notebooks/6.multiclass-classifier-mnist.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multi-class classifier: MNIST images\n",
    "\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/parrt/fundamentals-of-deep-learning/blob/main/notebooks/6.multiclass-classifier-mnist.ipynb)\n",
    "\n",
    "By [Terence Parr](https://explained.ai).\n",
    "\n",
    "To demonstrate $k$ class classification instead of binary ($k=2$) classification, let's use the traditional MNIST digital image recognition problem. We'll again use a random forest model as a baseline classifier. Instead of a sigmoid on a single output neuron, $k$ class classifiers use $k$ neurons in the final layer and then a softmax computation instead of a single-output sigmoid. We see fairly decent recognition results with just 50 neurons. By using 500 neurons, we get slightly better results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Support code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import colors \n",
    "\n",
    "%config InlineBackend.figure_format = 'retina'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_loss(history, ax=None, maxy=None, file=None):\n",
    "    if ax is None:\n",
    "        fig, ax = plt.subplots(1,1, figsize=(3.5,3))\n",
    "    ax.set_ylabel(\"Loss\")\n",
    "    ax.set_xlabel(\"Epochs\")\n",
    "    loss = history[:,0]\n",
    "    val_loss = history[:,1]\n",
    "    if maxy:\n",
    "        ax.set_ylim(0,maxy)\n",
    "    else:\n",
    "        ax.set_ylim(0,torch.max(val_loss))\n",
    "    ax.spines['top'].set_visible(False)    # turns off the top \"spine\" completely\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['left'].set_linewidth(.5)\n",
    "    ax.spines['bottom'].set_linewidth(.5)\n",
    "    ax.plot(loss, label='train_loss')\n",
    "    ax.plot(val_loss, label='val_loss')\n",
    "    ax.legend(loc='upper right')\n",
    "    plt.tight_layout()\n",
    "    if file:\n",
    "        plt.savefig(f\"/Users/{os.environ['USER']}/Desktop/{file}.pdf\")\n",
    "        \n",
    "def plot_accur(history, ax=None, file=None):\n",
    "    if ax is None:\n",
    "        fig, ax = plt.subplots(1,1, figsize=(3.5,3))\n",
    "    ax.set_ylabel(\"Accuracy\")\n",
    "    ax.set_xlabel(\"Epochs\")\n",
    "    accur = history[:,2]\n",
    "    val_accur = history[:,3]\n",
    "    ax.set_ylim(0,1.05)\n",
    "    ax.plot(accur, label='accur')\n",
    "    ax.plot(val_accur, label='val_accur')\n",
    "    ax.spines['top'].set_visible(False)    # turns off the top \"spine\" completely\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['left'].set_linewidth(.5)\n",
    "    ax.spines['bottom'].set_linewidth(.5)\n",
    "    ax.legend(loc='lower right')\n",
    "    plt.tight_layout()\n",
    "    if file:\n",
    "        plt.savefig(f\"/Users/{os.environ['USER']}/Desktop/{file}.pdf\")\n",
    "        \n",
    "def imgrid(images, digits, dim=(5,5), figsize=(4, 4)):\n",
    "    fig, axes = plt.subplots(*dim, figsize=figsize)\n",
    "    for i, ax in enumerate(axes.flat):\n",
    "        img_as_row = images[i]\n",
    "        img28x28 = img_as_row.reshape(28,28)\n",
    "        ax.axis('off') # don't show x, y axes\n",
    "        ax.imshow(img28x28, cmap='Greys')\n",
    "        ax.text(0, 8, digits[i], color='#313695', fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data\n",
    "\n",
    "Let's use a subset of the well-known MNIST handwritten digits data set. This problem is the \"hello world\" of image recognition for deep learning.  The images have 28x28 pixels for a total of 784 features if we flatten them into feature vectors. Each row in the data set will represent an image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mnist = pd.read_csv(\"https://mlbook.explained.ai/data/mnist-10k-sample.csv.zip\")\n",
    "X = df_mnist.drop('digit', axis=1).values # get just pixels\n",
    "y = df_mnist['digit'].values              # get just digit value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 784)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape # There are 784 pixels per 28x28 image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# shuffle so that images for the digits are not grouped together\n",
    "n = len(X)\n",
    "idx = np.random.randint(0,n,n)\n",
    "X = X[idx,:]\n",
    "y = y[idx]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the handy function from above, let's display a sample of the images and their true known values. As you can see, there's a great deal of variety in how different people draw Hindu-Arabic numerals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 64 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 288,
       "width": 292
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "imgrid(X,digits=y,dim=(8,8),figsize=(5,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to measure the generality of our model, which is what we really care about, let's split the images 80/20 into training/validation sets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = torch.tensor(X).float()\n",
