pytorch_tensor.ipynb

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      "source": [
        "import numpy as np\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.datasets import fetch_california_housing"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "California = fetch_california_housing()\n",
        "df = pd.DataFrame(California.data, columns=California.feature_names).iloc[:,0:6]\n",
        "df['target'] = California.target\n",
        "df.head()"
      ],
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      "execution_count": 35,
      "outputs": [
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          "data": {
            "text/plain": [
              "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  target\n",
              "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556   4.526\n",
              "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842   3.585\n",
              "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260   3.521\n",
              "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945   3.413\n",
              "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467   3.422"
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              "      <th></th>\n",
              "      <th>MedInc</th>\n",
              "      <th>HouseAge</th>\n",
              "      <th>AveRooms</th>\n",
              "      <th>AveBedrms</th>\n",
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              "      <td>8.3252</td>\n",
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              "      <td>322.0</td>\n",
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              "      <td>4.526</td>\n",
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              "      <th>1</th>\n",
              "      <td>8.3014</td>\n",
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              "      <td>3.413</td>\n",
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              "      <th>4</th>\n",
              "      <td>3.8462</td>\n",
              "      <td>52.0</td>\n",
              "      <td>6.281853</td>\n",
              "      <td>1.081081</td>\n",
              "      <td>565.0</td>\n",
              "      <td>2.181467</td>\n",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 20640,\n  \"fields\": [\n    {\n      \"column\": \"MedInc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.8998217179452732,\n        \"min\": 0.4999,\n        \"max\": 15.0001,\n        \"num_unique_values\": 12928,\n        \"samples\": [\n          5.0286,\n          2.0433,\n          6.1228\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HouseAge\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.585557612111637,\n        \"min\": 1.0,\n        \"max\": 52.0,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          35.0,\n          25.0,\n          7.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AveRooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.4741731394243205,\n        \"min\": 0.8461538461538461,\n        \"max\": 141.9090909090909,\n        \"num_unique_values\": 19392,\n        \"samples\": [\n          6.111269614835948,\n          5.912820512820513,\n          5.7924528301886795\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AveBedrms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.47391085679546435,\n        \"min\": 0.3333333333333333,\n        \"max\": 34.06666666666667,\n        \"num_unique_values\": 14233,\n        \"samples\": [\n          0.9906542056074766,\n          1.112099644128114,\n          1.0398230088495575\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1132.4621217653375,\n        \"min\": 3.0,\n        \"max\": 35682.0,\n        \"num_unique_values\": 3888,\n        \"samples\": [\n          4169.0,\n          636.0,\n          3367.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AveOccup\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.386049562213591,\n        \"min\": 0.6923076923076923,\n        \"max\": 1243.3333333333333,\n        \"num_unique_values\": 18841,\n        \"samples\": [\n          2.6939799331103678,\n          3.559375,\n          3.297082228116711\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1539561587441483,\n        \"min\": 0.14999,\n        \"max\": 5.00001,\n        \"num_unique_values\": 3842,\n        \"samples\": [\n          1.943,\n          3.79,\n          2.301\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(California.DESCR)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QaLjqT72dv4c",
        "outputId": "7bfbfc85-320d-4a34-f76d-3d05821d2faf"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ".. _california_housing_dataset:\n",
