MNIST_pytorch.ipynb

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            "grid_template_columns": null,
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            "_model_name": "LayoutModel",
            "justify_items": null,
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            "visibility": null,
            "align_self": null,
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            "left": null
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "THJIMOj2ZkVR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423,
          "referenced_widgets": [
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            "3df3e66bec6340eda0377c1bbb19c7f8",
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            "839039204546473db6bc08e526bdf891",
            "27e75496390c497a98dddd06803d3c97",
            "5373b6ebdf454db4a1b3f2319add41fd",
            "e603052fb876474b9b9e6f895d76c450",
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            "48dced80438446a8a8712020b4538583",
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            "d95055d36a2941f0b5f48eaf04c6a0de",
            "2041749427bb4369b3fc520a169d7d92"
          ]
        },
        "outputId": "8606cc77-6f1c-498e-c5a4-aad1eb32d15b"
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "\n",
        "input_size = 784\n",
        "hidden_size = 100\n",
        "num_classes = 10\n",
        "num_epochs = 2\n",
        "batch_size = 100\n",
        "learning_rate = 0.001\n",
        "\n",
        "\n",
        "train_dataset = torchvision.datasets.MNIST(root='./data', train=True, transform=transforms.ToTensor(), download=True)\n",
        "test_dataset = torchvision.datasets.MNIST(root='./data', train=False, transform=transforms.ToTensor())"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./data/MNIST/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0a9610710dfa42ec831639d4ad3b8be0",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/9912422 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/train-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6adaadfe726e4db39199668f8138211c",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/28881 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/train-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4e9193ec1b5442f6b912f103d1ca1264",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/1648877 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3df3e66bec6340eda0377c1bbb19c7f8",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/4542 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PIwdDaYsaEM3"
      },
      "source": [
        "train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)\n",
        "test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=False)"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R7Rx0mlkzxII",
        "outputId": "2b0cf9a9-2175-419d-cdfc-cae6ce6d5dd4"
      },
      "source": [
        "examples = iter(train_loader)\n",
        "samples, labels = examples.next()\n",
        "print(samples.shape, labels.shape)\n",
        "\n"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([100, 1, 28, 28]) torch.Size([100])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        },
        "id": "UblOynXf0T1G",
        "outputId": "6dabb732-d0e3-4a94-ee7c-e31e8bbc9b1b"
      },
      "source": [
        "for i in range(6):\n",
        "  plt.subplot(2,3,i+1)\n",
        "  plt.imshow(samples[i][0], cmap='gray')\n",
        "plt.show()  "
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 6 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "enO5lqni1CH2"
      },
      "source": [
        "class NeuralNet(nn.Module):\n",
        "  def __init__(self,input_size, hidden_size, num_classes):\n",
        "    super(NeuralNet, self).__init__()\n",
        "    self.l1 = nn.Linear(input_size, hidden_size)\n",
        "    self.relu = nn.functional.relu\n",
        "    self.l2 = nn.Linear(hidden_size, num_classes)\n",
        "\n",
        "  def forward(self, x):\n",
        "    out = self.l1(x)\n",
