notebooks/[demo] train MLP with dropout, L2 Reg.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rc('font', size=16)\n",
    "\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda', index=0)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "device"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "from torchvision import datasets, transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.8/dist-packages/torchvision/datasets/mnist.py:498: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  /pytorch/torch/csrc/utils/tensor_numpy.cpp:180.)\n",
      "  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n"
     ]
    }
   ],
   "source": [
    "fmnist_train = datasets.FashionMNIST(\n",
    "    root=\"../../../data\", train=True, transform=transforms.ToTensor(), download=True)\n",
    "fmnist_test = datasets.FashionMNIST(\n",
    "    root=\"../../../data\", train=False, transform=transforms.ToTensor(), download=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset FashionMNIST\n",
       "    Number of datapoints: 60000\n",
       "    Root location: ../../../data\n",
       "    Split: Train\n",
       "    StandardTransform\n",
       "Transform: ToTensor()"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fmnist_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset FashionMNIST\n",
       "    Number of datapoints: 10000\n",
       "    Root location: ../../../data\n",
       "    Split: Test\n",
       "    StandardTransform\n",
       "Transform: ToTensor()"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fmnist_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 174,
       "width": 739
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels_map = {\n",
    "    0: \"T-Shirt\",\n",
    "    1: \"Trouser\",\n",
    "    2: \"Pullover\",\n",
    "    3: \"Dress\",\n",
    "    4: \"Coat\",\n",
    "    5: \"Sandal\",\n",
    "    6: \"Shirt\",\n",
    "    7: \"Sneaker\",\n",
    "    8: \"Bag\",\n",
    "    9: \"Ankle Boot\",\n",
    "}\n",
    "\n",
    "fig, axes = plt.subplots(1, 5, figsize=(13, 4))\n",
    "plt.subplots_adjust(hspace=0.001, wspace=0.001)\n",
    "\n",
    "for i in range(5):\n",
    "    image, label = fmnist_train.__getitem__(i)\n",
    "    axes[i].imshow(image.squeeze().numpy())\n",
    "    axes[i].set_title(f'{label} : {labels_map[label]}')\n",
    "    axes[i].axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 100\n",
    "\n",
    "train_loader = DataLoader(fmnist_train, batch_size=batch_size, shuffle=True)\n",
    "test_loader = DataLoader(fmnist_test, batch_size=batch_size, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature batch shape: torch.Size([100, 1, 28, 28])\n",
      "Labels batch shape: torch.Size([100])\n"
     ]
    }
   ],
   "source": [
    "# Iterate through DataLoader\n",
    "train_images, train_labels = next(iter(train_loader))\n",
    "print(f\"Feature batch shape: {train_images.shape}\")\n",
    "print(f\"Labels batch shape: {train_labels.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of iterations per epoch 600\n"
     ]
    }
   ],
   "source": [
    "print('Number of iterations per epoch', len(train_loader)) # 60000 // batch_size + 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Model\n",
    "\n",
    "### [torch.nn](https://pytorch.org/tutorials/beginner/nn_tutorial.html) modules -- a base class used to develop all neural network models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_size = 28*28\n",
    "output_size = 10\n",
    "hidden1_size = 256\n",
    "hidden2_size = 256\n",
    "\n",
    "p1 = 0.2 # dropout p for hidden layer 1\n",
    "p2 = 0.5 # dropout p for hidden layer 2\n",
    "\n",
    "## --- Model 1 (d2l §4.6) -------------------------\n",
    "## ------ MLP (3 layer network) without dropout --- \n",
    "## ------------------------------------------------\n",
    "\n",
    "class MLP(nn.Module):\n",
    "    def __init__(self, input_size, hidden1_size, hidden2_size, output_size):\n",
    "        super().__init__()        \n",
    "        self.lin1 = nn.Linear(input_size, hidden1_size)        \n",
    "        self.lin2 = nn.Linear(hidden1_size, hidden2_size)\n",
    "        self.lin3 = nn.Linear(hidden2_size, output_size)\n",
    "        self.relu = nn.ReLU()\n",
    "        \n",
    "    def forward(self, X):            \n",
    "        H1  = self.relu(self.lin1(X))\n",
    "        H2  = self.relu(self.lin2(H1))\n",
    "        out = self.lin3(H2) \n",
    "        return out\n",
    "\n",
    "## --- Model 2 (d2l §4.6) -------------------------\n",
    "## ------ MLP with dropout ------------------------ \n",
    "## ------------------------------------------------\n",
    "\n",
    "class MLP_dropout(nn.Module):\n",
    "    def __init__(self, input_size, hidden1_size, hidden2_size, output_size, p1, p2):\n",
    "        super().__init__()        \n",
    "        self.lin1 = nn.Linear(input_size, hidden1_size)        \n",
    "        self.lin2 = nn.Linear(hidden1_size, hidden2_size)\n",
    "        self.lin3 = nn.Linear(hidden2_size, output_size)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.dropout1 = nn.Dropout(p1)\n",
    "        self.dropout2 = nn.Dropout(p2)\n",
    "        \n",
