId": "57291547-b1d9-45d0-ae05-21dab7a0bf47"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "torch.Size([16, 3, 3, 3])"
            ]
          },
          "execution_count": 51,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "conv.weight.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_KELJTWfSjdO",
        "outputId": "08d07510-7e0b-4ea9-a665-b78c1b22bd95"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "torch.Size([16])"
            ]
          },
          "execution_count": 52,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "conv.bias.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pC0904oNSjdP"
      },
      "outputs": [],
      "source": [
        "img, _ = cifar2[0]\n",
        "\n",
        "output = conv(img.unsqueeze(0))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UXneeInpSjdP",
        "outputId": "a499ec8c-3700-471c-cf90-dce7e1beff57"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(torch.Size([1, 3, 32, 32]), torch.Size([1, 16, 30, 30]))"
            ]
          },
          "execution_count": 54,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "img.unsqueeze(0).shape, output.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "k8TwL5FXSjdQ",
        "outputId": "899e364b-0442-4e44-dcaf-b6c92e9408a0"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ]
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.imshow(img.permute(1, 2, 0), cmap='gray')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9l1QQ3r7SjdQ",
        "outputId": "5209037c-3521-4133-8fcf-a60a6960374a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.imshow(output[0, 0].detach(), cmap='gray')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Mi_ilQIBSjdR",
        "outputId": "3b8cc610-a8cf-4827-927f-008dacb4a626"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "torch.Size([1, 16, 30, 30])"
            ]
          },
          "execution_count": 57,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "output.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PfnOWfi5SjdR"
      },
      "outputs": [],
      "source": [
        "conv = nn.Conv2d(3, 1, kernel_size=3, padding=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0r-bjqPCSjdS",
        "outputId": "1c6c8dee-deb6-427a-fc07-f2894ff0752a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "torch.Size([1, 1, 32, 32])"
            ]
          },
          "execution_count": 59,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "output = conv(img.unsqueeze(0))\n",
        "\n",
        "output.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "41LnuxGVSjdS"
      },
      "outputs": [],
      "source": [
        "with torch.no_grad():\n",
        "    conv.bias.zero_()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4vjJ_S1lSjdT"
      },
      "outputs": [],
      "source": [
        "with torch.no_grad():\n",
        "    conv.weight.fill_(1.0 / 9.0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BYFY90UNSjdT",
        "outputId": "86079f0f-d33b-4b25-8a2a-a090c81f511e"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "output = conv(img.unsqueeze(0))\n",
        "plt.imshow(output[0, 0].detach(), cmap='gray')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "i73bBF_mSjdU"
      },
      "outputs": [],
      "source": [
        "conv = nn.Conv2d(3, 1, kernel_size=3, padding=1)\n",
        "\n",
        "with torch.no_grad():\n",
        "    conv.weight[:] = torch.tensor([[-1.0, 0.0, 1.0],\n",
        "                                   [-1.0, 0.0, 1.0],\n",
        "                                   [-1.0, 0.0, 1.0]])\n",
        "    conv.bias.zero_()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "U8UqZp92SjdU",
        "outputId": "53a5052f-6a86-4d00-8e0d-89e837b27fe0"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "output = conv(img.unsqueeze(0))\n",
        "plt.imshow(output[0, 0].detach(), cmap='gray')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eFxzIs8GSjdV"
      },
      "outputs": [],
      "source": [
        "pool = nn.MaxPool2d(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zFA6od9-SjdV",
        "outputId": "d78ca7ba-840e-459c-b44b-29b3e5489e48"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "torch.Size([1, 3, 16, 16])"
            ]
          },
          "execution_count": 66,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "output = pool(img.unsqueeze(0))\n",
        "\n",
        "output.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Si54X8o9SjdW",
        "outputId": "0fd00653-f96b-4e50-d136-46f32d11c45a"
      },
      "outputs": [
        {
          "ename": "TypeError",
          "evalue": "ellipsis is not a Module subclass",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[1;32m<ipython-input-67-94807a3b1438>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      6\u001b[0m             \u001b[0mnn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTanh\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m             \u001b[0mnn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mMaxPool2d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m             ...)\n\u001b[0m",