        "    out = self.relu(out)\n",
        "    out = self.l2(out)\n",
        "    return out\n",
        "\n",
        "model = NeuralNet(input_size, hidden_size, num_classes)    \n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TCJuxHlk7f6u"
      },
      "source": [
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C3SuMqR0ZSHz",
        "outputId": "c7d2033f-cce7-4f8d-adbe-d39260a2f18b"
      },
      "source": [
        "n_total_steps = len(train_loader)\n",
        "for epoch in range(num_epochs):\n",
        "  for i,(images, labels) in enumerate(train_loader):\n",
        "    images = images.reshape(-1, 28*28).to(device)\n",
        "    labels = labels.to(device)\n",
        "\n",
        "    # forward pass\n",
        "    outputs = model(images)\n",
        "    loss = criterion(outputs, labels)\n",
        "\n",
        "    # backward pass\n",
        "    optimizer.zero_grad()\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "    print (f'Epoch [{epoch+1}/{num_epochs}], Step [{i+1}/{n_total_steps}], Loss: {loss.item():.4f}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [1/2], Step [1/600], Loss: 2.2876\n",
            "Epoch [1/2], Step [2/600], Loss: 2.2220\n",
            "Epoch [1/2], Step [3/600], Loss: 2.2080\n",
            "Epoch [1/2], Step [4/600], Loss: 2.1728\n",
            "Epoch [1/2], Step [5/600], Loss: 2.0941\n",
            "Epoch [1/2], Step [6/600], Loss: 2.0781\n",
            "Epoch [1/2], Step [7/600], Loss: 2.0024\n",
            "Epoch [1/2], Step [8/600], Loss: 1.9611\n",
            "Epoch [1/2], Step [9/600], Loss: 1.9535\n",
            "Epoch [1/2], Step [10/600], Loss: 1.8720\n",
            "Epoch [1/2], Step [11/600], Loss: 1.8749\n",
            "Epoch [1/2], Step [12/600], Loss: 1.7637\n",
            "Epoch [1/2], Step [13/600], Loss: 1.7655\n",
            "Epoch [1/2], Step [14/600], Loss: 1.7125\n",
            "Epoch [1/2], Step [15/600], Loss: 1.6039\n",
            "Epoch [1/2], Step [16/600], Loss: 1.6193\n",
            "Epoch [1/2], Step [17/600], Loss: 1.5236\n",
            "Epoch [1/2], Step [18/600], Loss: 1.6088\n",
            "Epoch [1/2], Step [19/600], Loss: 1.4497\n",
            "Epoch [1/2], Step [20/600], Loss: 1.4193\n",
            "Epoch [1/2], Step [21/600], Loss: 1.4807\n",
            "Epoch [1/2], Step [22/600], Loss: 1.2609\n",
            "Epoch [1/2], Step [23/600], Loss: 1.3127\n",
            "Epoch [1/2], Step [24/600], Loss: 1.2640\n",
            "Epoch [1/2], Step [25/600], Loss: 1.1957\n",
            "Epoch [1/2], Step [26/600], Loss: 1.2040\n",
            "Epoch [1/2], Step [27/600], Loss: 1.1134\n",
            "Epoch [1/2], Step [28/600], Loss: 1.0900\n",
            "Epoch [1/2], Step [29/600], Loss: 1.0314\n",
            "Epoch [1/2], Step [30/600], Loss: 1.0823\n",
            "Epoch [1/2], Step [31/600], Loss: 1.0581\n",
            "Epoch [1/2], Step [32/600], Loss: 0.9341\n",
            "Epoch [1/2], Step [33/600], Loss: 1.0224\n",
            "Epoch [1/2], Step [34/600], Loss: 0.9770\n",
            "Epoch [1/2], Step [35/600], Loss: 0.9012\n",
            "Epoch [1/2], Step [36/600], Loss: 0.9279\n",
            "Epoch [1/2], Step [37/600], Loss: 1.0030\n",
            "Epoch [1/2], Step [38/600], Loss: 0.8231\n",
            "Epoch [1/2], Step [39/600], Loss: 0.9358\n",
            "Epoch [1/2], Step [40/600], Loss: 0.8952\n",
            "Epoch [1/2], Step [41/600], Loss: 0.8013\n",
            "Epoch [1/2], Step [42/600], Loss: 0.7520\n",
            "Epoch [1/2], Step [43/600], Loss: 0.6718\n",
            "Epoch [1/2], Step [44/600], Loss: 0.7612\n",
            "Epoch [1/2], Step [45/600], Loss: 0.6560\n",
            "Epoch [1/2], Step [46/600], Loss: 0.5564\n",
            "Epoch [1/2], Step [47/600], Loss: 0.9746\n",
            "Epoch [1/2], Step [48/600], Loss: 0.7213\n",
            "Epoch [1/2], Step [49/600], Loss: 0.7112\n",
            "Epoch [1/2], Step [50/600], Loss: 0.5955\n",
            "Epoch [1/2], Step [51/600], Loss: 0.7034\n",
            "Epoch [1/2], Step [52/600], Loss: 0.7210\n",
