Mnist_MLP.ipynb

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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import torch\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "from sklearn.model_selection import train_test_split\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.utils.data\n",
        "from torch.autograd import Variable"
      ],
      "metadata": {
        "id": "HCkVOgDcLShy"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
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      "source": [
        "train=  pd.read_csv('/content/sample_data/mnist_train_small.csv')\n",
        "test=  pd.read_csv('/content/sample_data/mnist_test.csv')"
      ],
      "metadata": {
        "id": "k-CkLPVbLDqw"
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      "execution_count": 2,
      "outputs": []
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        "X_train, X_test, y_train, y_test = train_test_split(train,y_train , random_state=42,  test_size=0.25,  shuffle=True)"
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        "BATCH_SIZE = 32\n",
        "\n",
        "torch_X_train = torch.from_numpy(X_train).type(torch.LongTensor)\n",
        "torch_y_train = torch.from_numpy(y_train).type(torch.LongTensor) # data type is long\n",
        "\n",
        "# create feature and targets tensor for test set.\n",
        "torch_X_test = torch.from_numpy(X_test).type(torch.LongTensor)\n",
        "torch_y_test = torch.from_numpy(y_test).type(torch.LongTensor) # data type is long\n",
        "\n",
        "# Pytorch train and test sets\n",
        "train = torch.utils.data.TensorDataset(torch_X_train,torch_y_train)\n",
        "test = torch.utils.data.TensorDataset(torch_X_test,torch_y_test)\n",
        "\n",
        "# data loader\n",
        "train_loader = torch.utils.data.DataLoader(train, batch_size = BATCH_SIZE, shuffle = False)\n",
        "test_loader = torch.utils.data.DataLoader(test, batch_size = BATCH_SIZE, shuffle = False)"
      ],
      "metadata": {
        "id": "L1h8sHDehxYC"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class MLP(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(MLP, self).__init__()\n",
        "        self.linear1 = nn.Linear(784,250)\n",
        "        self.linear2 = nn.Linear(250,100)\n",
        "        self.linear3 = nn.Linear(100,10)\n",
        "\n",
        "    def forward(self,X):\n",
        "        X = F.relu(self.linear1(X))\n",
        "        X = F.relu(self.linear2(X))\n",
        "        X = self.linear3(X)\n",
        "        return F.log_softmax(X, dim=1)\n",
        "\n",
        "mlp = MLP()\n",
        "print(mlp)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "61CLaIabnPt9",
        "outputId": "98357433-537e-4360-f9a3-515e0b4444a9"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MLP(\n",
            "  (linear1): Linear(in_features=784, out_features=250, bias=True)\n",
            "  (linear2): Linear(in_features=250, out_features=100, bias=True)\n",
            "  (linear3): Linear(in_features=100, out_features=10, bias=True)\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def fit(model, train_loader):\n",
        "    optimizer = torch.optim.Adam(model.parameters())#,lr=0.001, betas=(0.9,0.999))\n",
        "    error = nn.CrossEntropyLoss()\n",
        "    EPOCHS = 5\n",
        "    model.train()\n",
        "    for epoch in range(EPOCHS):\n",
        "        correct = 0\n",
        "        for batch_idx, (X_batch, y_batch) in enumerate(train_loader):\n",
        "            var_X_batch = Variable(X_batch).float()\n",
        "            var_y_batch = Variable(y_batch)\n",
        "            optimizer.zero_grad()\n",
        "            output = model(var_X_batch)\n",
        "            loss = error(output, var_y_batch)\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "\n",
        "            # Total correct predictions\n",
        "            predicted = torch.max(output.data, 1)[1]\n",
        "            correct += (predicted == var_y_batch).sum()\n",
        "            #print(correct)\n",