            "\n",
            "California Housing dataset\n",
            "--------------------------\n",
            "\n",
            "**Data Set Characteristics:**\n",
            "\n",
            ":Number of Instances: 20640\n",
            "\n",
            ":Number of Attributes: 8 numeric, predictive attributes and the target\n",
            "\n",
            ":Attribute Information:\n",
            "    - MedInc        median income in block group\n",
            "    - HouseAge      median house age in block group\n",
            "    - AveRooms      average number of rooms per household\n",
            "    - AveBedrms     average number of bedrooms per household\n",
            "    - Population    block group population\n",
            "    - AveOccup      average number of household members\n",
            "    - Latitude      block group latitude\n",
            "    - Longitude     block group longitude\n",
            "\n",
            ":Missing Attribute Values: None\n",
            "\n",
            "This dataset was obtained from the StatLib repository.\n",
            "https://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html\n",
            "\n",
            "The target variable is the median house value for California districts,\n",
            "expressed in hundreds of thousands of dollars ($100,000).\n",
            "\n",
            "This dataset was derived from the 1990 U.S. census, using one row per census\n",
            "block group. A block group is the smallest geographical unit for which the U.S.\n",
            "Census Bureau publishes sample data (a block group typically has a population\n",
            "of 600 to 3,000 people).\n",
            "\n",
            "A household is a group of people residing within a home. Since the average\n",
            "number of rooms and bedrooms in this dataset are provided per household, these\n",
            "columns may take surprisingly large values for block groups with few households\n",
            "and many empty houses, such as vacation resorts.\n",
            "\n",
            "It can be downloaded/loaded using the\n",
            ":func:`sklearn.datasets.fetch_california_housing` function.\n",
            "\n",
            ".. rubric:: References\n",
            "\n",
            "- Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions,\n",
            "  Statistics and Probability Letters, 33 (1997) 291-297\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.iloc[:,0:6].values\n",
        "y = df.iloc[:,-1].values"
      ],
      "metadata": {
        "id": "IeswdpNfdvy_"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
      ],
      "metadata": {
        "id": "dx8JPhHQfkkG"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = torch.tensor(X_train).float()\n",
        "X_test = torch.tensor(X_test).float()\n",
        "y_train = torch.tensor(y_train).float().unsqueeze(1) # unsqueeze one add a dimension of size 1 to the tensor that we want to create\n",
        "y_test = torch.tensor(y_test).float().unsqueeze(1) # unsqueeze one add a dimension of size 1 to the tensor that we want to create"
      ],
      "metadata": {
        "id": "KKLLT1HnfkWk"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mean = X_train.mean(dim=0)\n",
        "std = X_train.std(dim=0)\n",
        "X_train = (X_train - mean)/std\n",
        "X_test = (X_test - mean)/std"
      ],
      "metadata": {
        "id": "YpJkdeEtdvu0"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "type(X_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yAvtNrrMven-",
        "outputId": "d3f2b1ca-4d3d-4746-c1bb-cfb05166a6bd"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Tensor"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class LinearRegression(nn.Module):\n",
        "    def __init__(self, input_size, output_size):\n",
        "        super().__init__()\n",
        "        self.fc = nn.Linear(input_size, 128, bias=True)\n",
        "        self.out = nn.Linear(128, output_size, bias=True)\n",
        "        self.relu = nn.ReLU()\n",
        "\n",
        "    def forward(self, x):\n",
        "      x = self.fc(x)\n",
        "      x = self.relu(x)\n",
        "      x = self.out(x)\n",
        "      return x\n"
      ],
      "metadata": {
        "id": "SriC23DNhAIM"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = LinearRegression(input_size = X_train.shape[1], output_size = 1)"
      ],
      "metadata": {
        "id": "SaQuYdichAAp"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "criterion = nn.MSELoss()\n",