            "Epoch [1/2], Step [53/600], Loss: 0.7497\n",
            "Epoch [1/2], Step [54/600], Loss: 0.6390\n",
            "Epoch [1/2], Step [55/600], Loss: 0.5421\n",
            "Epoch [1/2], Step [56/600], Loss: 0.5540\n",
            "Epoch [1/2], Step [57/600], Loss: 0.5917\n",
            "Epoch [1/2], Step [58/600], Loss: 0.6212\n",
            "Epoch [1/2], Step [59/600], Loss: 0.5141\n",
            "Epoch [1/2], Step [60/600], Loss: 0.4535\n",
            "Epoch [1/2], Step [61/600], Loss: 0.6197\n",
            "Epoch [1/2], Step [62/600], Loss: 0.5797\n",
            "Epoch [1/2], Step [63/600], Loss: 0.4584\n",
            "Epoch [1/2], Step [64/600], Loss: 0.5475\n",
            "Epoch [1/2], Step [65/600], Loss: 0.4603\n",
            "Epoch [1/2], Step [66/600], Loss: 0.5521\n",
            "Epoch [1/2], Step [67/600], Loss: 0.3961\n",
            "Epoch [1/2], Step [68/600], Loss: 0.4915\n",
            "Epoch [1/2], Step [69/600], Loss: 0.4951\n",
            "Epoch [1/2], Step [70/600], Loss: 0.5881\n",
            "Epoch [1/2], Step [71/600], Loss: 0.6210\n",
            "Epoch [1/2], Step [72/600], Loss: 0.5317\n",
            "Epoch [1/2], Step [73/600], Loss: 0.4540\n",
            "Epoch [1/2], Step [74/600], Loss: 0.5528\n",
            "Epoch [1/2], Step [75/600], Loss: 0.5652\n",
            "Epoch [1/2], Step [76/600], Loss: 0.5232\n",
            "Epoch [1/2], Step [77/600], Loss: 0.4582\n",
            "Epoch [1/2], Step [78/600], Loss: 0.5565\n",
            "Epoch [1/2], Step [79/600], Loss: 0.5719\n",
            "Epoch [1/2], Step [80/600], Loss: 0.4644\n",
            "Epoch [1/2], Step [81/600], Loss: 0.5168\n",
            "Epoch [1/2], Step [82/600], Loss: 0.5326\n",
            "Epoch [1/2], Step [83/600], Loss: 0.4685\n",
            "Epoch [1/2], Step [84/600], Loss: 0.5745\n",
            "Epoch [1/2], Step [85/600], Loss: 0.3712\n",
            "Epoch [1/2], Step [86/600], Loss: 0.4727\n",
            "Epoch [1/2], Step [87/600], Loss: 0.4044\n",
            "Epoch [1/2], Step [88/600], Loss: 0.4843\n",
            "Epoch [1/2], Step [89/600], Loss: 0.4665\n",
            "Epoch [1/2], Step [90/600], Loss: 0.4751\n",
            "Epoch [1/2], Step [91/600], Loss: 0.4367\n",
            "Epoch [1/2], Step [92/600], Loss: 0.5423\n",
            "Epoch [1/2], Step [93/600], Loss: 0.3373\n",
            "Epoch [1/2], Step [94/600], Loss: 0.4856\n",
            "Epoch [1/2], Step [95/600], Loss: 0.4319\n",
            "Epoch [1/2], Step [96/600], Loss: 0.4705\n",
            "Epoch [1/2], Step [97/600], Loss: 0.4660\n",
            "Epoch [1/2], Step [98/600], Loss: 0.3465\n",
            "Epoch [1/2], Step [99/600], Loss: 0.5543\n",
            "Epoch [1/2], Step [100/600], Loss: 0.4312\n",
            "Epoch [1/2], Step [101/600], Loss: 0.4345\n",
            "Epoch [1/2], Step [102/600], Loss: 0.2972\n",
            "Epoch [1/2], Step [103/600], Loss: 0.3541\n",
            "Epoch [1/2], Step [104/600], Loss: 0.3607\n",
            "Epoch [1/2], Step [105/600], Loss: 0.3898\n",
            "Epoch [1/2], Step [106/600], Loss: 0.4449\n",
            "Epoch [1/2], Step [107/600], Loss: 0.5106\n",
            "Epoch [1/2], Step [108/600], Loss: 0.4224\n",
            "Epoch [1/2], Step [109/600], Loss: 0.4460\n",
            "Epoch [1/2], Step [110/600], Loss: 0.4272\n",
            "Epoch [1/2], Step [111/600], Loss: 0.2977\n",
            "Epoch [1/2], Step [112/600], Loss: 0.3702\n",
            "Epoch [1/2], Step [113/600], Loss: 0.4386\n",
            "Epoch [1/2], Step [114/600], Loss: 0.4754\n",
            "Epoch [1/2], Step [115/600], Loss: 0.2993\n",
            "Epoch [1/2], Step [116/600], Loss: 0.4106\n",
            "Epoch [1/2], Step [117/600], Loss: 0.3983\n",
            "Epoch [1/2], Step [118/600], Loss: 0.4446\n",
            "Epoch [1/2], Step [119/600], Loss: 0.4775\n",
            "Epoch [1/2], Step [120/600], Loss: 0.4634\n",
            "Epoch [1/2], Step [121/600], Loss: 0.4712\n",
            "Epoch [1/2], Step [122/600], Loss: 0.3906\n",
