ml_101.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "2WmlUAqSpjfL",
        "outputId": "ae7749f6-dc57-4be6-a628-52ee76934032",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: onedrivedownloader in /usr/local/lib/python3.10/dist-packages (1.1.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from onedrivedownloader) (2.31.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from onedrivedownloader) (4.66.4)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->onedrivedownloader) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->onedrivedownloader) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->onedrivedownloader) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->onedrivedownloader) (2024.7.4)\n"
          ]
        }
      ],
      "source": [
        "# install dependency for Google Colab\n",
        "!pip install onedrivedownloader"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "KuXKryyLXSCj"
      },
      "outputs": [],
      "source": [
        "from datetime import timedelta\n",
        "import math\n",
        "from time import time\n",
        "import os\n",
        "import shutil\n",
        "import random\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from tqdm.auto import tqdm\n",
        "from PIL import Image\n",
        "\n",
        "from sklearn.neural_network import MLPRegressor\n",
        "from sklearn.linear_model import *\n",
        "from sklearn.svm import *\n",
        "from sklearn.gaussian_process import GaussianProcessRegressor\n",
        "from sklearn.ensemble import *\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import torch\n",
        "import torchvision\n",
        "from torchvision.datasets import MNIST\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from torchvision import datasets, transforms\n",
        "import torchvision.transforms as transforms\n",
        "from torch.utils.data import random_split, DataLoader\n",
        "import torch.nn.functional as F\n",
        "\n",
        "from onedrivedownloader import download"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pl2lkHpZRoV1"
      },
      "source": [
        "# Fitting a function"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZFe4xJcGpety"
      },
      "source": [
        "## Plotting a curve"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "5HEJ7jtnydgt"
      },
      "outputs": [],
      "source": [
        "granularity = 0.05\n",
        "x = np.arange(0, 2*np.pi, granularity).reshape(-1, 1)\n",
        "y = np.sin(x).reshape(-1, 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HTn_VOSP0FPC",
        "outputId": "482f08d9-8613-4e3a-8649-6eaa6db3a8da"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "126"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "len(x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "GyKKE8v-yhrf",
        "outputId": "912cb53f-b04e-4e51-f0a0-ec509b21f482"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.plot(x, y)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "rcB28epYy7XQ"
      },
      "outputs": [],
      "source": [
        "# TODO: sample out some training data and add a bit of noise to it\n",
        "# If numpy array indexing  is new to you, take a look at this: https://www.geeksforgeeks.org/numpy-indexing/\n",
        "# tl;dr: we can select a single index by providing a single index n arr[n],\n",
        "# but we can also select multiple elements of an array, by providing multiple indices n, o, p arr[[n, o, p]]\n",
        "\n",
        "# select random indices\n",
        "idx_train = np.random.choice(len(x), size=25) # Docs: https://numpy.org/doc/stable/reference/random/generated/numpy.random.choice.html\n",
        "x_train = x[idx_train]\n",
        "# add noise, as real world data is rarely perfect\n",
        "y_train = y[idx_train] + np.random.normal(loc=0, scale=0.1, size=(len(idx_train), 1)) # Docs: https://numpy.org/doc/stable/reference/random/generated/numpy.random.normal.html"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IP6mA2Sr0GaW",
        "outputId": "0056132e-4ccd-43cd-dbfa-823cecd4f5ac"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "25"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "len(x_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "H3WV2OitzVB5",
        "outputId": "5f74def0-5654-454a-e72b-a4c2af4a4204"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# TODO: plot the training data with the original sine curve\n",
        "plt.plot(x, y)\n",
        "plt.scatter( x_train, y_train, color='orange') # Docs: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.scatter.html\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AUlciFpUxvqk"
      },
      "source": [
        "## Learning a curve"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "R8gqnWLV8oIv"
      },
      "outputs": [],
      "source": [
        "# TODO: what classifiers could we try? Can you find more sklearn regressors that work better?\n",
        "# Can we improve them by changing the hyperparamters? (for instance the hidden_layer_sizes in the MLPRegressor)\n",
        "classifiers = [\n",
        "    RandomForestRegressor(), # Docs: https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html\n",