    "y = torch.tensor(y)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random forest baseline\n",
    "\n",
    "It's also a good idea to get a baseline performance so that we know whether all of this neural network stuff is worth the hassle. In other words, if a random forest, which is easy to use and train, is good enough then we don't need the complexity of a neural network. Let's train a random forest classifier with a lot of trees. (`n_jobs=-1` just tells the scikit-learn library to compute the trees in parallel across multiple core.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_estimators=900, n_jobs=-1)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestClassifier(n_estimators=900, n_jobs=-1)\n",
    "rf.fit(X_train, y_train) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's compute the accuracy of the model on both the training and validation sets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.0, 0.9725)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_accuracy = rf.score(X_train, y_train)\n",
    "y_accuracy = rf.score(X_test, y_test)\n",
    "y_train_accuracy, y_accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The classifier gets 100% accuracy on the 8000 images of the training set and 97.5% accuracy on the validation set. That's pretty good considering we didn't have to think much to get the classifier to work.\n",
    "\n",
    "To get more detail about how well the model predicts each digit, let's look at the *confusion matrix* whose $(i,j)$ elements represent the count of records (images) known to be class $i$ but predicted to be in class $j$.  In this case, the column and row headers are the digits 0..9. The sklearn function `confusion_matrix` takes the true $y$ and compares it to the predicted $\\hat{y}$ to yield a matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[197   0   0   0   0   0   0   1   0   0]\n",
      " [  0 205   0   0   0   0   0   0   0   0]\n",
      " [  2   0 222   0   1   0   2   1   2   0]\n",
      " [  1   0   4 196   0   3   1   0   4   1]\n",
      " [  0   0   0   0 207   0   0   0   0   6]\n",
      " [  4   0   0   0   0 171   1   0   2   0]\n",
      " [  1   0   0   0   0   0 191   0   0   0]\n",
      " [  0   0   2   0   0   0   0 209   0   0]\n",
      " [  1   2   2   1   0   1   1   0 180   4]\n",
      " [  0   0   0   2   2   0   0   0   0 167]]\n"
     ]
    }
   ],
   "source": [
    "y_pred = rf.predict(X_test)\n",
    "conf = confusion_matrix(y_test, y_pred)\n",
    "print(conf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The correct predictions lie on the diagonal when $i=j$ because we have predicted the true value. Without labels that's a little bit harder to interpret so let's thrown into a data frame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def showmatrix(conf):\n",
    "    df_conf = pd.DataFrame(conf,\n",
    "                       index=[f\"$y$={i}\" for i in range(0,9+1)],\n",
    "                       columns=[\"$\\hat{y}$=\"+str(i) for i in range(0,9+1)])\n",
    "    return df_conf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>$\\hat{y}$=0</th>\n",
       "      <th>$\\hat{y}$=1</th>\n",
       "      <th>$\\hat{y}$=2</th>\n",
       "      <th>$\\hat{y}$=3</th>\n",
       "      <th>$\\hat{y}$=4</th>\n",
       "      <th>$\\hat{y}$=5</th>\n",
       "      <th>$\\hat{y}$=6</th>\n",
       "      <th>$\\hat{y}$=7</th>\n",
       "      <th>$\\hat{y}$=8</th>\n",
       "      <th>$\\hat{y}$=9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>$y$=0</th>\n",
       "      <td>197</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$y$=1</th>\n",
       "      <td>0</td>\n",
       "      <td>205</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$y$=2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>222</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$y$=3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>196</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$y$=4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>207</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$y$=5</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>171</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$y$=6</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>191</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$y$=7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>209</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$y$=8</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>180</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$y$=9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>167</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       $\\hat{y}$=0  $\\hat{y}$=1  $\\hat{y}$=2  $\\hat{y}$=3  $\\hat{y}$=4  \\\n",
       "$y$=0          197            0            0            0            0   \n",
