01_mnist_data.ipynb

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      "cell_type": "markdown",
      "source": [
        "# PyTorch - MNIST Data\n",
        "\n",
        "Make Your Own Neural Network 2nd Edition OReilly"
      ],
      "metadata": {
        "id": "bNgBu-mEsOzz"
      }
    },
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at ./mount\n"
          ]
        }
      ],
      "source": [
        "# mount Drive to access data files\n",
        "from google.colab import drive\n",
        "drive.mount('./mount')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import pandas to read csv files\n",
        "import pandas\n",
        "\n",
        "# import matplotlib to show images\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "yw3SXVfWnxmn"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pandas.read_csv('mount/My Drive/Colab Notebooks/myo_gan/mnist_data/mnist_train.csv', header=None)"
      ],
      "metadata": {
        "id": "IDdGrKE3mdcW"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
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      "source": [
        "df.head()"
      ],
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          "height": 233
        },
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        "outputId": "bc1831fb-427d-4c50-ecf9-6751ca36600a"
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        "# get data from dataframe\n",
        "row = 0\n",
        "data = df.iloc[row]\n",
        "\n",
        "# label is the first value\n",
        "label = data[0]\n",
        "\n",
        "# image data is the remaining 784 values\n",
        "img = data[1:].values.reshape(28,28)\n",
        "plt.title(\"label = \" + str(label))\n",
        "plt.imshow(img, interpolation='none', cmap='Blues')\n",
        "plt.show()"
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              "<Figure size 640x480 with 1 Axes>"