        "optimizer = optim.SGD(model.parameters(), lr = 0.01)"
      ],
      "metadata": {
        "id": "3BBJXYkQdvnB"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "losses = []\n",
        "num_epochs = 1000\n",
        "\n",
        "for epoch in range(num_epochs):\n",
        "    y_pred = model(X_train)\n",
        "    loss = criterion(y_pred, y_train)\n",
        "\n",
        "    optimizer.zero_grad()#otherwise gradient will be accumulated, so have zero_grad() ensure the gradient only from the current pass\n",
        "    loss.backward()#compute the gradient of loss wrt model parameters\n",
        "    optimizer.step()#update model paramters based on grad computed on backword using optimziation algorithms.\n",
        "\n",
        "    if (epoch+1)%100 == 0:\n",
        "      print(f'Epoch [{epoch+1}/1000, Loss: {loss.item(): .4f}]')\n",
        "\n",
        "    losses.append(loss.item())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Qc0QXc3dvi0",
        "outputId": "ca4ed381-754c-4855-8524-ebe6c2b07cc7"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [100/1000, Loss:  0.5280]\n",
            "Epoch [200/1000, Loss:  0.5240]\n",
            "Epoch [300/1000, Loss:  0.5204]\n",
            "Epoch [400/1000, Loss:  0.5170]\n",
            "Epoch [500/1000, Loss:  0.5137]\n",
            "Epoch [600/1000, Loss:  0.5107]\n",
            "Epoch [700/1000, Loss:  0.5079]\n",
            "Epoch [800/1000, Loss:  0.5052]\n",
            "Epoch [900/1000, Loss:  0.5027]\n",
            "Epoch [1000/1000, Loss:  0.5002]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.plot(range(1000), losses)\n",
        "plt.xlabel('Iteration')\n",
        "plt.ylabel('Loss')\n",
        "plt.title('Loss over iterations')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "Sx3jhhD2dveu",
        "outputId": "09985c2a-7edf-4522-eb48-4e05ad700ec5"
      },
      "execution_count": 71,
      "outputs": [
        {
          "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": [
        "with torch.no_grad(): #optimzie memory usage and computation speed\n",
        "  y_pred = model(X_test)\n",
        "  test_loss = criterion(y_pred, y_test)\n",
        "  MAE = nn.L1Loss() # L1Loss is the Mean Absolute Error (MAE)\n",
        "  mae = MAE(y_pred, y_test)\n",
        "  print(f'Test Loss: {test_loss.item(): .4f}')\n",
        "  print(f'Test Loss: {mae.item(): .4f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uNLrcGj1dvXp",
        "outputId": "037ccf2a-6b08-443d-b2ab-7fc6a6c258cf"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss:  0.5042\n",
            "Test Loss:  0.5154\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def mean_absolute_percentage_error(y_true, y_pred):\n",
        "    return 100*torch.mean(torch.abs((y_true-y_pred)/y_true))"
      ],
      "metadata": {
        "id": "nnyZ4wYGzih9"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mape = mean_absolute_percentage_error(y_test, y_pred)\n",
        "mape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X89lXw6ZziYD",
        "outputId": "82aa5f2c-1e17-46d5-f462-3f2f09dec5a3"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(31.0294)"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3FpN_PQFziTY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torchvision\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import torchvision.transforms as transforms\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "cH0OtmX24sBV"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transforms.ToTensor())\n",
        "test = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transforms.ToTensor())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2GK9qNlE4r99",
        "outputId": "2a43e354-c6f3-456b-e738-b52fecc8b1c5"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 170M/170M [00:12<00:00, 13.4MB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/cifar-10-python.tar.gz to ./data\n",
            "Files already downloaded and verified\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HvV67J254r7F",
        "outputId": "65c2514b-76a3-411e-b4e4-f706af29c9c5"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor([[[0.2314, 0.1686, 0.1961,  ..., 0.6196, 0.5961, 0.5804],\n",
              "          [0.0627, 0.0000, 0.0706,  ..., 0.4824, 0.4667, 0.4784],\n",
              "          [0.0980, 0.0627, 0.1922,  ..., 0.4627, 0.4706, 0.4275],\n",
              "          ...,\n",
              "          [0.8157, 0.7882, 0.7765,  ..., 0.6275, 0.2196, 0.2078],\n",
              "          [0.7059, 0.6784, 0.7294,  ..., 0.7216, 0.3804, 0.3255],\n",
              "          [0.6941, 0.6588, 0.7020,  ..., 0.8471, 0.5922, 0.4824]],\n",