            "\u001b[1;32m~\\Miniconda3\\envs\\book\\lib\\site-packages\\torch\\nn\\modules\\container.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m     51\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     52\u001b[0m             \u001b[1;32mfor\u001b[0m \u001b[0midx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodule\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 53\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_module\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0midx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodule\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     54\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     55\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_get_item_by_idx\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0miterator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0midx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
            "\u001b[1;32m~\\Miniconda3\\envs\\book\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36madd_module\u001b[1;34m(self, name, module)\u001b[0m\n\u001b[0;32m    171\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodule\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mModule\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mmodule\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    172\u001b[0m             raise TypeError(\"{} is not a Module subclass\".format(\n\u001b[1;32m--> 173\u001b[1;33m                 torch.typename(module)))\n\u001b[0m\u001b[0;32m    174\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_six\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstring_classes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    175\u001b[0m             raise TypeError(\"module name should be a string. Got {}\".format(\n",
            "\u001b[1;31mTypeError\u001b[0m: ellipsis is not a Module subclass"
          ]
        }
      ],
      "source": [
        "model = nn.Sequential(\n",
        "            nn.Conv2d(3, 16, kernel_size=3, padding=1),\n",
        "            nn.Tanh(),\n",
        "            nn.MaxPool2d(2),\n",
        "            nn.Conv2d(16, 8, kernel_size=3, padding=1),\n",
        "            nn.Tanh(),\n",
        "            nn.MaxPool2d(2),\n",
        "            ...)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iMyJlMCfSjdX"
      },
      "outputs": [],
      "source": [
        "model = nn.Sequential(\n",
        "            nn.Conv2d(3, 16, kernel_size=3, padding=1),\n",
        "            nn.Tanh(),\n",
        "            nn.MaxPool2d(2),\n",
        "            nn.Conv2d(16, 8, kernel_size=3, padding=1),\n",
        "            nn.Tanh(),\n",
        "            nn.MaxPool2d(2),\n",
        "            # WARNING: something missing here\n",
        "            nn.Linear(512, 32),\n",
        "            nn.Tanh(),\n",
        "            nn.Linear(32, 2))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BKYkRZ7WSjdX"
      },
      "outputs": [],
      "source": [
        "sum([p.numel() for p in model.parameters()])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xrNs92FGSjdY"
      },
      "outputs": [],
      "source": [
        "model(img.unsqueeze(0))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "s1jQ83TjSjdY"
      },
      "outputs": [],
      "source": [
        "class Net(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Net, self).__init__()\n",
        "        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=1)\n",
        "        self.act1 = nn.Tanh()\n",
        "        self.pool1 = nn.MaxPool2d(2)\n",
        "        self.conv2 = nn.Conv2d(16, 8, kernel_size=3, padding=1)\n",
        "        self.act2 = nn.Tanh()\n",
        "        self.pool2 = nn.MaxPool2d(2)\n",
        "        self.fc1 = nn.Linear(8 * 8 * 8, 32)\n",
        "        self.act4 = nn.Tanh()\n",
        "        self.fc2 = nn.Linear(32, 2)\n",
        "\n",
        "    def forward(self, x):\n",
        "        out = self.pool1(self.act1(self.conv1(x)))\n",
        "        out = self.pool2(self.act2(self.conv2(out)))\n",
        "        out = out.view(-1, 8 * 8 * 8)\n",
        "        out = self.act4(self.fc1(out))\n",
        "        out = self.fc2(out)\n",
        "        return out"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dGS9pw0LSjdZ"
      },
      "outputs": [],
      "source": [
        "model = Net()\n",
        "\n",
        "sum([p.numel() for p in model.parameters()])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BoCa0ROdSjdZ"
      },
      "outputs": [],
      "source": [
        "import torch.nn.functional as F\n",
        "\n",
        "class Net(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Net, self).__init__()\n",
        "        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=1)\n",
        "        self.conv2 = nn.Conv2d(16, 8, kernel_size=3, padding=1)\n",
        "        self.fc1 = nn.Linear(8 * 8 * 8, 32)\n",
        "        self.fc2 = nn.Linear(32, 2)\n",
        "\n",
        "    def forward(self, x):\n",
        "        out = F.max_pool2d(torch.tanh(self.conv1(x)), 2)\n",
