module04_mnist_fc.ipynb

{
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   "cell_type": "code",
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    "colab": {},
    "colab_type": "code",
    "id": "DWr6cvb9pS3J"
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   "source": [
    "import torch\n",
    "import random\n",
    "import numpy as np\n",
    "\n",
    "random.seed(0)\n",
    "np.random.seed(0)\n",
    "torch.manual_seed(0)\n",
    "torch.cuda.manual_seed(0)\n",
    "torch.backends.cudnn.deterministic = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MqGQWTDIpS3R"
   },
   "outputs": [],
   "source": [
    "import torchvision.datasets\n",
    "MNIST_train = torchvision.datasets.MNIST('./', download=True, train=True)\n",
    "MNIST_test = torchvision.datasets.MNIST('./', download=True, train=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vv_Lz7PYpS3U"
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   "outputs": [],
   "source": [
    "X_train = MNIST_train.train_data\n",
    "y_train = MNIST_train.train_labels\n",
    "X_test = MNIST_test.test_data\n",
    "y_test = MNIST_test.test_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "hMhsAedlrQF5",
    "outputId": "ae08bd21-79ff-48da-9886-48996a178110"
   },
   "outputs": [],
   "source": [
    "X_train.dtype, y_train.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_yTaVOrPvap6"
   },
   "outputs": [],
   "source": [
    "X_train = X_train.float()\n",
    "X_test = X_test.float()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    },
    "colab_type": "code",
    "id": "_gfupg4kpS3X",
    "outputId": "e72c3ff4-50f7-4695-d5d5-f838e7ac2667"
   },
   "outputs": [],
   "source": [
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "zEC1RXBEz_SW",
    "outputId": "48b9a605-2622-4188-b326-116b675bd499"
   },
   "outputs": [],
   "source": [
    "y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 365
    },
    "colab_type": "code",
    "id": "Z1tFXMwJpS3e",
    "outputId": "e7c2778b-d6f5-4718-ea28-fc8544f0416c"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.imshow(X_train[0, :, :])\n",
    "plt.show()\n",
    "print(y_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "IUZgNg7zpS3j"
   },
   "outputs": [],
   "source": [
    "X_train = X_train.reshape([-1, 28 * 28])\n",
    "X_test = X_test.reshape([-1, 28 * 28])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "k7l65slppS3l"
   },
   "outputs": [],
   "source": [
    "class MNISTNet(torch.nn.Module):\n",
    "    def __init__(self, n_hidden_neurons):\n",
    "        super(MNISTNet, self).__init__()\n",
    "        self.fc1 = torch.nn.Linear(28 * 28, n_hidden_neurons)\n",
    "        self.ac1 = torch.nn.Sigmoid()\n",
    "        self.fc2 = torch.nn.Linear(n_hidden_neurons, 10) \n",
    "    \n",
    "    def forward(self, x):\n",
    "        x = self.fc1(x)\n",
    "        x = self.ac1(x)\n",
    "        x = self.fc2(x)\n",
    "        return x\n",
    "    \n",
    "mnist_net = MNISTNet(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "FJp7t3dR0pC5"
   },
   "outputs": [],
   "source": [
    "# torch.cuda.is_available()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ShQDv_w-0rz8"
   },
   "outputs": [],
   "source": [
    "# !nvidia-smi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iUHy-780po9d"
   },
   "outputs": [],
   "source": [
    "# device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
    "# mnist_net = mnist_net.to(device)\n",
    "# list(mnist_net.parameters())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "00_2j2igpS3o"
   },
   "outputs": [],
   "source": [
    "loss = torch.nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.Adam(mnist_net.parameters(), lr=1.0e-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wZtqiGvfpS3r"
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   "outputs": [],
   "source": [
    "batch_size = 100\n",
    "\n",
    "# test_accuracy_history = []\n",
    "# test_loss_history = []\n",
    "\n",
    "# X_test = X_test.to(device)\n",
    "# y_test = y_test.to(device)\n",
    "\n",
    "for epoch in range(10000):\n",
    "    order = np.random.permutation(len(X_train))\n",
    "    \n",
    "    for start_index in range(0, len(X_train), batch_size):\n",
    "        optimizer.zero_grad()\n",
    "        \n",
    "        batch_indexes = order[start_index:start_index+batch_size]\n",
    "        \n",
    "        X_batch = X_train[batch_indexes] #.to(device)\n",
    "        y_batch = y_train[batch_indexes] #.to(device)\n",
    "        \n",
    "        preds = mnist_net.forward(X_batch) \n",
    "        \n",
    "        loss_value = loss(preds, y_batch)\n",
    "        loss_value.backward()\n",
    "        \n",
    "        optimizer.step()\n",
    "\n",
    "    test_preds = mnist_net.forward(X_test)\n",
    "#     test_loss_history.append(loss(test_preds, y_test))\n",
    "    \n",
    "    accuracy = (test_preds.argmax(dim=1) == y_test).float().mean()\n",
    "#     test_accuracy_history.append(accuracy)\n",
    "    print(accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kLnumX3SpS3u"
   },
   "outputs": [],
   "source": [
    "plt.plot(test_accuracy_history)\n",
    "plt.plot(test_loss_history);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-nu1318JpS3y"
   },
   "outputs": [],
   "source": []
  }
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