       "$y$=1            0          205            0            0            0   \n",
       "$y$=2            2            0          222            0            1   \n",
       "$y$=3            1            0            4          196            0   \n",
       "$y$=4            0            0            0            0          207   \n",
       "$y$=5            4            0            0            0            0   \n",
       "$y$=6            1            0            0            0            0   \n",
       "$y$=7            0            0            2            0            0   \n",
       "$y$=8            1            2            2            1            0   \n",
       "$y$=9            0            0            0            2            2   \n",
       "\n",
       "       $\\hat{y}$=5  $\\hat{y}$=6  $\\hat{y}$=7  $\\hat{y}$=8  $\\hat{y}$=9  \n",
       "$y$=0            0            0            1            0            0  \n",
       "$y$=1            0            0            0            0            0  \n",
       "$y$=2            0            2            1            2            0  \n",
       "$y$=3            3            1            0            4            1  \n",
       "$y$=4            0            0            0            0            6  \n",
       "$y$=5          171            1            0            2            0  \n",
       "$y$=6            0          191            0            0            0  \n",
       "$y$=7            0            0          209            0            0  \n",
       "$y$=8            1            1            0          180            4  \n",
       "$y$=9            0            0            0            0          167  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "showmatrix(conf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, now let's see how well a neural network does."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vanilla one hidden layer of 50 neurons, softmax on end\n",
    "\n",
    "Our model has two differences from a binary classification problem. First, we have $k$ neurons in our final layer so that we get $k$ predictions.  Second, we use a softmax rather than a sigmoid, which gives us a likelihood for each target class:\n",
    "\n",
    "<img src=\"images/kclass.png\" width=\"200\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = nn.Sequential(\n",
    "    nn.Linear(784, 50), # 50 neurons\n",
    "    nn.ReLU(),\n",
    "    nn.Linear(50, 10),  # 10 output classes (0-9)\n",
    "    nn.Softmax(dim=1)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the training loop, we just have to change\n",
    "\n",
    "```python\n",
    "loss = F.binary_cross_entropy(y_pred, y)\n",
    "```\n",
    "\n",
    "to\n",
    "\n",
    "```python\n",
    "loss = F.cross_entropy(y_pred, y)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ctrain0(model, X, y, learning_rate = .0001, nepochs=2000):\n",
    "    optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n",
    "    for epoch in range(nepochs+1):\n",
    "        y_pred = model(X)\n",
    "        # Assume model yields softmax probabilities\n",
    "        loss = F.cross_entropy(y_pred, y)\n",
    "        if epoch % (nepochs//10) == 0:\n",
    "            print(f\"Epoch {epoch:5d} cross entropy loss {loss:7.4f}\")\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward() # autograd computes w1.grad, b1.grad, ...\n",
    "        optimizer.step()\n",
    "    print(f\"LAST        cross entropy loss {loss:7.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch     0 cross entropy loss  2.3023\n",
      "Epoch   100 cross entropy loss  1.5811\n",
      "Epoch   200 cross entropy loss  1.5831\n",
      "Epoch   300 cross entropy loss  1.4747\n",
      "Epoch   400 cross entropy loss  1.4731\n",
      "Epoch   500 cross entropy loss  1.4729\n",
      "Epoch   600 cross entropy loss  1.4728\n",
      "Epoch   700 cross entropy loss  1.4728\n",
      "Epoch   800 cross entropy loss  1.4726\n",
      "Epoch   900 cross entropy loss  1.4725\n",
      "Epoch  1000 cross entropy loss  1.4725\n",
      "LAST        cross entropy loss  1.4725\n"
     ]
    }
   ],
   "source": [
    "ctrain0(model, X_train, y_train, learning_rate = .03, nepochs=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to use the model, we can simply pass in a set of feature vectors to get the predictions. Recall that the output of the model is a $k$-vector for each input image, with a probability associated with each possible target class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([8.7113750e-10, 1.3441006e-22, 8.3794632e-15, 4.3592032e-09,\n",
       "       1.2494766e-19, 1.0248614e-07, 2.0847957e-22, 4.7591333e-05,\n",
       "       9.9995232e-01, 3.1501052e-08], dtype=float32)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_proba = model(X_train)\n",