              " \n",
              "         [[0.2431, 0.1804, 0.1882,  ..., 0.5176, 0.4902, 0.4863],\n",
              "          [0.0784, 0.0000, 0.0314,  ..., 0.3451, 0.3255, 0.3412],\n",
              "          [0.0941, 0.0275, 0.1059,  ..., 0.3294, 0.3294, 0.2863],\n",
              "          ...,\n",
              "          [0.6667, 0.6000, 0.6314,  ..., 0.5216, 0.1216, 0.1333],\n",
              "          [0.5451, 0.4824, 0.5647,  ..., 0.5804, 0.2431, 0.2078],\n",
              "          [0.5647, 0.5059, 0.5569,  ..., 0.7216, 0.4627, 0.3608]],\n",
              " \n",
              "         [[0.2471, 0.1765, 0.1686,  ..., 0.4235, 0.4000, 0.4039],\n",
              "          [0.0784, 0.0000, 0.0000,  ..., 0.2157, 0.1961, 0.2235],\n",
              "          [0.0824, 0.0000, 0.0314,  ..., 0.1961, 0.1961, 0.1647],\n",
              "          ...,\n",
              "          [0.3765, 0.1333, 0.1020,  ..., 0.2745, 0.0275, 0.0784],\n",
              "          [0.3765, 0.1647, 0.1176,  ..., 0.3686, 0.1333, 0.1333],\n",
              "          [0.4549, 0.3686, 0.3412,  ..., 0.5490, 0.3294, 0.2824]]]),\n",
              " 6)"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels = [item[1] for item in train]\n",
        "num_classes = len(set(labels))\n",
        "num_classes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gaS5lX664r3s",
        "outputId": "9a074331-62b6-495f-f39d-97ec8b77d0c3"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "image, label = train[20]\n",
        "print('Shape :', image.shape, '\\nlabel :', label)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XKO7Nwzn4rzR",
        "outputId": "58e7cd9b-909e-4775-8d71-e1ec01b8c2f7"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape : torch.Size([3, 32, 32]) \n",
            "label : 4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(image.T, interpolation='bicubic') #input of imshow should be (M, N, 3), but we have (3,N, M)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "f2XKAEt34rwJ",
        "outputId": "4eba32ec-3144-4025-f9ad-71525b1af877"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7deef2493460>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "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": [
        "train_loader = torch.utils.data.DataLoader(train, batch_size=50, shuffle=True)\n",
        "test_loader = torch.utils.data.DataLoader(test, batch_size=50, shuffle=True)"
      ],
      "metadata": {
        "id": "7bmL0ihe4rsx"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class ANN(nn.Module):\n",
        "    def __init__(self, input_size, hidden_size1, hidden_size2, num_classes):\n",
        "        super(ANN, self).__init__()\n",
        "        self.fc1 = nn.Linear(input_size, hidden_size1, bias = True)\n",
        "        self.relu = nn.ReLU()\n",
        "        self.fc2 = nn.Linear(hidden_size1, hidden_size2, bias = True)\n",
        "        self.output = nn.Linear(hidden_size2, num_classes, bias = True)\n",
        "\n",
        "    def forward(self, x):\n",
        "        out = x.view(x.size(0), -1) #tensor is 3D tensor, we need to flatten it to 2D tensor, 1st dim is batch size, -1 to flatten the other dimension of x to the 2nd dimension\n",
        "        out = self.fc1(out)\n",
        "        out = self.relu(out)\n",
        "        out = self.fc2(out)\n",
        "        out = self.relu(out)\n",
        "        out = self.output(out)\n",
        "        return out"
      ],
      "metadata": {
        "id": "7-lyZmOV4rp6"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = torch.randn(2,3,4)\n",
        "x.view(x.shape[0], -1) #flattening to 2D tensor"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r-atUIDs4rmz",
        "outputId": "3370e2dd-05f5-4a26-dfac-3bf1993de2d4"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[-0.6776, -0.1772,  0.9863,  0.3104,  0.0296,  0.1089, -1.8672,  0.5764,\n",
              "          1.5158,  1.5134,  1.9053,  0.7830],\n",
              "        [ 0.1522,  0.2359, -0.1827,  0.5589,  1.2105,  1.1294, -1.8925,  0.0477,\n",
              "         -1.2177, -2.5333, -1.2784,  0.2392]])"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_size = 3*32*32\n",
        "hidden_size1 = 120\n",
        "hidden_size2 = 100\n",
        "num_classes = 10\n",
        "model = ANN(input_size, hidden_size1, hidden_size2, num_classes)\n",
        "#"
      ],
      "metadata": {
        "id": "4ECKwOZ14rkA"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = optim.SGD(model.parameters(), lr = 0.01)"
      ],
      "metadata": {
        "id": "qwLV1Uoz4rhE"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "loss_list = []\n",