    "    def forward(self, X):            \n",
    "        H1  = self.relu(self.lin1(X))\n",
    "        H1  = self.dropout1(H1)\n",
    "        H2  = self.relu(self.lin2(H1))\n",
    "        H2  = self.dropout2(H1)\n",
    "        out = self.lin3(H2) \n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLP(\n",
       "  (lin1): Linear(in_features=784, out_features=256, bias=True)\n",
       "  (lin2): Linear(in_features=256, out_features=256, bias=True)\n",
       "  (lin3): Linear(in_features=256, out_features=10, bias=True)\n",
       "  (relu): ReLU()\n",
       ")"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# model original\n",
    "model_ori = MLP(input_size, hidden1_size, hidden2_size, output_size)\n",
    "model_ori.to(device)\n",
    "\n",
    "# model with dropout\n",
    "model_dropout = MLP_dropout(input_size, hidden1_size, hidden2_size, output_size, p1, p2)\n",
    "model_dropout.to(device)\n",
    "\n",
    "# model with L2 regularization\n",
    "model_L2 = MLP(input_size, hidden1_size, hidden2_size, output_size)\n",
    "model_L2.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TOTAL  : 269322\n"
     ]
    }
   ],
   "source": [
    "# count model parameters\n",
    "print('TOTAL  :', sum(param.numel() for param in model_ori.parameters() if param.requires_grad))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = nn.CrossEntropyLoss()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Usage : `loss = criterion(Y_pred, Y)`\n",
    "\n",
    "**Careful!** <br>\n",
    "`nn.CrossEntropyLoss` applies: ``nn.LogSoftmax`` and ``nn.NLLLoss`` (negative log likelihood loss). <br>\n",
    "So don't apply softmax in the forward loop. <br>\n",
    "``Y`` is class (digit) labels, not one-hot!   <br>\n",
    "``Y_pred`` has raw scores (logits), no Softmax!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.optim as optim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "learning_rate = 0.1\n",
    "weight_decay = 0.001\n",
    "\n",
    "optimizer_ori = torch.optim.SGD(model_ori.parameters(), lr=learning_rate)\n",
    "optimizer_dropout = torch.optim.SGD(model_dropout.parameters(), lr=learning_rate)\n",
    "optimizer_L2 = torch.optim.SGD(model_L2.parameters(), lr=learning_rate, weight_decay=weight_decay)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Training Loop\n",
    "\n",
    "### loss.backward() : calculate dloss/dw_i \n",
    "\n",
    "### optimizer.step()  : update network parameters (w_i)\n",
    "\n",
    "### optimizer.zero_grad() \n",
    "Whenever `loss.backward()` is called, it'll write the gradients and accumulate them in the w_i.grad attribute. <br>\n",
    "e.g. try to print out `model.linear1.weight.grad` <br>\n",
    "So we need to call `optimizer.zero_grad()` to zero gradient again and again for every iteration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_1_epoch(model, optimizer, train_loss, train_acc):\n",
    "    \n",
    "    model.train()             # set model to training mode\n",
    "\n",
    "    running_loss = 0.\n",
    "    running_corrects = 0\n",
    "    for i, (images, labels) in enumerate(train_loader):\n",
    "        # reshape images (100, 1, 28, 28) -> (100, 784)\n",
    "        images = images.reshape(-1, 28*28).to(device) \n",
    "        labels = labels.to(device)\n",
    "        \n",
    "        # forward\n",
    "        outputs = model(images)\n",
    "        \n",
    "        # loss\n",
    "        loss = criterion(outputs, labels) # predicted outputs v.s. actual labels\n",
    "        \n",
    "        # backwards\n",
    "        optimizer.zero_grad()  # zero gradient\n",
    "        loss.backward()        # loss.backward would calculate dloss/dw_i  \n",
    "        optimizer.step()       # update network parameters (w_i)\n",
    "        \n",
    "        # compute statistics (avg. loss, accuracy)\n",
    "        running_loss += loss.item() * labels.size(0)\n",
    "        _, predictions = torch.max(outputs, 1)\n",
    "        running_corrects += torch.sum(predictions == labels.data)\n",
    "        \n",
    "        #if (i+1)%300 == 0: # every 300 steps, print something\n",
    "        #    print(f'epoch {epoch+1}/{num_epochs}, step {i+1}/{len(train_loader)}, loss={loss.item():.4f}')\n",
    "            \n",
    "    epoch_loss = running_loss / len(fmnist_train)            ; train_loss.append(epoch_loss)\n",
    "    epoch_acc  = running_corrects / len(fmnist_train) * 100. ; train_acc.append(epoch_acc)\n",
    "    print(f'>>> End epoch {epoch+1}, train accuracy = {epoch_acc:.2f}%, train avgLoss = {epoch_loss:.4f}')\n",
    "\n",
    "    \n",
    "    \n",
    "def validation(model, valid_loss, valid_acc):\n",
    "    \n",
    "    model.eval()              # set model to evaluation mode\n",
    "    \n",
    "    running_loss = 0.\n",
    "    running_corrects = 0\n",
    "    \n",
    "    for i, (images, labels) in enumerate(test_loader):\n",
    "        \n",
    "        images = images.reshape(-1, 28*28).to(device)\n",
    "        labels = labels.to(device)\n",
    "        outputs = model(images)\n",
    "        \n",
    "        loss = criterion(outputs, labels)\n",
    "        \n",
    "        # compute statistics (avg. loss, accuracy)\n",
