3. SVHN_to_MNIST.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RTya_dtvpdaX"
   },
   "source": [
    "# Preparations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "e9W0cTFEPmAl"
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import os.path\n",
    "\n",
    "import numpy as np\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "import torchvision.transforms as transforms\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn import manifold\n",
    "from math import exp, sqrt\n",
    "from torch.autograd import Variable\n",
    "from my_dataset import MNIST_M\n",
    "from my_dataset import ST_Dataset\n",
    "\n",
    "%matplotlib inline\n",
    "%load_ext skip_kernel_extension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 363,
     "status": "ok",
     "timestamp": 1523506225267,
     "user": {
      "displayName": "Yuan Qi",
      "photoUrl": "//lh6.googleusercontent.com/--bd6SE8_hDo/AAAAAAAAAAI/AAAAAAAAAE0/J27oawL5omk/s50-c-k-no/photo.jpg",
      "userId": "112219197582513023329"
     },
     "user_tz": 420
    },
    "id": "4BrzNF_oQL77",
    "outputId": "fe8e2212-37c8-4027-d176-ef46991bcd01"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "use_gpu = True\n"
     ]
    }
   ],
   "source": [
    "use_gpu = torch.cuda.is_available()\n",
    "print(\"use_gpu = \" + str(use_gpu))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5AX_Msxj4we7"
   },
   "source": [
    "# Structure\n",
    "\n",
    "![SVHN-MNIST Structure](https://c1.staticflickr.com/1/945/41945025492_f9db7ac20a_o.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 211
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 310,
     "status": "ok",
     "timestamp": 1523506395172,
     "user": {
      "displayName": "Yuan Qi",
      "photoUrl": "//lh6.googleusercontent.com/--bd6SE8_hDo/AAAAAAAAAAI/AAAAAAAAAE0/J27oawL5omk/s50-c-k-no/photo.jpg",
      "userId": "112219197582513023329"
     },
     "user_tz": 420
    },
    "id": "d5glpPlhprD4",
    "outputId": "6e9189d3-996c-434c-985e-0e88793fe4eb",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "class CNN(nn.Module):\n",
    "    \n",
    "    def __init__(self):\n",
    "        super(CNN, self).__init__()\n",
    "        self.C1 = nn.Conv2d(3, 96, kernel_size=5, padding=2)\n",
    "        self.C2 = nn.Conv2d(96, 144, kernel_size=3, padding=1)\n",
    "        self.C3 = nn.Conv2d(144, 256, kernel_size=5, padding=2)\n",
    "        self.FC1 = nn.Linear(256 * 4 * 4, 512)\n",
    "        self.FC2 = nn.Linear(512, 10)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        # C1\n",
    "        x = F.relu(self.C1(x))\n",
    "        # M1\n",
    "        x = F.max_pool2d(x, (2, 2), stride=(2, 2))\n",
    "        # C2\n",
    "        x = F.relu(self.C2(x))\n",
    "        # M2\n",
    "        x = F.max_pool2d(x, (2, 2), stride=(2, 2))\n",
    "        # C3\n",
    "        x = F.relu(self.C3(x))\n",
    "        # M3\n",
    "        x = F.max_pool2d(x, (2, 2), stride=(2, 2))\n",
    "        # x's size is (128, 256, 4, 4)\n",
    "        # flatten\n",
    "        x = x.view(-1, 256 * 4 * 4)\n",
    "        f = x\n",
    "        # FC1\n",
    "        x = F.relu(self.FC1(x))\n",
    "        lh = x\n",
    "        # FC2\n",
    "        x = self.FC2(x)\n",
    "        return x, f, lh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CNN(\n",
      "  (C1): Conv2d(3, 96, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
      "  (C2): Conv2d(96, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (C3): Conv2d(144, 256, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
      "  (FC1): Linear(in_features=4096, out_features=512, bias=True)\n",
      "  (FC2): Linear(in_features=512, out_features=10, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "cnn_svhn = CNN()\n",
    "if (use_gpu):\n",
    "    cnn_svhn.cuda()\n",
    "print(cnn_svhn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def reset(model):\n",
    "    for m in model.modules():\n",
    "        if isinstance(m, nn.Conv2d):\n",
    "            n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\n",
    "            m.weight.data.normal_(0, sqrt(2 / n))\n",