        "accuracy_list = []\n",
        "\n",
        "for epoch in range(30):\n",
        "    epoch_loss = 0\n",
        "    for i, (images, labels) in enumerate(train_loader):\n",
        "        outputs = model(images)\n",
        "        loss = criterion(outputs, labels)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        epoch_loss += loss.item()\n",
        "\n",
        "    loss_list.append(epoch_loss/len(train_loader))\n",
        "\n",
        "    with torch.no_grad():\n",
        "        correct = 0\n",
        "        total = 0\n",
        "        for images, labels in train_loader:\n",
        "            outputs = model(images)\n",
        "            _, predicted = torch.max(outputs.data, 1)\n",
        "            total += labels.size(0)\n",
        "            correct += (predicted == labels).sum().item()\n",
        "\n",
        "        accuracy = 100*correct/total\n",
        "        accuracy_list.append(accuracy)\n",
        "\n",
        "    print(f\"Epoch [{epoch+1}/30] Training Accuracy: {accuracy:.4f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "94PKhJhF4reN",
        "outputId": "2ef46020-8e02-48a4-d65e-d94d0c30ac50"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [1/30] Training Accuracy: 31.0540%\n",
            "Epoch [2/30] Training Accuracy: 34.7620%\n",
            "Epoch [3/30] Training Accuracy: 36.6460%\n",
            "Epoch [4/30] Training Accuracy: 39.0280%\n",
            "Epoch [5/30] Training Accuracy: 39.8860%\n",
            "Epoch [6/30] Training Accuracy: 41.8700%\n",
            "Epoch [7/30] Training Accuracy: 43.7180%\n",
            "Epoch [8/30] Training Accuracy: 44.8760%\n",
            "Epoch [9/30] Training Accuracy: 46.5440%\n",
            "Epoch [10/30] Training Accuracy: 47.3000%\n",
            "Epoch [11/30] Training Accuracy: 44.8420%\n",
            "Epoch [12/30] Training Accuracy: 48.2740%\n",
            "Epoch [13/30] Training Accuracy: 48.8560%\n",
            "Epoch [14/30] Training Accuracy: 49.2100%\n",
            "Epoch [15/30] Training Accuracy: 50.4160%\n",
            "Epoch [16/30] Training Accuracy: 50.7760%\n",
            "Epoch [17/30] Training Accuracy: 51.4440%\n",
            "Epoch [18/30] Training Accuracy: 50.8200%\n",
            "Epoch [19/30] Training Accuracy: 52.4220%\n",
            "Epoch [20/30] Training Accuracy: 52.8440%\n",
            "Epoch [21/30] Training Accuracy: 51.2280%\n",
            "Epoch [22/30] Training Accuracy: 52.6220%\n",
            "Epoch [23/30] Training Accuracy: 54.2820%\n",
            "Epoch [24/30] Training Accuracy: 53.2260%\n",
            "Epoch [25/30] Training Accuracy: 55.0960%\n",
            "Epoch [26/30] Training Accuracy: 54.6200%\n",
            "Epoch [27/30] Training Accuracy: 55.8900%\n",
            "Epoch [28/30] Training Accuracy: 54.1240%\n",
            "Epoch [29/30] Training Accuracy: 55.7520%\n",
            "Epoch [30/30] Training Accuracy: 56.4980%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bSEVMpm_4rbV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "K7e8cgEu4rYd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mBsWPgCY4rVV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "V3CyoKIL4rSv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hRiMCWfh4rPm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jr5BM0U_4rMe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vupv_azS4rI0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "MTSsxfbhdvMu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "This is a **test**"
      ],
      "metadata": {
        "id": "N91Ii4rtJV4Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "np1 = np.array([1,2,3,4,5,6])\n",
        "type(np1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5ZeoEJ-tLO9n",
        "outputId": "d963eac6-f7d3-4be4-f586-2e3666c8345d"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np2 = np.array([[1,2,3],[4,5,6]])\n",
        "type(np2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XZsbIQByLS_u",
        "outputId": "10fa2f78-e918-4f0b-d59f-7c1c865d7fb0"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np2.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9MNXdCFNLiPF",
        "outputId": "902cbdd3-11b0-4c14-c366-d377eb21ace4"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np2.size"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BVej9LJfLsmB",
        "outputId": "84bd715c-3a47-411b-9ce9-1af930a747da"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np2.ndim\n",
        "np1.ndim"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cx3etsliL4gK",
        "outputId": "b88b24d9-eab9-4851-8b0f-a60c7668e591"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np1[0]\n",