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/nvyd55rgqFzPNX8rbPi7dKcTF+fzXANYCIVCSusSUHX16d/Hze+CAwC0TgQQAMAEAQQAMEEAAQBMEEAAABMEEADABAEEADBBAAEATBBAAAATBBAAwAQBBAAwQQABAEwQQAAAE55/jgFoDp4e3z+quj9/4H1m673Ff/Rcs/HTbM81w3t38VwDNGWcAQEATBBAAAATBBAAwAQBBAAwQQABAEwQQAAAEwQQAMAEAQQAMEEAAQBMEEAAABMEEADABAEEADDBZKRokRLi20RV98f7r/BcM2DTXz3XXPWr33uuGX6l9wlW8wamea6RpJkjenqu8fl8UW0LrRdnQAAAEwQQAMAEAQQAMEEAAQBMEEAAABMEEADABAEEADBBAAEATBBAAAATBBAAwAQBBAAwQQABAEwwGSnwLd2SEzzXLH1mmueaG3++0HPN+pf+7r3Gc8UJ/3zkZ55r8oef67kmJdHvuQYtB2dAAAATBBAAwAQBBAAwQQABAEwQQAAAEwQQAMAEAQQAMEEAAQBMEEAAABMEEADABAEEADBBAAEATPicc866iW8LhUIKBAKqrKpWUlKSdTtAo9hZedBzzfin3/Ncs/udP3iuidaY6bd4rnlmwkDPNalJTGDa1IVCIaV1Cai6+vTv45wBAQBMEEAAABOeA2jdunUaN26cgsGgfD6fVqxYEfH85MmT5fP5IpaxY8fGql8AQAvhOYBqa2s1aNAgzZ8//5Rjxo4dq3379oWX11577Qc1CQBoeTz/ImpeXp7y8vJOO8bv9ys9PT3qpgAALV+jXAMqLi5Wamqqzj//fM2YMUNVVVWnHFtXV6dQKBSxAABavpgH0NixY/Xyyy9rzZo1euyxx1RSUqK8vDwdP368wfGFhYUKBALhJTMzM9YtAQCaIM8fwZ3JjTfeGP57wIABGjhwoHr16qXi4mKNHj36pPGzZs1SQUFB+HEoFCKEAKAVaPTbsHv27KmUlBTt2LGjwef9fr+SkpIiFgBAy9foAfT555+rqqpKGRkZjb0pAEAz4vkjuIMHD0aczZSXl2vLli1KTk5WcnKyHnroIU2YMEHp6enauXOn7rvvPvXu3Vu5ubkxbRwA0Lx5DqBNmzbpiiuuCD/+5vrNpEmT9Pzzz2vr1q166aWXdODAAQWDQY0ZM0aPPPKI/H7mbwIA/AuTkQLNRM3hY55r/lBWEdW2pk3/jfeiKN5KMkef/juFDdn6X8ys0tQxGSkAoEkjgAAAJgggAIAJAggAYIIAAgCYIIAAACYIIACACQIIAGCCAAIAmCCAAAAmCCAAgAkCCABgggACAJiI+U9yA2gciQltPddcf2F0P28/rY33benro55L9pSs8VyzuTzbc81FWZ0916DxcQYEADBBAAEATBBAAAATBBAAwAQBBAAwQQABAEwQQAAAEwQQAMAEAQQAMEEAAQBMEEAAABMEEADABJORAgZ2VBz0XPPshs8816zb/P8810iKamLRaHS84GLPNRf26BT7RmCCMyAAgAkCCABgggACAJgggAAAJgggAIAJAggAYIIAAgCYIIAAACYIIACACQIIAGCCAAIAmCCAAAAmmIwU+JbPvjzkueZXq/7huWZ10XrPNarY4b3mbGrj/e0kNaOz55q4OJ/nGjRNnAEBAEwQQAAAEwQQAMAEAQQAMEEAAQBMEEAAABMEEADABAEEADBBAAEATBBAAAATBBAAwAQBBAAwwWSkaPKqauo81/z3X3dHta3HfvsnzzWu/MOottWUdR5yueeahTNHeK4Z3TfNcw1aDs6AAAAmCCAAgAlPAVRYWKghQ4YoMTFRqampuvbaa1VWVhYx5siRI8rPz1eXLl3UsWNHTZgwQZWVlTFtGgDQ/HkKoJKSEuXn52vDhg16++23dezYMY0ZM0a1tbXhMffcc4/efPNNLV++XCUlJdq7d6/Gjx8f88YBAM2bp5sQVq9eHfF48eLFSk1NVWlpqUaOHKnq6mq9+OKLWrJkia688kpJ0qJFi9SvXz9t2LBBw4YNi13nAIBm7QddA6qurpYkJScnS5JKS0t17Ngx5eTkhMf07dtX3bt31/r1Df8EcV1dnUKhUMQCAGj5og6g+vp63X333RoxYoT69+8vSaqoqFB8fLw6deoUMTYtLU0VFRUNvk5hYaECgUB4yczMjLYlAEAzEnUA5efna9u2bVq6dOkPamDWrFmqrq4OL3v27PlBrwcAaB6i+iLqzJkztWrVKq1bt07dunULr09PT9fRo0d14MCBiLOgyspKpaenN/hafr9ffr8/mjYAAM2YpzMg55xmzpypoqIirV27VllZWRHPDx48WG3bttWaNWvC68rKyrR7924NHz48Nh0DAFoET2dA+fn5WrJkiVauXKnExMTwdZ1AIKCEhAQFAgHdcccdKigoUHJyspKSknTnnXdq+PDh3AEHAIjgKYCef/55SdKoUaMi1i9atEiTJ0+WJD355JOKi4vThAkTVFdXp9zcXD333HMxaRYA0HL4nHPOuolvC4VCCgQCqqyqVlJSknU7OI2qg0c915Tvrz3zoO/494f/r+eauo/+6rmmqesy7ArPNfOnRffJw79FMUloXJwvqm2h5QmFQkrrElB19enfx5kLDgBgggACAJgggAAAJgggAIAJAggAYIIAAgCYIIAAACYIIACACQIIAGCCAAIAmCCAAAAmCCAAgAkCCABgIqpfREXTVX3omOeacc++H9W2/rFll+ea4zs/iGpbTVnqiBzPNc9MGeK5ZmTvrp5r2rVt47kGOFs4AwIAmCCAAAAmCCAAgAkCCABgggACAJgggAAAJgggAIAJAggAYIIAAgCYIIAAACYIIACACQIIAGCCyUjPkr9/HvJcM/3VzZ5rtv3lY881+vwj7zVNXUJiVGU3//xGzzWP/3s/zzUJ8UwSCnAGBAAwQQABAEwQQAAAEwQQAMAEAQQAMEEAAQBMEEAAABMEEADABAEEADBBAAEATBBAAAATBBAAwASTkZ4lz67f5blm2xtFsW8khtr9eKjnmut+MtBzzTltfJ5r/nPs+Z5rJCkxoW1UdQC84wwIAGCCAAIAmCCAAAAmCCAAgAkCCABgggACAJgggAAAJgggAIAJAggAYIIAAgCYIIAAACYIIACACZ9zzlk38W2hUEiBQECVVdVKSkqybgcA4FEoFFJal4Cqq0//Ps4ZEADABAEEADDhKYAKCws1ZMgQJSYmKjU1Vddee63KysoixowaNUo+ny9imT59ekybBgA0f54CqKSkRPn5+dqwYYPefvttHTt2TGPGjFFtbW3EuClTpmjfvn3hZd68eTFtGgDQ/Hn6RdTVq1dHPF68eLFSU1NVWlqqkSNHhte3b99e6enpsekQANAi/aBrQNXV1ZKk5OTkiPWvvvqqUlJS1L9/f82aNUuHDh065WvU1dUpFApFLACAls/TGdC31dfX6+6779aIESPUv3//8PqbbrpJPXr0UDAY1NatW3X//ferrKxMb7zxRoOvU1hYqIceeijaNgAAzVTU3wOaMWOG/vCHP+i9995Tt27dTjlu7dq1Gj16tHbs2KFevXqd9HxdXZ3q6urCj0OhkDIzM/keEAA0U9/3e0BRnQHNnDlTq1at0rp1604bPpKUnZ0tSacMIL/fL7/fH00bAIBmzFMAOed05513qqioSMXFxcrKyjpjzZYtWyRJGRkZUTUIAGiZPAVQfn6+lixZopUrVyoxMVEVFRWSpEAgoISEBO3cuVNLlizRVVddpS5dumjr1q265557NHLkSA0cOLBR/gEAgObJ0zUgn8/X4PpFixZp8uTJ2rNnj2655RZt27ZNtbW1yszM1HXXXafZs2d/7+s5zAUHAM1bo1wDOlNWZWZmqqSkxMtLAgBaKeaCAwCYIIAAACYIIACACQIIAGCCAAIAmCCAAAAmCCAAgAkCCABgggACAJgggAAAJgggAIAJAggAYIIAAgCYIIAAACYIIACACQIIAGCCAAIAmCCAAAAmCCAAgAkCCABgggACAJgggAAAJgggAIAJAggAYIIAAgCYOMe6ge9yzkmSakIh404AANH45v37m/fzU2lyAVRTUyNJ6p2VadwJAOCHqKmpUSAQOOXzPnemiDrL6uvrtXfvXiUmJsrn80U8FwqFlJmZqT179igpKcmoQ3vshxPYDyewH05gP5zQFPaDc041NTUKBoOKizv1lZ4mdwYUFxenbt26nXZMUlJSqz7AvsF+OIH9cAL74QT2wwnW++F0Zz7f4CYEAIAJAggAYKJZBZDf79fcuXPl9/utWzHFfjiB/XAC++EE9sMJzWk/NLmbEAAArUOzOgMCALQcBBAAwAQBBAAwQQABAEwQQAAAE80mgObPn69zzz1X7dq1U3Z2tv7yl79Yt3TWPfjgg/L5fBFL3759rdtqdOvWrdO4ceMUDAbl8/m0YsWKiOedc5ozZ44yMjKUkJCgnJwcbd++3abZRnSm/TB58uSTjo+xY8faNNtICgsLNWTIECUmJio1NVXXXnutysrKIsYcOXJE+