    "y_proba[0,:].detach().numpy()  # print probabilities for first image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([8000, 10])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_proba.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get the classification from the probability, we choose the index of the largest probability across the columns, which gives us an index for each input image:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([8000])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "tensor(8)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = torch.argmax(y_proba, axis=1)\n",
    "print(y_pred.shape)\n",
    "y_pred[0] # what is the predicted digit for the first image?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to compute the overall accuracy for a set of input images, we compare the predicted digit (target class) to the known true values with `y_pred==y_train`. Summing those Boolean values up gives us the number of correct predictions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy 0.988\n"
     ]
    }
   ],
   "source": [
    "ncorrect = torch.sum(y_pred==y_train)\n",
    "train_accur = ncorrect.float() / len(y_train)\n",
    "print(f\"Training accuracy {train_accur:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAINING CONFUSION MATRIX 98.8%\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>$\\hat{y}$=0</th>\n",
       "      <th>$\\hat{y}$=1</th>\n",
       "      <th>$\\hat{y}$=2</th>\n",
       "      <th>$\\hat{y}$=3</th>\n",
       "      <th>$\\hat{y}$=4</th>\n",
       "      <th>$\\hat{y}$=5</th>\n",
       "      <th>$\\hat{y}$=6</th>\n",
       "      <th>$\\hat{y}$=7</th>\n",
       "      <th>$\\hat{y}$=8</th>\n",
       "      <th>$\\hat{y}$=9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>$y$=0</th>\n",
       "      <td>803</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$y$=1</th>\n",
       "      <td>0</td>\n",
       "      <td>878</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$y$=2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>765</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$y$=3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>769</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$y$=4</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>790</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$y$=5</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>694</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$y$=6</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>786</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$y$=7</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>840</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$y$=8</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>829</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$y$=9</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>753</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       $\\hat{y}$=0  $\\hat{y}$=1  $\\hat{y}$=2  $\\hat{y}$=3  $\\hat{y}$=4  \\\n",
       "$y$=0          803            0            0            0            2   \n",
       "$y$=1            0          878            5            0            0   \n",
       "$y$=2            0            0          765            0            0   \n",
       "$y$=3            0            0            0          769            0   \n",
       "$y$=4            0            6            0            0          790   \n",
       "$y$=5            1            1            1            1            1   \n",
       "$y$=6            0            0            0            0            0   \n",
       "$y$=7            0            4            1            2            1   \n",
       "$y$=8            0            1            0            3            0   \n",
       "$y$=9            3            0            0            8            4   \n",
       "\n",
       "       $\\hat{y}$=5  $\\hat{y}$=6  $\\hat{y}$=7  $\\hat{y}$=8  $\\hat{y}$=9  \n",
       "$y$=0            0            0            0            0            0  \n",
       "$y$=1            0            1            0            1            0  \n",
       "$y$=2            0            2            6            4            0  \n",
       "$y$=3            3            0            0            0            3  \n",
       "$y$=4            0            0            1            1            2  \n",
       "$y$=5          694            9            0            1            0  \n",
       "$y$=6            1          786            0            0            0  \n",
       "$y$=7            0            0          840            0            3  \n",
       "$y$=8            0            0            2          829            0  \n",
       "$y$=9            2            0            5            1          753  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(f\"TRAINING CONFUSION MATRIX {train_accur*100:.1f}%\")\n",
    "conf = confusion_matrix(y_train, y_pred)\n",