        "            if batch_idx % 50 == 0:\n",
        "                print('Epoch : {} Accuracy:{:.3f}%'.format( epoch,  float(correct*100) / float(BATCH_SIZE*(batch_idx+1))))"
      ],
      "metadata": {
        "id": "Nwo_FbxDnYrZ"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fit(mlp, train_loader)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vH1z5S0hnkjR",
        "outputId": "024d92f8-2cec-4515-ed27-440f5e4020fc"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch : 0 Accuracy:18.750%\n",
            "Epoch : 0 Accuracy:67.586%\n",
            "Epoch : 0 Accuracy:76.207%\n",
            "Epoch : 0 Accuracy:80.401%\n",
            "Epoch : 0 Accuracy:82.400%\n",
            "Epoch : 0 Accuracy:84.039%\n",
            "Epoch : 0 Accuracy:85.112%\n",
            "Epoch : 0 Accuracy:85.880%\n",
            "Epoch : 0 Accuracy:86.526%\n",
            "Epoch : 0 Accuracy:87.216%\n",
            "Epoch : 1 Accuracy:93.750%\n",
            "Epoch : 1 Accuracy:93.505%\n",
            "Epoch : 1 Accuracy:93.626%\n",
            "Epoch : 1 Accuracy:93.916%\n",
            "Epoch : 1 Accuracy:94.201%\n",
            "Epoch : 1 Accuracy:94.348%\n",
            "Epoch : 1 Accuracy:94.259%\n",
            "Epoch : 1 Accuracy:94.168%\n",
            "Epoch : 1 Accuracy:94.015%\n",
            "Epoch : 1 Accuracy:94.166%\n",
            "Epoch : 2 Accuracy:100.000%\n",
            "Epoch : 2 Accuracy:94.608%\n",
            "Epoch : 2 Accuracy:94.833%\n",
            "Epoch : 2 Accuracy:95.426%\n",
            "Epoch : 2 Accuracy:95.631%\n",
            "Epoch : 2 Accuracy:95.829%\n",
            "Epoch : 2 Accuracy:95.453%\n",
            "Epoch : 2 Accuracy:95.406%\n",
            "Epoch : 2 Accuracy:95.324%\n",
            "Epoch : 2 Accuracy:95.399%\n",
            "Epoch : 3 Accuracy:100.000%\n",
            "Epoch : 3 Accuracy:96.078%\n",
            "Epoch : 3 Accuracy:95.947%\n",
            "Epoch : 3 Accuracy:96.109%\n",
            "Epoch : 3 Accuracy:96.222%\n",
            "Epoch : 3 Accuracy:96.290%\n",
            "Epoch : 3 Accuracy:96.179%\n",
            "Epoch : 3 Accuracy:96.243%\n",
            "Epoch : 3 Accuracy:96.127%\n",
            "Epoch : 3 Accuracy:96.037%\n",
            "Epoch : 4 Accuracy:96.875%\n",
            "Epoch : 4 Accuracy:96.569%\n",
            "Epoch : 4 Accuracy:96.411%\n",
            "Epoch : 4 Accuracy:96.834%\n",
            "Epoch : 4 Accuracy:97.155%\n",
            "Epoch : 4 Accuracy:97.211%\n",
            "Epoch : 4 Accuracy:97.166%\n",
            "Epoch : 4 Accuracy:97.080%\n",
            "Epoch : 4 Accuracy:96.891%\n",
            "Epoch : 4 Accuracy:96.854%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plt.imshow(train[0].reshape(28,28))\n",
        "def evaluate(model):\n",
        "#model = mlp\n",
        "    correct = 0\n",
        "    for test_imgs, test_labels in test_loader:\n",
        "        #print(test_imgs.shape)\n",
        "        test_imgs = Variable(test_imgs).float()\n",
        "        output = model(test_imgs)\n",
        "        predicted = torch.max(output,1)[1]\n",
        "        correct += (predicted == test_labels).sum()\n",
        "        # print(predicted)\n",
        "    print(\"Test accuracy:{:.3f}% \".format( float(correct) / (len(test_loader)*BATCH_SIZE)))\n",
        "evaluate(mlp)"
      ],
      "metadata": {
        "id": "zv0NLQNBLHDK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f8b422d7-3d08-4084-da94-51a518ad2f81"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test accuracy:0.938% \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x,y =train[1]"
      ],
      "metadata": {
        "id": "s6RhEHs3LdPd"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(x.reshape(28,28))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "gGPPalVdj5Lr",
        "outputId": "f8143790-8aa5-4777-ff87-cc5bb6c639cd"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7c925f05c670>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pXAzdeez_WYa"
      },
      "execution_count": 15,
      "outputs": []
    }
  ]
}