        "    LinearRegression(), # Docs: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html\n",
        "    MLPRegressor(), # Docs: https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPRegressor\n",
        "]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "sUTlKhfE1HeJ",
        "outputId": "944fdad6-c222-4d16-930e-9b46656bbcf4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RandomForestRegressor()\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-10-5cba44ec1c6b>:6: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  clf.fit( x_train, y_train)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9359354241182574\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LinearRegression()\n",
            "0.5491671233637216\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MLPRegressor()\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:1623: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7294685361550305\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "for clf in classifiers:\n",
        "  # TODO: train the classifier and evaluate it. We want to find the best one.\n",
        "  # How can we see the performance of a classifier?\n",
        "  print(clf)\n",
        "\n",
        "  clf.fit( x_train, y_train)\n",
        "  score = clf.score( x, y)\n",
        "  print(score)\n",
        "  y_pred = clf.predict(x)\n",
        "\n",
        "  # TODO: plot sine, train data, and prediction\n",
        "  plt.plot(x, y)\n",
        "  plt.scatter( x_train, y_train, color='orange')\n",
        "  plt.plot( x, y_pred, color='green')\n",
        "  plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9BGMYrrLRv5O"
      },
      "source": [
        "# Image classification example: Character recognition"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KxPsCNUbtLdL"
      },
      "source": [
        "## Import data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "bDtIqu91tTFK",
        "outputId": "147586a0-c1c7-4055-f9ef-9ac34fc81c22",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
            "Failed to download (trying next):\n",
            "HTTP Error 403: Forbidden\n",
            "\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz to data_mnist/MNIST/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 9912422/9912422 [00:01<00:00, 5423228.45it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data_mnist/MNIST/raw/train-images-idx3-ubyte.gz to data_mnist/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
            "Failed to download (trying next):\n",
            "HTTP Error 403: Forbidden\n",
            "\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz to data_mnist/MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 28881/28881 [00:00<00:00, 156703.26it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data_mnist/MNIST/raw/train-labels-idx1-ubyte.gz to data_mnist/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
            "Failed to download (trying next):\n",
            "HTTP Error 403: Forbidden\n",
            "\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz to data_mnist/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1648877/1648877 [00:01<00:00, 1510034.79it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data_mnist/MNIST/raw/t10k-images-idx3-ubyte.gz to data_mnist/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
            "Failed to download (trying next):\n",
            "HTTP Error 403: Forbidden\n",
            "\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz to data_mnist/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 4542/4542 [00:00<00:00, 749253.86it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data_mnist/MNIST/raw/t10k-labels-idx1-ubyte.gz to data_mnist/MNIST/raw\n",
            "\n"
          ]
        }
      ],
      "source": [
        "mnist_dataset = MNIST(root = 'data_mnist/', train=True, download=True, transform=transforms.ToTensor())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nq2CMfbVwNSn",
        "outputId": "f09fa329-e9a8-4d34-a06c-5c5befb6505c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "60000"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "len(mnist_dataset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "UxuWfRz_xoku"
      },
      "outputs": [],
      "source": [
        "train_data, test_data = random_split(mnist_dataset, [0.8, 0.2])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OB2R9goJxzSs",
        "outputId": "5e27ed40-0300-421d-8015-e02fd8fe9211"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "48000\n",
            "12000\n"
          ]
        }
      ],
      "source": [
        "print(len(train_data))\n",
        "print(len(test_data))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "p9ng-Rj-z7QO"
      },
      "outputs": [],
      "source": [
        "# data loaders\n",
        "train_loader = DataLoader(train_data, batch_size=128, shuffle=True)\n",
        "test_loader = DataLoader(test_data, batch_size=128, shuffle=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9ongdJKEtN6c"
      },
      "source": [
        "## Plotting"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "E40QZbUYtTeL",