    "            if m.bias is not None:\n",
    "                m.bias.data.zero_()\n",
    "        elif isinstance(m, nn.Linear):\n",
    "            size = m.weight.size()\n",
    "            fan_out = size[0] # number of rows\n",
    "            fan_in = size[1] # number of columns\n",
    "            m.weight.data.normal_(0, sqrt(2 / (fan_in + fan_out)))\n",
    "            m.bias.data.zero_()\n",
    "        elif hasattr(m, 'reset_parameters'):\n",
    "            m.reset_parameters()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "WaXlbBWN44Gd"
   },
   "source": [
    "# SVHN Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 123
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 30198,
     "status": "ok",
     "timestamp": 1523506265222,
     "user": {
      "displayName": "Yuan Qi",
      "photoUrl": "//lh6.googleusercontent.com/--bd6SE8_hDo/AAAAAAAAAAI/AAAAAAAAAE0/J27oawL5omk/s50-c-k-no/photo.jpg",
      "userId": "112219197582513023329"
     },
     "user_tz": 420
    },
    "id": "e5G7cSEk0V2S",
    "outputId": "621eee0f-8ee7-490e-db5f-f82317d18f8c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using downloaded and verified file: ./data/svhn/train_32x32.mat\n",
      "Using downloaded and verified file: ./data/svhn/test_32x32.mat\n"
     ]
    }
   ],
   "source": [
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "import os.path\n",
    "\n",
    "root_dir = \"./data/\"\n",
    "data_dir = \"svhn/\"\n",
    "\n",
    "transform = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n",
    "])\n",
    "\n",
    "batch_size = 128\n",
    "\n",
    "trainset_svhn = torchvision.datasets.SVHN(os.path.join(root_dir, data_dir),\n",
    "                                          split=\"train\", transform=transform, download=True)\n",
    "trainloader_svhn = torch.utils.data.DataLoader(trainset_svhn, batch_size=batch_size,\n",
    "                                          shuffle=False, num_workers=0)\n",
    "testset_svhn = torchvision.datasets.SVHN(os.path.join(root_dir, data_dir),\n",
    "                                          split=\"test\", transform=transform, download=True)\n",
    "testloader_svhn = torch.utils.data.DataLoader(testset_svhn, batch_size=batch_size,\n",
    "                                          shuffle=False, num_workers=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this function takes the numpy input image with dim 3 x height x width\n",
    "def convert_to_plt(input_image):\n",
    "    input_image = input_image*np.asarray([0.5,0.5,0.5]) + np.asarray([0.5,0.5,0.5])\n",
    "    return input_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 159
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 381,
     "status": "ok",
     "timestamp": 1523506265637,
     "user": {
      "displayName": "Yuan Qi",
      "photoUrl": "//lh6.googleusercontent.com/--bd6SE8_hDo/AAAAAAAAAAI/AAAAAAAAAE0/J27oawL5omk/s50-c-k-no/photo.jpg",
      "userId": "112219197582513023329"
     },
     "user_tz": 420
    },
    "id": "VTlEvJBY-ziX",
    "outputId": "54f6931a-ca67-452c-81fc-6c4cfae9e89a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The 10th image in the first 128 images in the training set:\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 72x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Its correspondent label:\n",
      "tensor(3)\n"
     ]
    }
   ],
   "source": [
    "# randomly plot a sample from training set\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "inputs = None\n",
    "labels = None\n",
    "for i, data in enumerate(trainloader_svhn):\n",
    "    inputs, labels = data\n",
    "    break\n",
    "\n",
    "idx = np.random.randint(0, batch_size)\n",
    "print(\"The \" + str(idx) + \"th image in the first \" + str(batch_size) +\\\n",
    "      \" images in the training set:\")\n",
    "\n",
    "plt_img = convert_to_plt(inputs[idx].permute(1, 2, 0).cpu().data.numpy())\n",
    "plt.figure(figsize=(1,1))\n",
    "plt.axis(\"off\")\n",
    "plt.imshow(plt_img)\n",
    "plt.show()\n",
    "print(\"Its correspondent label:\\n\" + str(labels[idx]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "pcSb16SIFgZt"
