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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "bkZjeoYv01zr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "28obk15u2F2-",
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        "colab": {
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          "height": 408,
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          ]
        },
        "outputId": "a127d6ac-15b5-4436-b60f-c07ee165f8d0"
      },
      "source": [
        "from torchvision import datasets, transforms\n",
        "\n",
        "training_data = datasets.FashionMNIST(\".\",download=True,train=True, transform=transforms.Compose([\n",
        "                                                                                transforms.ToTensor(),\n",
        "                                                                                lambda x: torch.flatten(x)\n",
        "]))\n",
        "test_data = datasets.FashionMNIST(\".\",download=True,train=False, transform=transforms.Compose([\n",
        "                                                                                transforms.ToTensor(),\n",
        "                                                                                lambda x: torch.flatten(x),\n",
        "]))"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to ./FashionMNIST/raw/train-images-idx3-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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              "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Extracting ./FashionMNIST/raw/train-images-idx3-ubyte.gz to ./FashionMNIST/raw\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to ./FashionMNIST/raw/train-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7813b59c8b844ae28a797c2bf4aa2bba",
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              "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
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          },
          "metadata": {
            "tags": []
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        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Extracting ./FashionMNIST/raw/train-labels-idx1-ubyte.gz to ./FashionMNIST/raw\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to ./FashionMNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a96edcf7fb074e7bbaf5fc158168e039",
              "version_minor": 0,
              "version_major": 2
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              "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
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        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Extracting ./FashionMNIST/raw/t10k-images-idx3-ubyte.gz to ./FashionMNIST/raw\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to ./FashionMNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6b9e92d073904ebc93d8c3d7f2d077fe",
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              "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Extracting ./FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to ./FashionMNIST/raw\n",
            "Processing...\n",
            "Done!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ksPWuq2O2Nnq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "35f16f90-8a1f-4c9f-c46a-c1f7dd1f8bd6"
      },
      "source": [
        "type(training_data[1][0])"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Tensor"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mJebsksX2dHf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d1ade350-0323-449f-bd98-cca6076a1c2b"
      },
      "source": [
        "training_data[2][0].size()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([784])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sbmbULVZM3Y_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from torch import nn, optim"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gz6TKjRb8NDX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tqdm import tqdm_notebook as tqdm\n",
        "from torch.utils.data import RandomSampler\n",
        "model_TD1 = nn.Sequential(nn.Linear(784, 1))\n",
        "\n",
        "def trainer(dataset,model,loss_fn,epoch=10,batch_size=1,rate=1e-4):\n",
        "  data = torch.utils.data.DataLoader(dataset, batch_size, RandomSampler)\n",
        "  optimizer = optim.Adam(model.parameters(),lr=rate)\n",
        "  for i in tqdm(range(epoch)):\n",
        "    for imgs, labels in tqdm(data):\n",
        "      imgs = imgs.cuda()\n",
        "      labels = labels.cuda()\n",
        "      optimizer.zero_grad()\n",
        "      labels = labels.float()\n",
        "      output = model(imgs)\n",
        "      loss = loss_fn(output, labels)\n",
        "      loss.backward()\n",
        "\n",
        "      optimizer.step()"
      ],
      "execution_count": 0,
      "outputs": []
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            "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/loss.py:431: UserWarning: Using a target size (torch.Size([64])) that is different to the input size (torch.Size([64, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
            "  return F.mse_loss(input, target, reduction=self.reduction)\n",
            "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/loss.py:431: UserWarning: Using a target size (torch.Size([32])) that is different to the input size (torch.Size([32, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
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    {
      "cell_type": "code",
      "metadata": {
        "id": "nbJ8Bm8qcCF4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def success_rate(model,test_data):\n",