    "showmatrix(conf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation accuracy 0.971\n"
     ]
    }
   ],
   "source": [
    "y_test_proba = model(X_test)\n",
    "y_test_pred = torch.argmax(y_test_proba, axis=1)\n",
    "ncorrect = torch.sum(y_test_pred==y_test)\n",
    "test_accur = ncorrect.float() / len(y_test)\n",
    "print(f\"Validation accuracy {test_accur:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VALIDATION CONFUSION MATRIX 97.1%\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>$\\hat{y}$=0</th>\n",
       "      <th>$\\hat{y}$=1</th>\n",
       "      <th>$\\hat{y}$=2</th>\n",
       "      <th>$\\hat{y}$=3</th>\n",
       "      <th>$\\hat{y}$=4</th>\n",
       "      <th>$\\hat{y}$=5</th>\n",
       "      <th>$\\hat{y}$=6</th>\n",
       "      <th>$\\hat{y}$=7</th>\n",
       "      <th>$\\hat{y}$=8</th>\n",
       "      <th>$\\hat{y}$=9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>$y$=0</th>\n",
       "      <td>197</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$y$=1</th>\n",
       "      <td>0</td>\n",
       "      <td>200</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$y$=2</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>222</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$y$=3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>198</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$y$=4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>208</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$y$=5</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$y$=6</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>192</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$y$=7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>209</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$y$=8</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>180</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$y$=9</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       $\\hat{y}$=0  $\\hat{y}$=1  $\\hat{y}$=2  $\\hat{y}$=3  $\\hat{y}$=4  \\\n",
       "$y$=0          197            0            0            0            0   \n",
       "$y$=1            0          200            3            0            0   \n",
       "$y$=2            4            0          222            0            1   \n",
       "$y$=3            0            0            4          198            0   \n",
       "$y$=4            0            1            1            0          208   \n",
       "$y$=5            2            0            0            0            0   \n",
       "$y$=6            0            0            0            0            0   \n",
       "$y$=7            0            0            0            0            1   \n",
       "$y$=8            1            1            1            5            0   \n",
       "$y$=9            1            0            0            1            4   \n",
       "\n",
       "       $\\hat{y}$=5  $\\hat{y}$=6  $\\hat{y}$=7  $\\hat{y}$=8  $\\hat{y}$=9  \n",
       "$y$=0            0            0            0            0            1  \n",
       "$y$=1            0            1            0            1            0  \n",
       "$y$=2            0            1            1            1            0  \n",
       "$y$=3            6            0            0            2            0  \n",
       "$y$=4            0            1            0            0            2  \n",
       "$y$=5          174            2            0            0            0  \n",
       "$y$=6            0          192            0            0            0  \n",
       "$y$=7            0            0          209            0            1  \n",
       "$y$=8            2            1            0          180            1  \n",
       "$y$=9            2            0            1            0          162  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(f\"VALIDATION CONFUSION MATRIX {test_accur*100:.1f}%\")\n",
    "conf = confusion_matrix(y_test, y_test_pred)\n",
    "showmatrix(conf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dang. That's not quite as good as our baseline so let's keep going."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Track validation versus training loss\n",
    "\n",
    "As we did before in a previous notebook, it often helps if we track the validation error has training proceeds so we can see how it diverges from the training error. Here's an improved training method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ctrain1(model, X_train, y_train, X_test, y_test, \n",
    "            learning_rate = .0001, nepochs=2000):\n",
    "    optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n",
    "    history = []\n",
    "    for epoch in range(nepochs+1):\n",
    "        y_pred = model(X_train)\n",
    "        # Assume model yields softmax probabilities\n",
    "        loss = F.cross_entropy(y_pred, y_train)\n",
    "        y_pred = torch.argmax(y_pred, axis=1)\n",