   },
   "source": [
    "## Training on MNIST\n",
    "\n",
    "Or you can load the parameters directly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load_model = False\n"
     ]
    }
   ],
   "source": [
    "import os.path\n",
    "para_file = \"./parameters/cnn3_svhn.pt\"\n",
    "if not os.path.exists('./parameters'):\n",
    "    os.makedirs('./parameters')\n",
    "load_model = os.path.isfile(para_file)\n",
    "print(\"load_model = \" + str(load_model))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%skip (not $load_model)\n",
    "cnn_svhn.load_state_dict(torch.load(para_file))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "YRHBSIQ2FkOL"
   },
   "outputs": [],
   "source": [
    "import torch.optim as optim\n",
    "\n",
    "lr_init = 0.01\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.SGD(cnn_svhn.parameters(), lr=lr_init, momentum=0.9)\n",
    "\n",
    "def adjust_lr(optimizer, p):\n",
    "    global lr_init\n",
    "    lr_0 = lr_init\n",
    "    alpha = 10\n",
    "    beta = 0.75\n",
    "    lr = lr_0 / (1 + alpha * p) ** beta\n",
    "    for param_group in optimizer.param_groups:\n",
    "        param_group['lr'] = lr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DgUSfM0KF4iT"
   },
   "source": [
    "### Training process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 10050
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 606703,
     "status": "ok",
     "timestamp": 1523144380194,
     "user": {
      "displayName": "Yuan Qi",
      "photoUrl": "//lh6.googleusercontent.com/--bd6SE8_hDo/AAAAAAAAAAI/AAAAAAAAAE0/J27oawL5omk/s50-c-k-no/photo.jpg",
      "userId": "112219197582513023329"
     },
     "user_tz": 420
    },
    "id": "IlstjDLbFkQ6",
    "outputId": "59a56d48-bdaa-4e0e-bc20-0feaecd74fa5",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 50] batch loss: 2.243\n",
      "[100] batch loss: 2.187\n",
      "[150] batch loss: 1.885\n",
      "[200] batch loss: 1.206\n",
      "[250] batch loss: 0.825\n",
      "[300] batch loss: 0.671\n",
      "[350] batch loss: 0.617\n",
      "[400] batch loss: 0.534\n",
      "[450] batch loss: 0.530\n",
      "[500] batch loss: 0.499\n",
      "[550] batch loss: 0.490\n",
      "epoch 1 loss: inf -> 595.178\n",
      "\n",
      "[ 50] batch loss: 0.431\n",
      "[100] batch loss: 0.424\n",
      "[150] batch loss: 0.409\n",
      "[200] batch loss: 0.433\n",
      "[250] batch loss: 0.391\n",
      "[300] batch loss: 0.370\n",
      "[350] batch loss: 0.357\n",
      "[400] batch loss: 0.335\n",
      "[450] batch loss: 0.340\n",
      "[500] batch loss: 0.327\n",
      "[550] batch loss: 0.336\n",
      "epoch 2 loss: 595.178 -> 215.407\n",
      "\n",
      "[ 50] batch loss: 0.317\n",
      "[100] batch loss: 0.313\n",
      "[150] batch loss: 0.299\n",
      "[200] batch loss: 0.334\n",
      "[250] batch loss: 0.292\n",
      "[300] batch loss: 0.274\n",
      "[350] batch loss: 0.276\n",
      "[400] batch loss: 0.253\n",
      "[450] batch loss: 0.265\n",
      "[500] batch loss: 0.246\n",
      "[550] batch loss: 0.260\n",
      "epoch 3 loss: 215.407 -> 162.362\n",
      "\n",
      "[ 50] batch loss: 0.254\n",
      "[100] batch loss: 0.251\n",
      "[150] batch loss: 0.243\n",
      "[200] batch loss: 0.276\n",
      "[250] batch loss: 0.239\n",
      "[300] batch loss: 0.220\n",
      "[350] batch loss: 0.222\n",
      "[400] batch loss: 0.203\n",
      "[450] batch loss: 0.218\n",
      "[500] batch loss: 0.199\n",
      "[550] batch loss: 0.209\n",
      "epoch 4 loss: 162.362 -> 131.179\n",
      "\n",
      "[ 50] batch loss: 0.212\n",
      "[100] batch loss: 0.207\n",
      "[150] batch loss: 0.205\n",
      "[200] batch loss: 0.234\n",
      "[250] batch loss: 0.201\n",
      "[300] batch loss: 0.182\n",
      "[350] batch loss: 0.182\n",
      "[400] batch loss: 0.166\n",
      "[450] batch loss: 0.182\n",
      "[500] batch loss: 0.162\n",
      "[550] batch loss: 0.169\n",
      "epoch 5 loss: 131.179 -> 108.736\n",
      "\n",
      "[ 50] batch loss: 0.176\n",
      "[100] batch loss: 0.173\n",
      "[150] batch loss: 0.174\n",
      "[200] batch loss: 0.200\n",