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            "Epoch [1/2], Step [125/600], Loss: 0.5193\n",
            "Epoch [1/2], Step [126/600], Loss: 0.4083\n",
            "Epoch [1/2], Step [127/600], Loss: 0.4267\n",
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            "Epoch [1/2], Step [130/600], Loss: 0.3268\n",
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            "Epoch [1/2], Step [133/600], Loss: 0.3487\n",
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            "Epoch [1/2], Step [138/600], Loss: 0.3526\n",
            "Epoch [1/2], Step [139/600], Loss: 0.3882\n",
            "Epoch [1/2], Step [140/600], Loss: 0.3908\n",
            "Epoch [1/2], Step [141/600], Loss: 0.3164\n",
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            "Epoch [1/2], Step [146/600], Loss: 0.3873\n",
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            "Epoch [1/2], Step [157/600], Loss: 0.4772\n",
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            "Epoch [1/2], Step [159/600], Loss: 0.6617\n",
            "Epoch [1/2], Step [160/600], Loss: 0.2871\n",
            "Epoch [1/2], Step [161/600], Loss: 0.3627\n",
            "Epoch [1/2], Step [162/600], Loss: 0.3232\n",
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            "Epoch [1/2], Step [170/600], Loss: 0.3356\n",
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            "Epoch [1/2], Step [176/600], Loss: 0.2255\n",
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            "Epoch [1/2], Step [179/600], Loss: 0.4848\n",
            "Epoch [1/2], Step [180/600], Loss: 0.3732\n",
            "Epoch [1/2], Step [181/600], Loss: 0.3540\n",
            "Epoch [1/2], Step [182/600], Loss: 0.2631\n",
            "Epoch [1/2], Step [183/600], Loss: 0.5785\n",
            "Epoch [1/2], Step [184/600], Loss: 0.4366\n",
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            "Epoch [1/2], Step [189/600], Loss: 0.3543\n",
            "Epoch [1/2], Step [190/600], Loss: 0.4549\n",
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            "Epoch [1/2], Step [196/600], Loss: 0.3687\n",
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            "Epoch [1/2], Step [198/600], Loss: 0.2830\n",
            "Epoch [1/2], Step [199/600], Loss: 0.4194\n",
            "Epoch [1/2], Step [200/600], Loss: 0.3089\n",
            "Epoch [1/2], Step [201/600], Loss: 0.3023\n",
            "Epoch [1/2], Step [202/600], Loss: 0.4016\n",
            "Epoch [1/2], Step [203/600], Loss: 0.3037\n",
            "Epoch [1/2], Step [204/600], Loss: 0.1827\n",
            "Epoch [1/2], Step [205/600], Loss: 0.3496\n",
            "Epoch [1/2], Step [206/600], Loss: 0.2745\n",
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            "Epoch [1/2], Step [208/600], Loss: 0.3289\n",
            "Epoch [1/2], Step [209/600], Loss: 0.3357\n",
            "Epoch [1/2], Step [210/600], Loss: 0.4315\n",
            "Epoch [1/2], Step [211/600], Loss: 0.4167\n",
            "Epoch [1/2], Step [212/600], Loss: 0.1864\n",
            "Epoch [1/2], Step [213/600], Loss: 0.3985\n",
            "Epoch [1/2], Step [214/600], Loss: 0.4166\n",
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            "Epoch [1/2], Step [219/600], Loss: 0.2872\n",
            "Epoch [1/2], Step [220/600], Loss: 0.2951\n",
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            "Epoch [1/2], Step [222/600], Loss: 0.4007\n",
            "Epoch [1/2], Step [223/600], Loss: 0.3067\n",
            "Epoch [1/2], Step [224/600], Loss: 0.3412\n",
            "Epoch [1/2], Step [225/600], Loss: 0.1757\n",
            "Epoch [1/2], Step [226/600], Loss: 0.2148\n",
            "Epoch [1/2], Step [227/600], Loss: 0.4825\n",
            "Epoch [1/2], Step [228/600], Loss: 0.3908\n",
            "Epoch [1/2], Step [229/600], Loss: 0.3383\n",
            "Epoch [1/2], Step [230/600], Loss: 0.2395\n",
            "Epoch [1/2], Step [231/600], Loss: 0.3534\n",
            "Epoch [1/2], Step [232/600], Loss: 0.2828\n",
            "Epoch [1/2], Step [233/600], Loss: 0.3217\n",