    "        running_loss += loss.item() * labels.size(0)\n",
    "        _, predictions = torch.max(outputs, 1)\n",
    "        running_corrects += torch.sum(predictions == labels.data)\n",
    "    \n",
    "    valid_loss.append(running_loss/len(test_loader.dataset))\n",
    "    valid_acc.append(running_corrects/len(test_loader.dataset) * 100.)\n",
    "    print(f'>>>              valid accuracy = {valid_acc[-1]:.2f}%, valid avgLoss = {valid_loss[-1]:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train model without any regularization techniques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> End epoch 1, train accuracy = 73.58%, train avgLoss = 0.7486\n",
      ">>>              valid accuracy = 78.52%, valid avgLoss = 0.5689\n",
      ">>> End epoch 2, train accuracy = 83.27%, train avgLoss = 0.4651\n",
      ">>>              valid accuracy = 83.76%, valid avgLoss = 0.4572\n",
      ">>> End epoch 3, train accuracy = 85.01%, train avgLoss = 0.4112\n",
      ">>>              valid accuracy = 84.80%, valid avgLoss = 0.4210\n",
      ">>> End epoch 4, train accuracy = 86.25%, train avgLoss = 0.3750\n",
      ">>>              valid accuracy = 85.12%, valid avgLoss = 0.4003\n",
      ">>> End epoch 5, train accuracy = 87.18%, train avgLoss = 0.3530\n",
      ">>>              valid accuracy = 86.13%, valid avgLoss = 0.3850\n",
      ">>> End epoch 6, train accuracy = 87.78%, train avgLoss = 0.3366\n",
      ">>>              valid accuracy = 85.66%, valid avgLoss = 0.3833\n",
      ">>> End epoch 7, train accuracy = 88.11%, train avgLoss = 0.3193\n",
      ">>>              valid accuracy = 87.10%, valid avgLoss = 0.3587\n",
      ">>> End epoch 8, train accuracy = 88.61%, train avgLoss = 0.3089\n",
      ">>>              valid accuracy = 87.17%, valid avgLoss = 0.3596\n",
      ">>> End epoch 9, train accuracy = 88.98%, train avgLoss = 0.2984\n",
      ">>>              valid accuracy = 86.64%, valid avgLoss = 0.3620\n",
      ">>> End epoch 10, train accuracy = 89.29%, train avgLoss = 0.2878\n",
      ">>>              valid accuracy = 87.72%, valid avgLoss = 0.3433\n",
      ">>> End epoch 11, train accuracy = 89.68%, train avgLoss = 0.2778\n",
      ">>>              valid accuracy = 87.65%, valid avgLoss = 0.3429\n",
      ">>> End epoch 12, train accuracy = 90.17%, train avgLoss = 0.2688\n",
      ">>>              valid accuracy = 87.83%, valid avgLoss = 0.3360\n",
      ">>> End epoch 13, train accuracy = 90.17%, train avgLoss = 0.2635\n",
      ">>>              valid accuracy = 88.44%, valid avgLoss = 0.3305\n",
      ">>> End epoch 14, train accuracy = 90.40%, train avgLoss = 0.2566\n",
      ">>>              valid accuracy = 88.49%, valid avgLoss = 0.3202\n",
      ">>> End epoch 15, train accuracy = 90.76%, train avgLoss = 0.2485\n",
      ">>>              valid accuracy = 87.60%, valid avgLoss = 0.3441\n",
      ">>> End epoch 16, train accuracy = 90.99%, train avgLoss = 0.2425\n",
      ">>>              valid accuracy = 88.07%, valid avgLoss = 0.3324\n",
      ">>> End epoch 17, train accuracy = 91.22%, train avgLoss = 0.2368\n",
      ">>>              valid accuracy = 87.41%, valid avgLoss = 0.3519\n",
      ">>> End epoch 18, train accuracy = 91.33%, train avgLoss = 0.2314\n",
      ">>>              valid accuracy = 87.25%, valid avgLoss = 0.3526\n",
      ">>> End epoch 19, train accuracy = 91.56%, train avgLoss = 0.2255\n",
      ">>>              valid accuracy = 88.46%, valid avgLoss = 0.3311\n",
      ">>> End epoch 20, train accuracy = 91.97%, train avgLoss = 0.2179\n",
      ">>>              valid accuracy = 88.73%, valid avgLoss = 0.3264\n",
      ">>> End epoch 21, train accuracy = 92.00%, train avgLoss = 0.2127\n",
      ">>>              valid accuracy = 89.02%, valid avgLoss = 0.3143\n",
      ">>> End epoch 22, train accuracy = 92.17%, train avgLoss = 0.2085\n",
      ">>>              valid accuracy = 88.17%, valid avgLoss = 0.3354\n",
      ">>> End epoch 23, train accuracy = 92.30%, train avgLoss = 0.2048\n",
      ">>>              valid accuracy = 88.99%, valid avgLoss = 0.3187\n",
      ">>> End epoch 24, train accuracy = 92.49%, train avgLoss = 0.1994\n",
      ">>>              valid accuracy = 89.07%, valid avgLoss = 0.3311\n",
      ">>> End epoch 25, train accuracy = 92.71%, train avgLoss = 0.1956\n",
      ">>>              valid accuracy = 88.73%, valid avgLoss = 0.3204\n",
      ">>> End epoch 26, train accuracy = 92.73%, train avgLoss = 0.1931\n",
      ">>>              valid accuracy = 89.04%, valid avgLoss = 0.3175\n",
      ">>> End epoch 27, train accuracy = 92.90%, train avgLoss = 0.1877\n",
      ">>>              valid accuracy = 89.05%, valid avgLoss = 0.3189\n",
      ">>> End epoch 28, train accuracy = 93.11%, train avgLoss = 0.1834\n",
      ">>>              valid accuracy = 89.12%, valid avgLoss = 0.3232\n",
      ">>> End epoch 29, train accuracy = 93.36%, train avgLoss = 0.1789\n",
      ">>>              valid accuracy = 89.26%, valid avgLoss = 0.3218\n",
      ">>> End epoch 30, train accuracy = 93.45%, train avgLoss = 0.1747\n",
      ">>>              valid accuracy = 89.09%, valid avgLoss = 0.3208\n",
      ">>> End epoch 31, train accuracy = 93.75%, train avgLoss = 0.1683\n",