    "        ncorrect = torch.sum(y_pred==y_train)\n",
    "        accur = ncorrect.float() / len(y_train)\n",
    "\n",
    "        y_test_pred = model(X_test)\n",
    "        loss_test = F.cross_entropy(y_test_pred, y_test)\n",
    "        y_test_pred = torch.argmax(y_test_pred, axis=1)\n",
    "        ncorrect = torch.sum(y_test_pred==y_test)\n",
    "        loss_accur = ncorrect.float() / len(y_test)\n",
    "        \n",
    "        history.append((loss, loss_test, accur, loss_accur))\n",
    "        if epoch % (nepochs//10) == 0:\n",
    "            print(f\"Epoch {epoch:4d} cross entropy loss {loss:7.4f}   test loss {loss_test:7.4f}\")\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward() # autograd computes w1.grad, b1.grad, ...\n",
    "        optimizer.step()\n",
    "    print(f\"LAST       cross entropy loss {loss:7.4f}\")\n",
    "    return torch.tensor(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make sure to create a new model otherwise it starts from where it left off\n",
    "model = nn.Sequential(\n",
    "    nn.Linear(784, 50),\n",
    "    nn.ReLU(),\n",
    "    nn.Linear(50, 10),  # 10 output classes (0-9)\n",
    "    nn.Softmax(dim=1)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch    0 cross entropy loss  2.3032   test loss  2.3027\n",
      "Epoch  100 cross entropy loss  1.4874   test loss  1.5132\n",
      "Epoch  200 cross entropy loss  1.4799   test loss  1.5065\n",
      "Epoch  300 cross entropy loss  1.4783   test loss  1.5047\n",
      "Epoch  400 cross entropy loss  1.4767   test loss  1.5049\n",
      "Epoch  500 cross entropy loss  1.4756   test loss  1.5054\n",
      "Epoch  600 cross entropy loss  1.4755   test loss  1.5044\n",
      "Epoch  700 cross entropy loss  1.4755   test loss  1.5045\n",
      "Epoch  800 cross entropy loss  1.4755   test loss  1.5044\n",
      "Epoch  900 cross entropy loss  1.4755   test loss  1.5042\n",
      "Epoch 1000 cross entropy loss  1.4754   test loss  1.5043\n",
      "LAST       cross entropy loss  1.4754\n"
     ]
    }
   ],
   "source": [
    "history = ctrain1(model, X_train, y_train, X_test, y_test,\n",
    "                  learning_rate = .03, nepochs=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy 0.985\n"
     ]
    }
   ],
   "source": [
    "y_proba = model(X_train)\n",
    "y_pred = torch.argmax(y_proba, axis=1)\n",
    "ncorrect = torch.sum(y_pred==y_train)\n",
    "train_accur = ncorrect.float() / len(y_train)\n",
    "print(f\"Training accuracy {train_accur:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation accuracy 0.959\n"
     ]
    }
   ],
   "source": [
    "y_test_proba = model(X_test)\n",
    "y_test_pred = torch.argmax(y_test_proba, axis=1)\n",
    "ncorrect = torch.sum(y_test_pred==y_test)\n",
    "test_accur = ncorrect.float() / len(y_test)\n",
    "print(f\"Validation accuracy {test_accur:.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have a complete history, we can plot the error trajectories:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 252x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 207,
       "width": 244
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 252x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 207,
       "width": 244
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_accur(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The good news is that the error curves do not seem to diverge and so our model does not seem to be overfit. On the other hand, our neural network does not perform above the random forest baseline so let's crank up the number of neurons and see what happens."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using more neurons improves accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = nn.Sequential(\n",
    "    nn.Linear(784, 300),\n",
    "    nn.ReLU(),\n",
    "    nn.Linear(300, 10),  # 10 output classes (0-9)\n",
    "    nn.Softmax(dim=1)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch    0 cross entropy loss  2.3024   test loss  2.3027\n",
      "Epoch  100 cross entropy loss  1.5792   test loss  1.5847\n",
      "Epoch  200 cross entropy loss  1.4750   test loss  1.4937\n",
      "Epoch  300 cross entropy loss  1.4729   test loss  1.4933\n",
      "Epoch  400 cross entropy loss  1.4721   test loss  1.4927\n",
      "Epoch  500 cross entropy loss  1.4719   test loss  1.4925\n",
      "Epoch  600 cross entropy loss  1.4718   test loss  1.4927\n",
      "Epoch  700 cross entropy loss  1.4710   test loss  1.4939\n",
      "Epoch  800 cross entropy loss  1.4708   test loss  1.4920\n",
      "Epoch  900 cross entropy loss  1.4707   test loss  1.4913\n",
      "Epoch 1000 cross entropy loss  1.4707   test loss  1.4911\n",
      "LAST       cross entropy loss  1.4707\n"
     ]
    }
   ],
   "source": [
    "history = ctrain1(model, X_train, y_train, X_test, y_test,\n",
    "                  learning_rate = 0.02, nepochs=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy 0.990\n"