        "outputId": "21f2bbac-11cb-4455-e219-7f3207158e4e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Label: 0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "image, label = mnist_dataset[1000]\n",
        "plt.imshow(transforms.ToPILImage()(image), cmap='gray')\n",
        "print('Label:', label)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bT8ql2DftQgU"
      },
      "source": [
        "## Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "iWrAluhPtLBS"
      },
      "outputs": [],
      "source": [
        "class MnistModel(nn.Module):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "    self.flatten = nn.Flatten()\n",
        "    self.linear = nn.Linear(28*28, 10)\n",
        "\n",
        "  def forward(self, x):\n",
        "    x = self.flatten(x)\n",
        "    x = F.softmax(self.linear(x))\n",
        "    return x\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "l6IkXpLt5cBn"
      },
      "outputs": [],
      "source": [
        "def train(model, device, train_loader, optimizer, criterion, epochs, test_loader=None):\n",
        "  for epoch in range(epochs):\n",
        "    start = time()\n",
        "\n",
        "    n_correct = 0\n",
        "    n_total = 0\n",
        "    for data, target in tqdm(train_loader):\n",
        "      data, target = data.to(device), target.to(device)\n",
        "      optimizer.zero_grad()\n",
        "      output = model(data)\n",
        "      loss = criterion(output, target)\n",
        "      loss.backward()\n",
        "      optimizer.step()\n",
        "\n",
        "      n_correct += torch.sum(torch.argmax(output, dim=1) == target).item()\n",
        "      n_total += len(output)\n",
        "    train_acc = n_correct / n_total\n",
        "\n",
        "    print(f'Epoch: {epoch} Loss: {loss.item():.2f} Train acc: {train_acc:.2f}', end='')\n",
        "\n",
        "    n_correct = 0\n",
        "    n_total = 0\n",
        "    if test_loader:\n",
        "      with torch.no_grad():\n",
        "        for data, target in test_loader:\n",
        "          data, target = data.to(device), target.to(device)\n",
        "          output = model(data)\n",
        "          n_correct += torch.sum(torch.argmax(output, dim=1) == target).item()\n",
        "          n_total += len(output)\n",
        "      test_acc = n_correct / n_total\n",
        "      print(f' Test acc: {test_acc:.2f}', end='')\n",
        "\n",
        "    stop = time()\n",
        "\n",
        "    print(f' Time: {timedelta(seconds=stop-start)}')\n",
        "    print()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3V6aGFdJ7ZNN",
        "outputId": "c9f7af26-aff6-4444-c134-e1da9274dc9c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cpu\n"
          ]
        }
      ],
      "source": [
        "device_id = 'cpu'\n",
        "if torch.cuda.is_available(): # check for nvidia gpu\n",
        "    device_id = 'cuda'\n",
        "elif torch.backends.mps.is_available(): # check for apple gpu\n",
        "    device_id = 'mps'\n",
        "device = torch.device(device_id)\n",
        "print(device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 750,
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        },
        "id": "RrPyQbt67UAi",
        "outputId": "b398cf7b-1e02-4436-981d-fffa9b0defb3"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/375 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a1185ed3ee4b40e99ab527ad60676523"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-17-4497efa0384c>:9: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
            "  x = F.softmax(self.linear(x))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 0 Loss: 1.63 Train acc: 0.79 Test acc: 0.88 Time: 0:00:11.507631\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/375 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5e13eade96c74fc09a30ad489eebf0f7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 1 Loss: 1.58 Train acc: 0.89 Test acc: 0.90 Time: 0:00:14.163418\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/375 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c1df5f9df4174a4a874e141e804eb76a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 2 Loss: 1.60 Train acc: 0.90 Test acc: 0.91 Time: 0:00:11.647292\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/375 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "49fb00dda7464740bafda0d6065fae63"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 3 Loss: 1.57 Train acc: 0.91 Test acc: 0.91 Time: 0:00:12.144153\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/375 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "996fd5abede14ae3a7d064aa3137efbe"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 4 Loss: 1.55 Train acc: 0.91 Test acc: 0.91 Time: 0:00:11.061923\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/375 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ccd4a757760941c49f84b94f860c5ace"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 5 Loss: 1.56 Train acc: 0.92 Test acc: 0.92 Time: 0:00:11.247956\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/375 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6abaec640d1142acb133451f7b922046"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 