        "np1[3:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3Ya5C6yWL_kR",
        "outputId": "c0858e6e-f013-40ca-fe3d-aa801721e8cc"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([4, 5])"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np2[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wDPbxC1jMRoL",
        "outputId": "abf098c8-f50a-4e32-95a6-9f9a353a8914"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2, 3])"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np3 = np.array([[1,2],[3,4]])\n",
        "np4 = np.array([[5,6],[7,8]])"
      ],
      "metadata": {
        "id": "Wr9ojRm6MplV"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np3+np4"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W6krZfaCNwmb",
        "outputId": "626bedf3-5527-4068-866c-c0b707e13489"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 6,  8],\n",
              "       [10, 12]])"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np3/np4"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z4_-zwKnNyHB",
        "outputId": "4fcf740a-752d-4b21-dee3-6de22ecc2a90"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.2       , 0.33333333],\n",
              "       [0.42857143, 0.5       ]])"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np3*np4"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r8_BquXhN19I",
        "outputId": "d1351fb7-2a10-416d-86f7-4a7adf710337"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 5, 12],\n",
              "       [21, 32]])"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np3@np4"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6zmVT5x5N6zx",
        "outputId": "b8aa945b-ab2c-4ad6-909f-c03141196b50"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[19, 22],\n",
              "       [43, 50]])"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch"
      ],
      "metadata": {
        "id": "0DoLWi5-N-Mm"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "arr1 = np.array([])\n",
        "arr1.dtype\n",
        "# ype(arr1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d4ZyEcDZRWd7",
        "outputId": "04081e1b-9d49-49ad-e1d7-926a02ad1b56"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('float64')"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tens1 = torch.tensor([])\n",
        "type(tens1)\n",
        "tens1.dtype"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XxM-XVjXR1J7",
        "outputId": "68d3ef38-d194-4dd1-c28c-fa68619fff8f"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.float32"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "arr2 = np.array([1,2,3,4])\n",
        "arr2\n",
        "type(arr2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ksJCLqqOSLRK",
        "outputId": "c735ca12-5bf0-43d2-8a24-48f1dfd98c61"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tens2 = torch.from_numpy(arr2)\n",
        "tens2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mi-Yn6iySyOo",
        "outputId": "f42df269-4b29-4de4-af24-9bd71022172e"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([1, 2, 3, 4])"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tens2[1] = 10\n",
        "tens2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NvPz_GSvTQHE",
        "outputId": "0d7ff804-08ce-4195-a28f-c271c1237268"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([ 1, 10,  3,  4])"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "arr2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F5V-jPJDTdFo",
        "outputId": "18d64a5a-997c-4001-d9b5-f62ee3b6fb3d"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 1, 10,  3,  4])"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "arr3 = np.array([10,11,12,13])\n",
        "tens3 = torch.tensor(arr3)\n",
        "tens3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x2NaJbhVTsCL",
        "outputId": "a29206e3-2686-4192-cc69-25d23a8844e3"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([10, 11, 12, 13])"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tens3[0] = 200\n",
        "tens3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5htA2LFkUYOC",
        "outputId": "bc9d8870-8126-45fc-aa6a-1727019fe88c"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([200,  11,  12,  13])"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "arr3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PC-YMfddUkx6",
        "outputId": "e21fa590-970a-4787-a1d4-ce4d195c3532"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([10, 11, 12, 13])"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Et_gsXIMUl1v"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}