      ">>>              valid accuracy = 89.03%, valid avgLoss = 0.3320\n",
      ">>> End epoch 32, train accuracy = 93.77%, train avgLoss = 0.1671\n",
      ">>>              valid accuracy = 89.31%, valid avgLoss = 0.3204\n",
      ">>> End epoch 33, train accuracy = 93.89%, train avgLoss = 0.1637\n",
      ">>>              valid accuracy = 89.23%, valid avgLoss = 0.3334\n",
      ">>> End epoch 34, train accuracy = 94.14%, train avgLoss = 0.1586\n",
      ">>>              valid accuracy = 89.49%, valid avgLoss = 0.3195\n",
      ">>> End epoch 35, train accuracy = 94.18%, train avgLoss = 0.1561\n",
      ">>>              valid accuracy = 88.21%, valid avgLoss = 0.3536\n",
      ">>> End epoch 36, train accuracy = 94.31%, train avgLoss = 0.1526\n",
      ">>>              valid accuracy = 89.09%, valid avgLoss = 0.3407\n",
      ">>> End epoch 37, train accuracy = 94.42%, train avgLoss = 0.1490\n",
      ">>>              valid accuracy = 89.30%, valid avgLoss = 0.3429\n",
      ">>> End epoch 38, train accuracy = 94.60%, train avgLoss = 0.1461\n",
      ">>>              valid accuracy = 88.90%, valid avgLoss = 0.3529\n",
      ">>> End epoch 39, train accuracy = 94.73%, train avgLoss = 0.1430\n",
      ">>>              valid accuracy = 88.92%, valid avgLoss = 0.3645\n",
      ">>> End epoch 40, train accuracy = 94.89%, train avgLoss = 0.1375\n",
      ">>>              valid accuracy = 87.96%, valid avgLoss = 0.3943\n",
      ">>> End epoch 41, train accuracy = 94.89%, train avgLoss = 0.1371\n",
      ">>>              valid accuracy = 89.42%, valid avgLoss = 0.3464\n",
      ">>> End epoch 42, train accuracy = 95.16%, train avgLoss = 0.1311\n",
      ">>>              valid accuracy = 88.85%, valid avgLoss = 0.3653\n",
      ">>> End epoch 43, train accuracy = 95.24%, train avgLoss = 0.1303\n",
      ">>>              valid accuracy = 89.36%, valid avgLoss = 0.3423\n",
      ">>> End epoch 44, train accuracy = 95.18%, train avgLoss = 0.1287\n",
      ">>>              valid accuracy = 89.41%, valid avgLoss = 0.3432\n",
      ">>> End epoch 45, train accuracy = 95.42%, train avgLoss = 0.1227\n",
      ">>>              valid accuracy = 88.98%, valid avgLoss = 0.3497\n",
      ">>> End epoch 46, train accuracy = 95.54%, train avgLoss = 0.1207\n",
      ">>>              valid accuracy = 89.12%, valid avgLoss = 0.3711\n",
      ">>> End epoch 47, train accuracy = 95.59%, train avgLoss = 0.1189\n",
      ">>>              valid accuracy = 88.94%, valid avgLoss = 0.3684\n",
      ">>> End epoch 48, train accuracy = 95.66%, train avgLoss = 0.1164\n",
      ">>>              valid accuracy = 88.25%, valid avgLoss = 0.4091\n",
      ">>> End epoch 49, train accuracy = 95.65%, train avgLoss = 0.1134\n",
      ">>>              valid accuracy = 89.18%, valid avgLoss = 0.3714\n",
      ">>> End epoch 50, train accuracy = 96.09%, train avgLoss = 0.1078\n",
      ">>>              valid accuracy = 89.36%, valid avgLoss = 0.3760\n",
      ">>> End epoch 51, train accuracy = 96.02%, train avgLoss = 0.1082\n",
      ">>>              valid accuracy = 89.25%, valid avgLoss = 0.3760\n",
      ">>> End epoch 52, train accuracy = 96.01%, train avgLoss = 0.1067\n",
      ">>>              valid accuracy = 89.40%, valid avgLoss = 0.3910\n",
      ">>> End epoch 53, train accuracy = 96.19%, train avgLoss = 0.1033\n",
      ">>>              valid accuracy = 89.09%, valid avgLoss = 0.4081\n",
      ">>> End epoch 54, train accuracy = 96.25%, train avgLoss = 0.1016\n",
      ">>>              valid accuracy = 89.15%, valid avgLoss = 0.3809\n",
      ">>> End epoch 55, train accuracy = 96.32%, train avgLoss = 0.0979\n",
      ">>>              valid accuracy = 89.44%, valid avgLoss = 0.3929\n",
      ">>> End epoch 56, train accuracy = 96.45%, train avgLoss = 0.0958\n",
      ">>>              valid accuracy = 89.10%, valid avgLoss = 0.3801\n",
      ">>> End epoch 57, train accuracy = 96.41%, train avgLoss = 0.0948\n",
      ">>>              valid accuracy = 89.55%, valid avgLoss = 0.3922\n",
      ">>> End epoch 58, train accuracy = 96.61%, train avgLoss = 0.0911\n",
      ">>>              valid accuracy = 89.01%, valid avgLoss = 0.4028\n",
      ">>> End epoch 59, train accuracy = 96.65%, train avgLoss = 0.0905\n",
      ">>>              valid accuracy = 89.69%, valid avgLoss = 0.3876\n",
      ">>> End epoch 60, train accuracy = 96.81%, train avgLoss = 0.0874\n",
      ">>>              valid accuracy = 89.21%, valid avgLoss = 0.4029\n"
     ]
    }
   ],
   "source": [
    "train_loss_ori = []\n",
    "train_acc_ori = []\n",
    "valid_loss_ori = []\n",
    "valid_acc_ori = []\n",
    "\n",
    "num_epochs = 60\n",
    "for epoch in range(0, num_epochs):\n",
    "    train_1_epoch(model_ori, optimizer_ori, train_loss_ori, train_acc_ori)\n",
    "    validation(model_ori, valid_loss_ori, valid_acc_ori)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train model with dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> End epoch 1, train accuracy = 73.11%, train avgLoss = 0.7659\n",
      ">>>              valid accuracy = 80.96%, valid avgLoss = 0.5260\n",
      ">>> End epoch 2, train accuracy = 81.43%, train avgLoss = 0.5261\n",
      ">>>              valid accuracy = 83.33%, valid avgLoss = 0.4685\n",