6 Loss: 1.60 Train acc: 0.92 Test acc: 0.92 Time: 0:00:09.911735\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/375 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "09cc1c2bbbd24b67b35f8b876f6f23f4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 7 Loss: 1.57 Train acc: 0.92 Test acc: 0.92 Time: 0:00:11.061853\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/375 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a5f015c3463942659cb0db7aa65ce3c5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 8 Loss: 1.53 Train acc: 0.92 Test acc: 0.92 Time: 0:00:10.155000\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/375 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f8ccca627340462d91eb1c04b81cd86b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 9 Loss: 1.55 Train acc: 0.93 Test acc: 0.92 Time: 0:00:10.028171\n",
            "\n"
          ]
        }
      ],
      "source": [
        "model = MnistModel().to(device)\n",
        "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "train(model, device, train_loader, optimizer, criterion, 10, test_loader=test_loader)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YmZHjpd9R12E"
      },
      "source": [
        "# Finding our lost red dots"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5fBxhs-YpbQf"
      },
      "source": [
        "## Import data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "7QbVp7hQXSDk"
      },
      "outputs": [],
      "source": [
        "# definition of some utility functions to import the data\n",
        "class ImageDataset(Dataset):\n",
        "  def __init__(self, root_dir, transform=None, size=None, random_seed=42):\n",
        "    self.root_dir = root_dir\n",
        "    self.transform = transform\n",
        "    self.random = random.Random(random_seed)\n",
        "    self.files = os.listdir(self.root_dir)\n",
        "    if size:\n",
        "      if len(self.files) < size:\n",
        "        raise Exception(f'Only found {len(self.files)} files in root directory, but the requested dataset size is {size}')\n",
        "      self.files = self.random.sample(self.files, size)\n",
        "\n",
        "  def __getitem__(self, idx):\n",
        "    filename = self.files[idx]\n",
        "    img_path = os.path.join(self.root_dir, filename)\n",
        "    img = Image.open(img_path)\n",
        "    img = self.transform(img) if self.transform is not None else img\n",
        "\n",
        "    dot_count = torch.tensor([int(filename.split('_')[0])])\n",
        "\n",
        "    return img, dot_count\n",
        "\n",
        "  def __len__(self):\n",
        "    return len(self.files)\n",
        "\n",
        "def prepare_data(path, size=1000, transform=None, batch_size=128, shuffle=True):\n",
        "  if transform is None:\n",
        "    transform = transforms.ToTensor()\n",
        "\n",
        "  dataset = ImageDataset(root_dir=path, transform=transform, size=size)\n",
        "  dataloader = DataLoader(dataset=dataset, batch_size=batch_size, shuffle=shuffle)\n",
        "  return dataloader, dataset\n",
        "\n",
        "def download_data_dots(root='data_dots'):\n",
        "  url = 'https://goforeoy-my.sharepoint.com/:u:/g/personal/sebastian_landl_gofore_com/EcD5rowDcglFp6j1lSHRI2MBL2bPNssdXGyNRxEeh8cvRA'\n",
        "  filename = 'data_dots.zip'\n",
        "\n",
        "  if os.path.exists(root):\n",
        "    print(f'Folder {root} already exists. Not downloading.')\n",
        "    return\n",
        "  download(url, filename=filename, unzip=True, unzip_path=root, clean=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "Ox9ubk_CXSDl",
        "outputId": "8f652939-4812-413a-df8e-6a7f92438c8d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Folder data_dots already exists. Not downloading.\n"
          ]
        }
      ],
      "source": [
        "# This cell downloads the data and unpacks it into the folder structure defined above\n",
        "download_data_dots()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "wyWEiV2vxuGI"
      },
      "outputs": [],
      "source": [
        "# data parameters; here we specify the amount of data to load. More data may improve our model in exchange for longer training times.\n",
        "train_data_size = 50_000\n",
        "test_data_size = 5_000\n",
        "train_data_path = 'data_dots/train'\n",
        "test_data_path = 'data_dots/test'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "iXoFikfTXSDx"
      },
      "outputs": [],
      "source": [
        "train_loader, train_dataset = prepare_data(train_data_path, size=train_data_size, batch_size=128)\n",
        "test_loader, test_dataset = prepare_data(test_data_path, size=test_data_size, batch_size=128)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_QZ-4Rb9XzkU"
      },
      "source": [
        "## Plotting data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 484
        },
        "id": "OKIAqbzHX1oR",
        "outputId": "94e804d8-a1cd-4cdd-b1cc-2fcdd381d404"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([3, 32, 32])\n",