        "        out = F.max_pool2d(torch.tanh(self.conv2(out)), 2)\n",
        "        out = out.view(-1, 8 * 8 * 8)\n",
        "        out = torch.tanh(self.fc1(out))\n",
        "        out = self.fc2(out)\n",
        "        return out"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "byZ3xolTSjda"
      },
      "outputs": [],
      "source": [
        "model = Net()\n",
        "model(img.unsqueeze(0))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uSLbPWDTSjda"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "train_loader = torch.utils.data.DataLoader(cifar2, batch_size=64,\n",
        "                                           shuffle=True)\n",
        "\n",
        "class Net(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Net, self).__init__()\n",
        "        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=1)\n",
        "        self.conv2 = nn.Conv2d(16, 8, kernel_size=3, padding=1)\n",
        "        self.fc1 = nn.Linear(8 * 8 * 8, 32)\n",
        "        self.fc2 = nn.Linear(32, 2)\n",
        "\n",
        "    def forward(self, x):\n",
        "        out = F.max_pool2d(torch.relu(self.conv1(x)), 2)\n",
        "        out = F.max_pool2d(torch.relu(self.conv2(out)), 2)\n",
        "        out = out.view(-1, 8 * 8 * 8)\n",
        "        out = torch.tanh(self.fc1(out))\n",
        "        out = self.fc2(out)\n",
        "        return out\n",
        "\n",
        "model = Net()\n",
        "\n",
        "learning_rate = 1e-2\n",
        "\n",
        "optimizer = optim.SGD(model.parameters(), lr=learning_rate)\n",
        "\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "\n",
        "n_epochs = 100\n",
        "\n",
        "for epoch in range(n_epochs):\n",
        "    for imgs, labels in train_loader:\n",
        "        outputs = model(imgs)\n",
        "        loss = loss_fn(outputs, labels)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "    print(\"Epoch: %d, Loss: %f\" % (epoch, float(loss)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dfwIP6i4Sjdb"
      },
      "outputs": [],
      "source": [
        "train_loader = torch.utils.data.DataLoader(cifar2, batch_size=64,\n",
        "                                           shuffle=False)\n",
        "\n",
        "correct = 0\n",
        "total = 0\n",
        "\n",
        "with torch.no_grad():\n",
        "    for imgs, labels in train_loader:\n",
        "        outputs = model(imgs)\n",
        "        _, predicted = torch.max(outputs, dim=1)\n",
        "        total += labels.shape[0]\n",
        "        correct += int((predicted == labels).sum())\n",
        "\n",
        "print(\"Accuracy: %f\" % (correct / total))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "R3mdveDKSjdb"
      },
      "outputs": [],
      "source": [
        "val_loader = torch.utils.data.DataLoader(cifar2_val, batch_size=64,\n",
        "                                         shuffle=False)\n",
        "\n",
        "correct = 0\n",
        "total = 0\n",
        "\n",
        "with torch.no_grad():\n",
        "    for imgs, labels in val_loader:\n",
        "        outputs = model(imgs)\n",
        "        _, predicted = torch.max(outputs, dim=1)\n",
        "        total += labels.shape[0]\n",
        "        correct += int((predicted == labels).sum())\n",
        "\n",
        "print(\"Accuracy: %f\" % (correct / total))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b6lNNjTuSjdc"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "class Net(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Net, self).__init__()\n",
        "        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=1)\n",
        "        self.conv2 = nn.Conv2d(16, 8, kernel_size=3, padding=1)\n",
        "        self.fc1 = nn.Linear(8 * 8 * 8, 32)\n",
        "        self.fc2 = nn.Linear(32, 2)\n",
        "\n",
        "    def forward(self, x):\n",
        "        out = F.max_pool2d(torch.relu(self.conv1(x)), 2)\n",
        "        out = F.max_pool2d(torch.relu(self.conv2(out)), 2)\n",
        "        out = out.view(-1, 8 * 8 * 8)\n",
        "        out = torch.tanh(self.fc1(out))\n",
        "        out = self.fc2(out)\n",
        "        return out\n",
        "\n",
        "model = Net()\n",
        "sum([p.numel() for p in model.parameters()])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "U9gaSs5MSjdc"
      },
      "outputs": [],
      "source": [
        "model = nn.Sequential(\n",
        "            nn.Conv2d(3, 16, kernel_size=3, padding=1),\n",
        "            nn.Tanh(),\n",
        "            nn.MaxPool2d(2),\n",
        "            nn.Conv2d(16, 8, kernel_size=3, padding=1),\n",
        "            nn.Tanh(),\n",
        "            nn.MaxPool2d(2),\n",
        "            nn.Linear(8*8*8, 32),\n",
        "            nn.Tanh(),\n",
        "            nn.Linear(32, 2))\n",
        "\n",
        "model(img.unsqueeze(0))"
      ]
    }
  ],
  "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": "3.7.6"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}