      ">>> End epoch 3, train accuracy = 82.99%, train avgLoss = 0.4778\n",
      ">>>              valid accuracy = 84.23%, valid avgLoss = 0.4376\n",
      ">>> End epoch 4, train accuracy = 83.90%, train avgLoss = 0.4491\n",
      ">>>              valid accuracy = 84.38%, valid avgLoss = 0.4296\n",
      ">>> End epoch 5, train accuracy = 84.25%, train avgLoss = 0.4338\n",
      ">>>              valid accuracy = 84.89%, valid avgLoss = 0.4142\n",
      ">>> End epoch 6, train accuracy = 85.08%, train avgLoss = 0.4172\n",
      ">>>              valid accuracy = 84.70%, valid avgLoss = 0.4119\n",
      ">>> End epoch 7, train accuracy = 85.46%, train avgLoss = 0.4080\n",
      ">>>              valid accuracy = 85.81%, valid avgLoss = 0.3903\n",
      ">>> End epoch 8, train accuracy = 85.77%, train avgLoss = 0.3981\n",
      ">>>              valid accuracy = 85.94%, valid avgLoss = 0.3885\n",
      ">>> End epoch 9, train accuracy = 85.97%, train avgLoss = 0.3867\n",
      ">>>              valid accuracy = 86.11%, valid avgLoss = 0.3845\n",
      ">>> End epoch 10, train accuracy = 86.25%, train avgLoss = 0.3808\n",
      ">>>              valid accuracy = 86.36%, valid avgLoss = 0.3790\n",
      ">>> End epoch 11, train accuracy = 86.38%, train avgLoss = 0.3762\n",
      ">>>              valid accuracy = 86.58%, valid avgLoss = 0.3703\n",
      ">>> End epoch 12, train accuracy = 86.56%, train avgLoss = 0.3697\n",
      ">>>              valid accuracy = 86.55%, valid avgLoss = 0.3759\n",
      ">>> End epoch 13, train accuracy = 86.74%, train avgLoss = 0.3665\n",
      ">>>              valid accuracy = 86.58%, valid avgLoss = 0.3678\n",
      ">>> End epoch 14, train accuracy = 87.03%, train avgLoss = 0.3593\n",
      ">>>              valid accuracy = 87.00%, valid avgLoss = 0.3609\n",
      ">>> End epoch 15, train accuracy = 87.19%, train avgLoss = 0.3565\n",
      ">>>              valid accuracy = 87.48%, valid avgLoss = 0.3561\n",
      ">>> End epoch 16, train accuracy = 87.25%, train avgLoss = 0.3516\n",
      ">>>              valid accuracy = 87.17%, valid avgLoss = 0.3524\n",
      ">>> End epoch 17, train accuracy = 87.35%, train avgLoss = 0.3480\n",
      ">>>              valid accuracy = 87.28%, valid avgLoss = 0.3510\n",
      ">>> End epoch 18, train accuracy = 87.40%, train avgLoss = 0.3436\n",
      ">>>              valid accuracy = 87.13%, valid avgLoss = 0.3544\n",
      ">>> End epoch 19, train accuracy = 87.48%, train avgLoss = 0.3424\n",
      ">>>              valid accuracy = 87.18%, valid avgLoss = 0.3546\n",
      ">>> End epoch 20, train accuracy = 87.64%, train avgLoss = 0.3372\n",
      ">>>              valid accuracy = 87.58%, valid avgLoss = 0.3513\n",
      ">>> End epoch 21, train accuracy = 87.83%, train avgLoss = 0.3334\n",
      ">>>              valid accuracy = 87.46%, valid avgLoss = 0.3476\n",
      ">>> End epoch 22, train accuracy = 87.88%, train avgLoss = 0.3307\n",
      ">>>              valid accuracy = 87.63%, valid avgLoss = 0.3481\n",
      ">>> End epoch 23, train accuracy = 88.07%, train avgLoss = 0.3269\n",
      ">>>              valid accuracy = 87.37%, valid avgLoss = 0.3526\n",
      ">>> End epoch 24, train accuracy = 88.13%, train avgLoss = 0.3251\n",
      ">>>              valid accuracy = 87.92%, valid avgLoss = 0.3422\n",
      ">>> End epoch 25, train accuracy = 88.17%, train avgLoss = 0.3247\n",
      ">>>              valid accuracy = 87.89%, valid avgLoss = 0.3408\n",
      ">>> End epoch 26, train accuracy = 88.24%, train avgLoss = 0.3226\n",
      ">>>              valid accuracy = 87.99%, valid avgLoss = 0.3368\n",
      ">>> End epoch 27, train accuracy = 88.38%, train avgLoss = 0.3198\n",
      ">>>              valid accuracy = 87.88%, valid avgLoss = 0.3390\n",
      ">>> End epoch 28, train accuracy = 88.49%, train avgLoss = 0.3183\n",
      ">>>              valid accuracy = 87.68%, valid avgLoss = 0.3389\n",
      ">>> End epoch 29, train accuracy = 88.46%, train avgLoss = 0.3155\n",
      ">>>              valid accuracy = 87.96%, valid avgLoss = 0.3397\n",
      ">>> End epoch 30, train accuracy = 88.47%, train avgLoss = 0.3124\n",
      ">>>              valid accuracy = 88.33%, valid avgLoss = 0.3335\n",
      ">>> End epoch 31, train accuracy = 88.54%, train avgLoss = 0.3107\n",
      ">>>              valid accuracy = 88.23%, valid avgLoss = 0.3314\n",
      ">>> End epoch 32, train accuracy = 88.62%, train avgLoss = 0.3096\n",
      ">>>              valid accuracy = 88.13%, valid avgLoss = 0.3331\n",
      ">>> End epoch 33, train accuracy = 88.85%, train avgLoss = 0.3064\n",
      ">>>              valid accuracy = 88.09%, valid avgLoss = 0.3370\n",
      ">>> End epoch 34, train accuracy = 88.65%, train avgLoss = 0.3070\n",
      ">>>              valid accuracy = 88.33%, valid avgLoss = 0.3301\n",
      ">>> End epoch 35, train accuracy = 88.73%, train avgLoss = 0.3038\n",
      ">>>              valid accuracy = 88.09%, valid avgLoss = 0.3343\n",
      ">>> End epoch 36, train accuracy = 88.84%, train avgLoss = 0.3029\n",
      ">>>              valid accuracy = 88.46%, valid avgLoss = 0.3262\n",