            "tensor([9])\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x784b4da0a710>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Let's look at one of the images\n",
        "x, y = train_dataset[42]\n",
        "print(x.shape)\n",
        "print(y)\n",
        "plt.imshow(transforms.ToPILImage()(x))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WrK-lhQMX2ty"
      },
      "source": [
        "## Learning data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "NB-0JifwX34Y"
      },
      "outputs": [],
      "source": [
        "# How many of our predictions are correct?\n",
        "def accuracy(model, device, data_loader):\n",
        "  n_correct = 0\n",
        "  n_total = 0\n",
        "  with torch.no_grad():\n",
        "    for data, target in data_loader:\n",
        "      data, target = data.to(device), target.to(device)\n",
        "      output = model(data)\n",
        "      n_correct += torch.sum(torch.round(output) == target).item()\n",
        "      n_total += len(output)\n",
        "\n",
        "  return n_correct / n_total\n",
        "\n",
        "def train(model, device, train_loader, optimizer, criterion, epochs, test_loader=None):\n",
        "  for epoch in range(epochs):\n",
        "    start = time()\n",
        "\n",
        "    n_correct = 0\n",
        "    n_total = 0\n",
        "    for data, target in tqdm(train_loader):\n",
        "      data, target = data.to(device), target.to(device)\n",
        "      optimizer.zero_grad()\n",
        "      output = model(data)\n",
        "      loss = criterion(output, target)\n",
        "      loss.backward()\n",
        "      optimizer.step()\n",
        "\n",
        "      n_correct += torch.sum(torch.round(output) == target).item()\n",
        "      n_total += len(output)\n",
        "    train_acc = n_correct / n_total\n",
        "\n",
        "    print(f'Epoch: {epoch} Loss: {loss.item():.2f} Train acc: {train_acc:.2f}', end='')\n",
        "\n",
        "    if test_loader:\n",
        "      test_acc = accuracy(model, device, test_loader)\n",
        "      print(f' Test acc: {test_acc:.2f}', end='')\n",
        "\n",
        "    stop = time()\n",
        "\n",
        "    print(f' Time: {timedelta(seconds=stop-start)}')\n",
        "    print()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_CQkg701PEok",
        "outputId": "c6333bc8-5415-484a-d4f7-f6bac5d06696"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cpu\n"
          ]
        }
      ],
      "source": [
        "# The training can run on differnt devices\n",
        "# cpu is the default, as it is always available, but on\n",
        "# some machines a more specialized device is available\n",
        "device_id = 'cpu'\n",
        "if torch.cuda.is_available(): # check for nvidia gpu\n",
        "    device_id = 'cuda'\n",
        "elif torch.backends.mps.is_available(): # check for apple gpu\n",
        "    device_id = 'mps'\n",
        "device = torch.device(device_id)\n",
        "print(device)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cqMzQ_6DXSD4"
      },
      "source": [
        "Now let's get to training and improving out model.  \n",
        "You can look into:\n",
        "- Model architecture\n",
        "  - [nn.Linear](https://pytorch.org/docs/stable/generated/torch.nn.Linear.html)  \n",
        "    The Linear Layer is essentially a matrix that transforms a vector of a given size to another vector of the specified output size.\n",
        "  - [nn.Conv2d](https://pytorch.org/docs/stable/generated/torch.nn.Conv2d.html)  \n",
        "    2D convolution is very good for image tasks. It takes one (or more) 2D matrix as an input and creates one (or more) 2D matrix as an output. Our images are basically 3 layers (Red, Green and Blue) of 32 by 32 matrices. [Here](https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md) are some illustrations of how the convolution operator works on a single layer. Keep in mind that passing data from a 2D convolution layer to a liner layer requires you to flatten the data to a 1D array. You can check out [this website](https://www.baeldung.com/cs/convolutional-layer-size) for an explanation of how to calculate the output size. Depending on what hyperparameters you want to use in the convolution layer and we assume a square image you can start with this simple formula:  \n",
        "\t\t`conv_out_size = (image_size - conv_kernel_size + 2 * padding) + 1`  \n",
        "    `linear_layer_in_size = conv_out_channels * conv_out_size * conv_out_size`\n",
        "- Amount of data\n",
        "- Number of training iterations\n",
        "- Any other parameter you can see really"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "vC-t7VBfXSD5"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "class Model(nn.Module):\n",
        "    def __init__(self, image_size=32, num_classes=10):\n",
        "        super(Model, self).__init__()\n",
        "\n",
        "        self.conv1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, padding=1)\n",
        "        self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, padding=1)\n",
        "        self.conv3 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, padding=1)\n",
        "\n",