            "Epoch [1/2], Step [234/600], Loss: 0.2091\n",
            "Epoch [1/2], Step [235/600], Loss: 0.1625\n",
            "Epoch [1/2], Step [236/600], Loss: 0.2842\n",
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            "Epoch [1/2], Step [238/600], Loss: 0.3341\n",
            "Epoch [1/2], Step [239/600], Loss: 0.2575\n",
            "Epoch [1/2], Step [240/600], Loss: 0.2316\n",
            "Epoch [1/2], Step [241/600], Loss: 0.2654\n",
            "Epoch [1/2], Step [242/600], Loss: 0.3845\n",
            "Epoch [1/2], Step [243/600], Loss: 0.3565\n",
            "Epoch [1/2], Step [244/600], Loss: 0.3317\n",
            "Epoch [1/2], Step [245/600], Loss: 0.2383\n",
            "Epoch [1/2], Step [246/600], Loss: 0.2408\n",
            "Epoch [1/2], Step [247/600], Loss: 0.3722\n",
            "Epoch [1/2], Step [248/600], Loss: 0.3092\n",
            "Epoch [1/2], Step [249/600], Loss: 0.3351\n",
            "Epoch [1/2], Step [250/600], Loss: 0.2804\n",
            "Epoch [1/2], Step [251/600], Loss: 0.3273\n",
            "Epoch [1/2], Step [252/600], Loss: 0.5004\n",
            "Epoch [1/2], Step [253/600], Loss: 0.5263\n",
            "Epoch [1/2], Step [254/600], Loss: 0.3518\n",
            "Epoch [1/2], Step [255/600], Loss: 0.3768\n",
            "Epoch [1/2], Step [256/600], Loss: 0.3768\n",
            "Epoch [1/2], Step [257/600], Loss: 0.2517\n",
            "Epoch [1/2], Step [258/600], Loss: 0.4682\n",
            "Epoch [1/2], Step [259/600], Loss: 0.3638\n",
            "Epoch [1/2], Step [260/600], Loss: 0.2602\n",
            "Epoch [1/2], Step [261/600], Loss: 0.2759\n",
            "Epoch [1/2], Step [262/600], Loss: 0.3159\n",
            "Epoch [1/2], Step [263/600], Loss: 0.4495\n",
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            "Epoch [1/2], Step [265/600], Loss: 0.3529\n",
            "Epoch [1/2], Step [266/600], Loss: 0.2089\n",
            "Epoch [1/2], Step [267/600], Loss: 0.2326\n",
            "Epoch [1/2], Step [268/600], Loss: 0.3538\n",
            "Epoch [1/2], Step [269/600], Loss: 0.2488\n",
            "Epoch [1/2], Step [270/600], Loss: 0.2951\n",
            "Epoch [1/2], Step [271/600], Loss: 0.2401\n",
            "Epoch [1/2], Step [272/600], Loss: 0.2564\n",
            "Epoch [1/2], Step [273/600], Loss: 0.4468\n",
            "Epoch [1/2], Step [274/600], Loss: 0.3105\n",
            "Epoch [1/2], Step [275/600], Loss: 0.2513\n",
            "Epoch [1/2], Step [276/600], Loss: 0.4090\n",
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            "Epoch [1/2], Step [280/600], Loss: 0.2541\n",
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            "Epoch [2/2], Step [595/600], Loss: 0.3855\n",
            "Epoch [2/2], Step [596/600], Loss: 0.1816\n",
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            "Epoch [2/2], Step [598/600], Loss: 0.1072\n",
            "Epoch [2/2], Step [599/600], Loss: 0.0866\n",
            "Epoch [2/2], Step [600/600], Loss: 0.1282\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cgVh_4jdpY2O",
        "outputId": "7753f38b-b6e4-48e7-8d90-b61c2ded60af"
      },
      "source": [
        "with torch.no_grad():\n",
        "  n_correct = 0\n",
        "  n_samples = 0\n",
        "  for images, labels in test_loader:\n",
        "    images = images.reshape(-1, 28*28).to(device)\n",
        "    labels = labels.to(device)\n",
        "    outputs = model(images)\n",
        "    _, predicted = torch.max(outputs.data, 1)  #1 indicates return max of row # predicted will store index of the label having highest probability\n",
        "    n_samples += labels.size(0)  \n",
        "    n_correct += (predicted==labels).sum().item()\n",
        "\n",
        "  acc = 100.0 * n_correct / n_samples\n",
        "  print(f'Accuracy of the network on the 10000 test images: {acc} %')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of the network on the 10000 test images: 95.42 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YZ-skacIzFcw"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}