      ">>> End epoch 37, train accuracy = 88.92%, train avgLoss = 0.2997\n",
      ">>>              valid accuracy = 88.23%, valid avgLoss = 0.3278\n",
      ">>> End epoch 38, train accuracy = 89.06%, train avgLoss = 0.2971\n",
      ">>>              valid accuracy = 88.22%, valid avgLoss = 0.3309\n",
      ">>> End epoch 39, train accuracy = 89.22%, train avgLoss = 0.2959\n",
      ">>>              valid accuracy = 88.08%, valid avgLoss = 0.3336\n",
      ">>> End epoch 40, train accuracy = 89.03%, train avgLoss = 0.2949\n",
      ">>>              valid accuracy = 88.69%, valid avgLoss = 0.3227\n",
      ">>> End epoch 41, train accuracy = 89.10%, train avgLoss = 0.2929\n",
      ">>>              valid accuracy = 88.71%, valid avgLoss = 0.3220\n",
      ">>> End epoch 42, train accuracy = 89.26%, train avgLoss = 0.2888\n",
      ">>>              valid accuracy = 88.48%, valid avgLoss = 0.3259\n",
      ">>> End epoch 43, train accuracy = 89.33%, train avgLoss = 0.2884\n",
      ">>>              valid accuracy = 88.45%, valid avgLoss = 0.3288\n",
      ">>> End epoch 44, train accuracy = 89.36%, train avgLoss = 0.2887\n",
      ">>>              valid accuracy = 88.24%, valid avgLoss = 0.3363\n",
      ">>> End epoch 45, train accuracy = 89.41%, train avgLoss = 0.2871\n",
      ">>>              valid accuracy = 88.77%, valid avgLoss = 0.3234\n",
      ">>> End epoch 46, train accuracy = 89.54%, train avgLoss = 0.2863\n",
      ">>>              valid accuracy = 88.72%, valid avgLoss = 0.3230\n",
      ">>> End epoch 47, train accuracy = 89.47%, train avgLoss = 0.2834\n",
      ">>>              valid accuracy = 88.68%, valid avgLoss = 0.3249\n",
      ">>> End epoch 48, train accuracy = 89.54%, train avgLoss = 0.2825\n",
      ">>>              valid accuracy = 88.56%, valid avgLoss = 0.3287\n",
      ">>> End epoch 49, train accuracy = 89.65%, train avgLoss = 0.2799\n",
      ">>>              valid accuracy = 88.69%, valid avgLoss = 0.3245\n",
      ">>> End epoch 50, train accuracy = 89.61%, train avgLoss = 0.2795\n",
      ">>>              valid accuracy = 88.52%, valid avgLoss = 0.3268\n",
      ">>> End epoch 51, train accuracy = 89.47%, train avgLoss = 0.2817\n",
      ">>>              valid accuracy = 88.88%, valid avgLoss = 0.3236\n",
      ">>> End epoch 52, train accuracy = 89.64%, train avgLoss = 0.2761\n",
      ">>>              valid accuracy = 88.44%, valid avgLoss = 0.3219\n",
      ">>> End epoch 53, train accuracy = 89.78%, train avgLoss = 0.2743\n",
      ">>>              valid accuracy = 88.41%, valid avgLoss = 0.3233\n",
      ">>> End epoch 54, train accuracy = 89.73%, train avgLoss = 0.2739\n",
      ">>>              valid accuracy = 88.35%, valid avgLoss = 0.3269\n",
      ">>> End epoch 55, train accuracy = 89.72%, train avgLoss = 0.2746\n",
      ">>>              valid accuracy = 88.67%, valid avgLoss = 0.3183\n",
      ">>> End epoch 56, train accuracy = 89.99%, train avgLoss = 0.2714\n",
      ">>>              valid accuracy = 88.73%, valid avgLoss = 0.3233\n",
      ">>> End epoch 57, train accuracy = 89.78%, train avgLoss = 0.2719\n",
      ">>>              valid accuracy = 88.88%, valid avgLoss = 0.3211\n",
      ">>> End epoch 58, train accuracy = 89.99%, train avgLoss = 0.2718\n",
      ">>>              valid accuracy = 88.71%, valid avgLoss = 0.3233\n",
      ">>> End epoch 59, train accuracy = 89.96%, train avgLoss = 0.2707\n",
      ">>>              valid accuracy = 88.67%, valid avgLoss = 0.3231\n",
      ">>> End epoch 60, train accuracy = 89.95%, train avgLoss = 0.2683\n",
      ">>>              valid accuracy = 88.57%, valid avgLoss = 0.3277\n"
     ]
    }
   ],
   "source": [
    "train_loss_dropout = []\n",
    "train_acc_dropout = []\n",
    "valid_loss_dropout = []\n",
    "valid_acc_dropout = []\n",
    "\n",
    "num_epochs = 60\n",
    "for epoch in range(0, num_epochs):\n",
    "    train_1_epoch(model_dropout, optimizer_dropout, train_loss_dropout, train_acc_dropout)\n",
    "    validation(model_dropout, valid_loss_dropout, valid_acc_dropout)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train model with L2 Regularization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> End epoch 1, train accuracy = 73.40%, train avgLoss = 0.7555\n",
      ">>>              valid accuracy = 80.46%, valid avgLoss = 0.5321\n",
      ">>> End epoch 2, train accuracy = 82.86%, train avgLoss = 0.4758\n",
      ">>>              valid accuracy = 83.64%, valid avgLoss = 0.4566\n",
      ">>> End epoch 3, train accuracy = 84.90%, train avgLoss = 0.4194\n",
      ">>>              valid accuracy = 84.69%, valid avgLoss = 0.4260\n",
      ">>> End epoch 4, train accuracy = 85.68%, train avgLoss = 0.3924\n",
      ">>>              valid accuracy = 84.93%, valid avgLoss = 0.4135\n",
      ">>> End epoch 5, train accuracy = 86.60%, train avgLoss = 0.3704\n",
      ">>>              valid accuracy = 84.76%, valid avgLoss = 0.4172\n",
      ">>> End epoch 6, train accuracy = 86.93%, train avgLoss = 0.3559\n",
      ">>>              valid accuracy = 85.85%, valid avgLoss = 0.3949\n",
      ">>> End epoch 7, train accuracy = 87.51%, train avgLoss = 0.3423\n",
      ">>>              valid accuracy = 85.74%, valid avgLoss = 0.3936\n",