        "        self.pool = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)\n",
        "\n",
        "        self.flatten = nn.Flatten()\n",
        "\n",
        "        self.fc1 = nn.Linear(128 * (image_size // 8) * (image_size // 8), 512)\n",
        "        self.fc2 = nn.Linear(512, num_classes)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.pool(F.relu(self.conv1(x)))\n",
        "        x = self.pool(F.relu(self.conv2(x)))\n",
        "        x = self.pool(F.relu(self.conv3(x)))\n",
        "\n",
        "        x = self.flatten(x)\n",
        "        x = F.relu(self.fc1(x))\n",
        "        x = self.fc2(x)\n",
        "\n",
        "        return x\n"
      ]
    },
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        },
        "id": "aLlFS9U_igam",
        "outputId": "1f4fe4eb-a3a8-44ef-fd6b-ae8f0eee6b49"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/79 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "75f6f746078a4fedb37244607de46744"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/loss.py:101: UserWarning: Using a target size (torch.Size([128, 1])) that is different to the input size (torch.Size([128, 10])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
            "  return F.l1_loss(input, target, reduction=self.reduction)\n",
            "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/loss.py:101: UserWarning: Using a target size (torch.Size([16, 1])) that is different to the input size (torch.Size([16, 10])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
            "  return F.l1_loss(input, target, reduction=self.reduction)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 0 Loss: 0.91 Train acc: 2.32 Test acc: 3.25 Time: 0:00:29.838034\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ab6c82cb3a26428e966b87622002fa6b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 1 Loss: 0.40 Train acc: 5.82 Test acc: 5.67 Time: 0:00:29.671845\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "06e515efccb6443483f9d5c71e06d562"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 2 Loss: 0.19 Train acc: 7.52 Test acc: 9.17 Time: 0:00:31.566740\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/79 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1f8d65452f09415eb125bed55dd814e5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 3 Loss: 0.37 Train acc: 8.89 Test acc: 9.50 Time: 0:00:31.560501\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "327819bc7b8943afab49dd41241004b2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 4 Loss: 0.12 Train acc: 8.85 Test acc: 9.07 Time: 0:00:28.435616\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b72ee5ae6d584d6d88b0872de741a2ec"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 5 Loss: 0.13 Train acc: 9.78 Test acc: 9.79 Time: 0:00:28.675834\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/79 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6fc39aaf25a7419399450f5b55ef8fa8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 6 Loss: 0.66 Train acc: 8.63 Test acc: 0.43 Time: 0:00:29.523957\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "  0%|          | 0/79 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f6af278a552c4cac8a3fdbb806589edd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 7 Loss: 0.11 Train acc: 8.75 Test acc: 9.91 Time: 0:00:29.506071\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/79 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "569988be52f8404a9f74b090db239c6a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 8 Loss: 0.33 Train acc: 9.79 Test acc: 9.80 Time: 0:00:29.975923\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/79 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d1bef0aeaba84b15bd012abec5fc4072"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 9 Loss: 0.06 Train acc: 9.90 Test acc: 9.95 Time: 0:00:34.351931\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# TODO: run the training, see how your model performs, improve the model\n",
        "model = Model().to(device)\n",
        "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
        "criterion = nn.L1Loss() # List of pytorch loss functions: https://neptune.ai/blog/pytorch-loss-functions\n",
        "train(model, device, train_loader, optimizer, criterion, 10, test_loader=test_loader)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "-Weji0fHXSD9",
        "outputId": "919e7ab9-8cde-4aa7-a9ce-d698bc607ca9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 484
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Actual number of dots: 7\n",
            "Prediction           : 1\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x784b4b9b0c40>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Let's check one of our images\n",
        "x, y = test_dataset[42]\n",
        "with torch.no_grad():\n",
        "    pred = model(x.unsqueeze(dim=0).to(device))\n",
        "print('Actual number of dots:', y.item())\n",
        "predicted_class = pred.argmax(dim=1).item()\n",
        "print('Prediction           :', predicted_class)\n",
        "plt.imshow(transforms.ToPILImage()(x))"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
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