     ]
    }
   ],
   "source": [
    "y_proba = model(X_train)\n",
    "y_pred = torch.argmax(y_proba, axis=1)\n",
    "ncorrect = torch.sum(y_pred==y_train)\n",
    "train_accur = ncorrect.float() / len(y_train)\n",
    "print(f\"Training accuracy {train_accur:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation accuracy 0.971\n"
     ]
    }
   ],
   "source": [
    "y_test_proba = model(X_test)\n",
    "y_test_pred = torch.argmax(y_test_proba, axis=1)\n",
    "ncorrect = torch.sum(y_test_pred==y_test)\n",
    "test_accur = ncorrect.float() / len(y_test)\n",
    "print(f\"Validation accuracy {test_accur:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VALIDATION CONFUSION MATRIX 97.1%\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>$\\hat{y}$=0</th>\n",
       "      <th>$\\hat{y}$=1</th>\n",
       "      <th>$\\hat{y}$=2</th>\n",
       "      <th>$\\hat{y}$=3</th>\n",
       "      <th>$\\hat{y}$=4</th>\n",
       "      <th>$\\hat{y}$=5</th>\n",
       "      <th>$\\hat{y}$=6</th>\n",
       "      <th>$\\hat{y}$=7</th>\n",
       "      <th>$\\hat{y}$=8</th>\n",
       "      <th>$\\hat{y}$=9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>$y$=0</th>\n",
       "      <td>197</td>\n",
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       "      <th>$y$=2</th>\n",
       "      <td>4</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>198</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "      <th>$y$=4</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>$y$=5</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$y$=6</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>192</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$y$=7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>209</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$y$=8</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>180</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$y$=9</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       $\\hat{y}$=0  $\\hat{y}$=1  $\\hat{y}$=2  $\\hat{y}$=3  $\\hat{y}$=4  \\\n",
       "$y$=0          197            0            0            0            0   \n",
       "$y$=1            0          200            3            0            0   \n",
       "$y$=2            4            0          222            0            1   \n",
       "$y$=3            0            0            4          198            0   \n",
       "$y$=4            0            1            1            0          208   \n",
       "$y$=5            2            0            0            0            0   \n",
       "$y$=6            0            0            0            0            0   \n",
       "$y$=7            0            0            0            0            1   \n",
       "$y$=8            1            1            1            5            0   \n",
       "$y$=9            1            0            0            1            4   \n",
       "\n",
       "       $\\hat{y}$=5  $\\hat{y}$=6  $\\hat{y}$=7  $\\hat{y}$=8  $\\hat{y}$=9  \n",
       "$y$=0            0            0            0            0            1  \n",
       "$y$=1            0            1            0            1            0  \n",
       "$y$=2            0            1            1            1            0  \n",
       "$y$=3            6            0            0            2            0  \n",
       "$y$=4            0            1            0            0            2  \n",
       "$y$=5          174            2            0            0            0  \n",
       "$y$=6            0          192            0            0            0  \n",
       "$y$=7            0            0          209            0            1  \n",
       "$y$=8            2            1            0          180            1  \n",
       "$y$=9            2            0            1            0          162  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conf = confusion_matrix(y_test, y_test_pred.detach().numpy())\n",
    "print(f\"VALIDATION CONFUSION MATRIX {test_accur*100:.1f}%\")\n",
    "showmatrix(conf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 252x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 207,
       "width": 244
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 252x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 207,
       "width": 244
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_accur(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Heh, great! We got a tiny bit more accuracy with more neurons (can vary run-to-run, though). When you are this close to 100% accurate, any improvement is very challenging so this is good.\n",
    "\n",
    "In the end, vanilla networks are not the most powerful architecture to process images. For that we need [convolutional networks](https://en.wikipedia.org/wiki/Convolutional_neural_network) or CNNs."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}