        "  test_loader = torch.utils.data.DataLoader(test_data, batch_size=len(test_data))\n",
        "  imgs, labels = iter(test_loader).next()\n",
        "  imgs = imgs.cuda()\n",
        "  labels = labels.cuda()\n",
        "  output = model(imgs)\n",
        "  print(output)\n",
        "  correct = [i for i,j in zip(output, labels) if torch.round(i) == j]\n",
        "  print(len(correct))\n",
        "  return(len(correct) / len(labels))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3jwxDRSlhy2B",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "c6f34b7f-aa2b-41e1-c6a6-6aef70cc5054"
      },
      "source": [
        "suc = success_rate(model_TD1, test_data)\n",
        "suc"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[3.6745],\n",
            "        [5.3241],\n",
            "        [4.9974],\n",
            "        ...,\n",
            "        [3.2376],\n",
            "        [4.2832],\n",
            "        [2.9110]], device='cuda:0', grad_fn=<AddmmBackward>)\n",
            "696\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0696"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 59
        }
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        },
        "outputId": "922b8406-4f26-4f56-c618-a6bbd6e62ec6"
      },
      "source": [
        "def trainer(dataset,model,loss_fn,epoch=10,batch_size=1,rate=1e-4):\n",
        "  data = torch.utils.data.DataLoader(dataset, batch_size, RandomSampler)\n",
        "  optimizer = optim.Adam(model.parameters(),lr=rate)\n",
        "  for i in tqdm(range(epoch)):\n",
        "    for imgs, labels in tqdm(data):\n",
        "      imgs = imgs.cuda()\n",
        "      labels = labels.cuda()\n",
        "\n",
        "      optimizer.zero_grad()\n",
        "\n",
        "      output = model(imgs)\n",
        "      loss = loss_fn(output, labels)\n",
        "      loss.backward()\n",
        "\n",
        "      optimizer.step()\n",
        "\n",
        "\n",
        "model_2 = nn.Sequential(nn.Linear(784, 10),\n",
        "                        nn.LogSoftmax(1))\n",
        "model_2.cuda()\n",
        "trainer(training_data, model_2, nn.CrossEntropyLoss(), batch_size=64)"
      ],
      "execution_count": 19,
      "outputs": [
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      "cell_type": "code",
      "metadata": {
        "id": "RVmCyhhK06d_",
        "colab_type": "code",
        "colab": {
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          "height": 52
        },
        "outputId": "713c258f-ed3a-47d5-eca7-38f37d2a0b29"
      },
      "source": [
        "def success_rate(model,test_data):\n",
        "  test_loader = torch.utils.data.DataLoader(test_data, batch_size=len(test_data))\n",
        "  imgs, labels = iter(test_loader).next()\n",
        "  imgs = imgs.cuda()\n",
        "  labels = labels.cuda()\n",
        "\n",
        "  output = model(imgs)\n",
        "  output = [torch.argmax(i) for i in output]\n",
        "  correct = [i for i,j in zip(output, labels) if i == j]\n",
        "  print(len(correct))\n",
        "  return(len(correct) / len(labels))\n",
        "\n",
        "success_rate(model_2, test_data)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "8265\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
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            "tags": []
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          "execution_count": 22
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        "                        nn.ReLU(),\n",
        "                        nn.Linear(128,10),\n",
        "                        nn.LogSoftmax(1))\n",
        "model_3.cuda()\n",
        "trainer(training_data, model_3, nn.CrossEntropyLoss(), batch_size=64)"
      ],
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      "outputs": [
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        "                        nn.Sigmoid(),\n",
        "                        nn.Linear(128,10),\n",
        "                        nn.LogSoftmax(1))\n",
        "model_4.cuda()\n",
        "trainer(training_data, model_4, nn.CrossEntropyLoss(), batch_size=64)\n",
        "success_rate(model_4, test_data)"
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      "source": [
        "model_5 = nn.Sequential(nn.Linear(784, 128),\n",
        "                        nn.Tanh(),\n",
        "                        nn.Linear(128,10),\n",
        "                        nn.LogSoftmax(1))\n",
        "model_5.cuda()\n",
        "trainer(training_data, model_5, nn.CrossEntropyLoss(), batch_size=8)\n",
        "success_rate(model_5, test_data)"
      ],
      "execution_count": 49,
      "outputs": [
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        "outputId": "7d364477-0271-4fe4-b572-98bbf6fd4b76"
      },
      "source": [
        "model_6 = nn.Sequential(nn.Linear(784, 128),\n",
        "                        nn.Tanh(),\n",
        "                        nn.Linear(128,10),\n",
        "                        nn.LogSoftmax(1))\n",
        "model_6.cuda()\n",
        "trainer(training_data, model_6, nn.NLLLoss(), batch_size=8)\n",
        "success_rate(model_6, test_data)"
      ],
      "execution_count": 51,
      "outputs": [
        {
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "8732\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8732"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BjeVV2V2BQzh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import files\n",
        "torch.save(model_5, 'model_5')"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}