      ">>> End epoch 8, train accuracy = 87.78%, train avgLoss = 0.3339\n",
      ">>>              valid accuracy = 87.14%, valid avgLoss = 0.3592\n",
      ">>> End epoch 9, train accuracy = 88.33%, train avgLoss = 0.3247\n",
      ">>>              valid accuracy = 86.13%, valid avgLoss = 0.3747\n",
      ">>> End epoch 10, train accuracy = 88.44%, train avgLoss = 0.3189\n",
      ">>>              valid accuracy = 86.33%, valid avgLoss = 0.3794\n",
      ">>> End epoch 11, train accuracy = 88.58%, train avgLoss = 0.3123\n",
      ">>>              valid accuracy = 86.85%, valid avgLoss = 0.3649\n",
      ">>> End epoch 12, train accuracy = 88.87%, train avgLoss = 0.3066\n",
      ">>>              valid accuracy = 86.79%, valid avgLoss = 0.3596\n",
      ">>> End epoch 13, train accuracy = 88.95%, train avgLoss = 0.3021\n",
      ">>>              valid accuracy = 86.91%, valid avgLoss = 0.3565\n",
      ">>> End epoch 14, train accuracy = 89.24%, train avgLoss = 0.2971\n",
      ">>>              valid accuracy = 87.40%, valid avgLoss = 0.3486\n",
      ">>> End epoch 15, train accuracy = 89.26%, train avgLoss = 0.2926\n",
      ">>>              valid accuracy = 87.33%, valid avgLoss = 0.3512\n",
      ">>> End epoch 16, train accuracy = 89.54%, train avgLoss = 0.2889\n",
      ">>>              valid accuracy = 87.64%, valid avgLoss = 0.3415\n",
      ">>> End epoch 17, train accuracy = 89.61%, train avgLoss = 0.2864\n",
      ">>>              valid accuracy = 86.93%, valid avgLoss = 0.3504\n",
      ">>> End epoch 18, train accuracy = 89.56%, train avgLoss = 0.2821\n",
      ">>>              valid accuracy = 87.86%, valid avgLoss = 0.3380\n",
      ">>> End epoch 19, train accuracy = 89.73%, train avgLoss = 0.2777\n",
      ">>>              valid accuracy = 87.70%, valid avgLoss = 0.3367\n",
      ">>> End epoch 20, train accuracy = 89.87%, train avgLoss = 0.2770\n",
      ">>>              valid accuracy = 87.94%, valid avgLoss = 0.3318\n",
      ">>> End epoch 21, train accuracy = 90.04%, train avgLoss = 0.2735\n",
      ">>>              valid accuracy = 87.01%, valid avgLoss = 0.3504\n",
      ">>> End epoch 22, train accuracy = 89.99%, train avgLoss = 0.2702\n",
      ">>>              valid accuracy = 87.81%, valid avgLoss = 0.3332\n",
      ">>> End epoch 23, train accuracy = 90.18%, train avgLoss = 0.2681\n",
      ">>>              valid accuracy = 87.87%, valid avgLoss = 0.3359\n",
      ">>> End epoch 24, train accuracy = 90.18%, train avgLoss = 0.2673\n",
      ">>>              valid accuracy = 88.11%, valid avgLoss = 0.3297\n",
      ">>> End epoch 25, train accuracy = 90.25%, train avgLoss = 0.2640\n",
      ">>>              valid accuracy = 87.01%, valid avgLoss = 0.3456\n",
      ">>> End epoch 26, train accuracy = 90.36%, train avgLoss = 0.2624\n",
      ">>>              valid accuracy = 87.37%, valid avgLoss = 0.3426\n",
      ">>> End epoch 27, train accuracy = 90.46%, train avgLoss = 0.2604\n",
      ">>>              valid accuracy = 87.48%, valid avgLoss = 0.3480\n",
      ">>> End epoch 28, train accuracy = 90.50%, train avgLoss = 0.2589\n",
      ">>>              valid accuracy = 88.63%, valid avgLoss = 0.3189\n",
      ">>> End epoch 29, train accuracy = 90.54%, train avgLoss = 0.2575\n",
      ">>>              valid accuracy = 87.49%, valid avgLoss = 0.3424\n",
      ">>> End epoch 30, train accuracy = 90.67%, train avgLoss = 0.2565\n",
      ">>>              valid accuracy = 88.62%, valid avgLoss = 0.3140\n",
      ">>> End epoch 31, train accuracy = 90.78%, train avgLoss = 0.2547\n",
      ">>>              valid accuracy = 88.52%, valid avgLoss = 0.3183\n",
      ">>> End epoch 32, train accuracy = 90.72%, train avgLoss = 0.2536\n",
      ">>>              valid accuracy = 88.01%, valid avgLoss = 0.3364\n",
      ">>> End epoch 33, train accuracy = 90.77%, train avgLoss = 0.2521\n",
      ">>>              valid accuracy = 88.71%, valid avgLoss = 0.3138\n",
      ">>> End epoch 34, train accuracy = 90.77%, train avgLoss = 0.2506\n",
      ">>>              valid accuracy = 88.39%, valid avgLoss = 0.3237\n",
      ">>> End epoch 35, train accuracy = 90.79%, train avgLoss = 0.2503\n",
      ">>>              valid accuracy = 88.52%, valid avgLoss = 0.3196\n",
      ">>> End epoch 36, train accuracy = 90.97%, train avgLoss = 0.2464\n",
      ">>>              valid accuracy = 88.15%, valid avgLoss = 0.3287\n",
      ">>> End epoch 37, train accuracy = 90.86%, train avgLoss = 0.2504\n",
      ">>>              valid accuracy = 88.13%, valid avgLoss = 0.3357\n",
      ">>> End epoch 38, train accuracy = 90.90%, train avgLoss = 0.2459\n",
      ">>>              valid accuracy = 88.21%, valid avgLoss = 0.3286\n",
      ">>> End epoch 39, train accuracy = 91.07%, train avgLoss = 0.2452\n",
      ">>>              valid accuracy = 88.41%, valid avgLoss = 0.3232\n",
      ">>> End epoch 40, train accuracy = 91.05%, train avgLoss = 0.2443\n",
      ">>>              valid accuracy = 87.35%, valid avgLoss = 0.3378\n",
      ">>> End epoch 41, train accuracy = 91.11%, train avgLoss = 0.2420\n",
      ">>>              valid accuracy = 88.92%, valid avgLoss = 0.3079\n",
      ">>> End epoch 42, train accuracy = 91.15%, train avgLoss = 0.2424\n",