fn56tKlizp27KgJEyaosrLSqOPG8X32w6hRo046HqZPn27UccOaRQC9/vrrKigo0Ny5c7V582YNGjRIubm52r9/v3VrZ90FF1ygffv2hZf33nvPuqVGV1tbq0GDBmn+/PkNPj9v3jw9/fTTWrBggTZu3KgOHTooNzdXR44cOcudNq4z7QdJGjt2bMTx8dprr53FDhtfSUmJ8vPztWHDBr399ts6duyYxowZo9ra2vCYe+65R2+++aaWL1+ukpIS7d27V+PHjzfsOva+z36QpClTpkQcD/PmzTPq+BRcMzB06FCXn58ffnz8+HEXDAZdYWGhYVdn39y5c92gQYOs2zAlyRUVFYUf19fXu/T0dPf444+H1x04cMD5/X732muvGXR4dnx3Pzjn3KRJk9w111xj0o+V/fv3O0mupKTEOXfiv33btm3d8uXLw2M++ugjJ8mtX7/eqs1G99394Jxzl19+ubvrrrvsmvoemvwZ0NGjR1VaWqqcnJzwuri4OOXk5Gj9+vWGndnYvn27gsGgevbsqZtvvlm7d++2bslUeXm5KioqIo6PQCCg7OzsVnl8FBcXKzU1Veeff75mzJihqqoq65YaVXV1tSQpOTlZklRaWqpjx45FHA99+/ZV9+7dW/Tx8N398I1XX31VKSkp6t+/v2bNmqVDhw5ZtHdKTW427O/68ssvdfz4caWlpUWsT0tL08cff2zUlY3s7GwtXrxY559/vvbt26eHHnpIl112mbZt26bExETr9kxUVFRIUoPHxzfPtRZjx47V+PHjlZWVpZ07d+o//uM/lJeXp/Xr16tNmzbW7cVcfX297r77bo0YMUL9+/eXdOJ4iI+PV6dOnSLGtuTjoaH9IEk33XSTevTooWAwqK1bt+r+++9XWVmZ3njjDcNuIzX5AMK/5OXlhf8eOHCgsrOz1aNHDy1btkx33HGHYWdoCm688cbw3wMGDNDAgQPVq1cvFRcXa/To0YadNY78/Hxt27atVVwHPZ1T7YepU6eG/x4wYIAyMjI0evRo7dy5U7169TrbbTaoyX8El5KSojZt2px0F0tlZaXS09ONumoaOnXqpD59+mjHjh3WrZj55hjg+DhZz549lZKS0iKPj5kzZ2rVqlV69913I34/LD09XUePHtWBAwcixrfU4+FU+6Eh2dnZktSkjocmH0Dx8fEaPHiw1qxZE15XX1+vNWvWaPjw4Yad2Tt48KB27typjIwM61bMZGVlKT09PeL4CIVC2rhxY6s/Pj7//HNVVVW1qOPDOaeZM2eqqKhIa9euVVZWVsTzgwcPVtu2bSOOh7KyMu3evbtFHQ9n2g8N2bJliyQ1rePB+i6I72Pp0qXO7/e7xYsXu3/84x9u6tSprlOnTq6iosK6tbPq3nvvdcXFxa68vNy9//77Licnx6WkpLj9+/dbt9aoampq3AcffOA++OADJ8k98cQT7oMPPnCfffaZc865X//6165Tp05u5cqVbuvWre6aa65xWVlZ7vDhw8adx9bp9kNNTY37xS9+4davX+/Ky8vdO++84y666CJ33nnnuSNHjli3HjMzZsxwgUDAFRcXu3379oWXQ4cOhcdMnz7dde/e3a1du9Zt2rTJDR8+3A0fPtyw69g7037YsWOHe/jhh92mTZtceXm5W7lypevZs6cbOXKkceeRmkUAOefcM88847p37+7i4+Pd0KFD3YYNG6xbOutuuOEGl5GR4eLj492PfvQjd8MNN7gdO3ZYt9Xo3n33XSfppGXSpEnOuRO3Yj/wwAMuLS3N+f1+N3r0aFdWVmbbdCM43X44dOiQGzNmjOvatatr27at69Gjh5syZUqL+5+0hv79ktyiRYvCYw4fPux+9rOfuc6dO7v27du76667zu3bt8+u6UZwpv2we/duN3LkSJecnOz8fr/r3bu3++Uvf+mqq6ttG/8Ofg8IAGCiyV8DAgC0TAQQAMAEAQQAMEEAAQBMEEAAABMEEADABAEEADBBAAEATBBAAAATBBAAwAQBBAAw8f8Bs2N1j+1FRfsAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OzWwGG5brKW_"
      },
      "execution_count": 5,
      "outputs": []
    }
  ]
}