      ">>>              valid accuracy = 87.23%, valid avgLoss = 0.3392\n",
      ">>> End epoch 43, train accuracy = 91.34%, train avgLoss = 0.2390\n",
      ">>>              valid accuracy = 89.02%, valid avgLoss = 0.3139\n",
      ">>> End epoch 44, train accuracy = 91.30%, train avgLoss = 0.2390\n",
      ">>>              valid accuracy = 89.17%, valid avgLoss = 0.3082\n",
      ">>> End epoch 45, train accuracy = 91.31%, train avgLoss = 0.2385\n",
      ">>>              valid accuracy = 89.02%, valid avgLoss = 0.3091\n",
      ">>> End epoch 46, train accuracy = 91.14%, train avgLoss = 0.2389\n",
      ">>>              valid accuracy = 89.05%, valid avgLoss = 0.3168\n",
      ">>> End epoch 47, train accuracy = 91.44%, train avgLoss = 0.2360\n",
      ">>>              valid accuracy = 88.92%, valid avgLoss = 0.3103\n",
      ">>> End epoch 48, train accuracy = 91.34%, train avgLoss = 0.2370\n",
      ">>>              valid accuracy = 86.80%, valid avgLoss = 0.3756\n",
      ">>> End epoch 49, train accuracy = 91.32%, train avgLoss = 0.2373\n",
      ">>>              valid accuracy = 87.79%, valid avgLoss = 0.3375\n",
      ">>> End epoch 50, train accuracy = 91.44%, train avgLoss = 0.2340\n",
      ">>>              valid accuracy = 89.14%, valid avgLoss = 0.3090\n",
      ">>> End epoch 51, train accuracy = 91.43%, train avgLoss = 0.2351\n",
      ">>>              valid accuracy = 88.08%, valid avgLoss = 0.3277\n",
      ">>> End epoch 52, train accuracy = 91.45%, train avgLoss = 0.2350\n",
      ">>>              valid accuracy = 87.80%, valid avgLoss = 0.3398\n",
      ">>> End epoch 53, train accuracy = 91.57%, train avgLoss = 0.2312\n",
      ">>>              valid accuracy = 88.70%, valid avgLoss = 0.3178\n",
      ">>> End epoch 54, train accuracy = 91.47%, train avgLoss = 0.2322\n",
      ">>>              valid accuracy = 88.81%, valid avgLoss = 0.3147\n",
      ">>> End epoch 55, train accuracy = 91.45%, train avgLoss = 0.2335\n",
      ">>>              valid accuracy = 88.58%, valid avgLoss = 0.3168\n",
      ">>> End epoch 56, train accuracy = 91.68%, train avgLoss = 0.2301\n",
      ">>>              valid accuracy = 88.34%, valid avgLoss = 0.3228\n",
      ">>> End epoch 57, train accuracy = 91.63%, train avgLoss = 0.2306\n",
      ">>>              valid accuracy = 87.83%, valid avgLoss = 0.3372\n",
      ">>> End epoch 58, train accuracy = 91.54%, train avgLoss = 0.2302\n",
      ">>>              valid accuracy = 88.89%, valid avgLoss = 0.3141\n",
      ">>> End epoch 59, train accuracy = 91.62%, train avgLoss = 0.2288\n",
      ">>>              valid accuracy = 87.84%, valid avgLoss = 0.3377\n",
      ">>> End epoch 60, train accuracy = 91.49%, train avgLoss = 0.2300\n",
      ">>>              valid accuracy = 88.99%, valid avgLoss = 0.3081\n"
     ]
    }
   ],
   "source": [
    "train_loss_L2 = []\n",
    "train_acc_L2 = []\n",
    "valid_loss_L2 = []\n",
    "valid_acc_L2 = []\n",
    "\n",
    "num_epochs = 60\n",
    "for epoch in range(0, num_epochs):\n",
    "    train_1_epoch(model_L2, optimizer_L2, train_loss_L2, train_acc_L2)\n",
    "    validation(model_L2, valid_loss_L2, valid_acc_L2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 321,
       "width": 893
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(15, 5))\n",
    "plt.rc('font', size=12)\n",
    "\n",
    "ax[0].plot(range(1,num_epochs+1)[::2], train_loss_ori[::2], 'o--', label='No regularization (train)', color='b')\n",
    "ax[0].plot(range(1,num_epochs+1)[::2], valid_loss_ori[::2], 'o--', label='No regularization (valid)', color='dodgerblue')\n",
    "ax[1].plot(range(1,num_epochs+1)[::2], train_acc_ori[::2] , 'o--', label='No regularization (train)', color='b')\n",
    "ax[1].plot(range(1,num_epochs+1)[::2], valid_acc_ori[::2], 'o--', label='No regularization (valid)', color='dodgerblue')\n",
    "\n",
    "ax[0].plot(range(1,num_epochs+1)[::2], train_loss_dropout[::2], '^--', label='dropout (train)', color='firebrick')\n",
    "ax[0].plot(range(1,num_epochs+1)[::2], valid_loss_dropout[::2], '^--', label='dropout (valid)', color='darkorange')\n",
    "ax[1].plot(range(1,num_epochs+1)[::2], train_acc_dropout[::2] , '^--', label='dropout (train)', color='firebrick')\n",
    "ax[1].plot(range(1,num_epochs+1)[::2], valid_acc_dropout[::2] , '^--', label='dropout (valid)', color='darkorange')\n",
    "\n",
    "ax[0].plot(range(1,num_epochs+1)[::2], train_loss_L2[::2], 's--', label='L2 regularization (train)', color='forestgreen')\n",
    "ax[0].plot(range(1,num_epochs+1)[::2], valid_loss_L2[::2], 's--', label='L2 regularization (valid)', color='yellowgreen')\n",
    "ax[1].plot(range(1,num_epochs+1)[::2], train_acc_L2[::2] , 's--', label='L2 regularization (train)', color='forestgreen')\n",
    "ax[1].plot(range(1,num_epochs+1)[::2], valid_acc_L2[::2], 's--', label='L2 regularization (valid)', color='yellowgreen')\n",
    "\n",
    "ax[0].set_ylabel('loss')\n",
    "ax[1].set_ylabel('accuracy')\n",
    "\n",
    "for i in range(2):\n",
    "    ax[i].set_xlabel('epoch')\n",
    "    ax[i].legend(loc='best')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ------------ End ------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}