      "[250] batch loss: 0.172\n",
      "[300] batch loss: 0.152\n",
      "[350] batch loss: 0.152\n",
      "[400] batch loss: 0.137\n",
      "[450] batch loss: 0.152\n",
      "[500] batch loss: 0.132\n",
      "[550] batch loss: 0.140\n",
      "epoch 6 loss: 108.736 -> 90.924\n",
      "\n",
      "[ 50] batch loss: 0.146\n",
      "[100] batch loss: 0.145\n",
      "[150] batch loss: 0.148\n",
      "[200] batch loss: 0.171\n",
      "[250] batch loss: 0.145\n",
      "[300] batch loss: 0.126\n",
      "[350] batch loss: 0.125\n",
      "[400] batch loss: 0.113\n",
      "[450] batch loss: 0.129\n",
      "[500] batch loss: 0.110\n",
      "[550] batch loss: 0.118\n",
      "epoch 7 loss: 90.924 -> 76.270\n",
      "\n",
      "[ 50] batch loss: 0.124\n",
      "[100] batch loss: 0.121\n",
      "[150] batch loss: 0.125\n",
      "[200] batch loss: 0.145\n",
      "[250] batch loss: 0.120\n",
      "[300] batch loss: 0.104\n",
      "[350] batch loss: 0.103\n",
      "[400] batch loss: 0.093\n",
      "[450] batch loss: 0.109\n",
      "[500] batch loss: 0.091\n",
      "[550] batch loss: 0.100\n",
      "epoch 8 loss: 76.270 -> 63.833\n",
      "\n",
      "[ 50] batch loss: 0.106\n",
      "[100] batch loss: 0.103\n",
      "[150] batch loss: 0.103\n",
      "[200] batch loss: 0.121\n",
      "[250] batch loss: 0.099\n",
      "[300] batch loss: 0.087\n",
      "[350] batch loss: 0.087\n",
      "[400] batch loss: 0.077\n",
      "[450] batch loss: 0.092\n",
      "[500] batch loss: 0.076\n",
      "[550] batch loss: 0.084\n",
      "epoch 9 loss: 63.833 -> 53.548\n",
      "\n",
      "[ 50] batch loss: 0.088\n",
      "[100] batch loss: 0.088\n",
      "[150] batch loss: 0.083\n",
      "[200] batch loss: 0.100\n",
      "[250] batch loss: 0.082\n",
      "[300] batch loss: 0.074\n",
      "[350] batch loss: 0.071\n",
      "[400] batch loss: 0.063\n",
      "[450] batch loss: 0.076\n",
      "[500] batch loss: 0.062\n",
      "[550] batch loss: 0.068\n",
      "epoch 10 loss: 53.548 -> 44.159\n",
      "\n",
      "[ 50] batch loss: 0.071\n",
      "[100] batch loss: 0.074\n",
      "[150] batch loss: 0.067\n",
      "[200] batch loss: 0.080\n",
      "[250] batch loss: 0.066\n",
      "[300] batch loss: 0.063\n",
      "[350] batch loss: 0.059\n",
      "[400] batch loss: 0.052\n",
      "[450] batch loss: 0.063\n",
      "[500] batch loss: 0.050\n",
      "[550] batch loss: 0.056\n",
      "epoch 11 loss: 44.159 -> 36.181\n",
      "\n",
      "[ 50] batch loss: 0.055\n",
      "[100] batch loss: 0.064\n",
      "[150] batch loss: 0.055\n",
      "[200] batch loss: 0.064\n",
      "[250] batch loss: 0.052\n",
      "[300] batch loss: 0.053\n",
      "[350] batch loss: 0.048\n",
      "[400] batch loss: 0.043\n",
      "[450] batch loss: 0.051\n",
      "[500] batch loss: 0.041\n",
      "[550] batch loss: 0.046\n",
      "epoch 12 loss: 36.181 -> 29.633\n",
      "\n",
      "[ 50] batch loss: 0.044\n",
      "[100] batch loss: 0.054\n",
      "[150] batch loss: 0.047\n",
      "[200] batch loss: 0.052\n",
      "[250] batch loss: 0.042\n",
      "[300] batch loss: 0.045\n",
      "[350] batch loss: 0.039\n",
      "[400] batch loss: 0.035\n",
      "[450] batch loss: 0.043\n",
      "[500] batch loss: 0.034\n",
      "[550] batch loss: 0.037\n",
      "epoch 13 loss: 29.633 -> 24.316\n",
      "\n",
      "[ 50] batch loss: 0.035\n",
      "[100] batch loss: 0.045\n",
      "[150] batch loss: 0.039\n",
      "[200] batch loss: 0.042\n",
      "[250] batch loss: 0.033\n",
      "[300] batch loss: 0.036\n",
      "[350] batch loss: 0.032\n",
      "[400] batch loss: 0.027\n",
      "[450] batch loss: 0.036\n",
      "[500] batch loss: 0.028\n",
      "[550] batch loss: 0.029\n",
      "epoch 14 loss: 24.316 -> 19.820\n",
      "\n",
      "[ 50] batch loss: 0.030\n",
      "[100] batch loss: 0.038\n",
      "[150] batch loss: 0.032\n",
      "[200] batch loss: 0.034\n",
      "[250] batch loss: 0.027\n",
      "[300] batch loss: 0.029\n",
      "[350] batch loss: 0.026\n",
      "[400] batch loss: 0.021\n",
      "[450] batch loss: 0.029\n",
      "[500] batch loss: 0.025\n",
      "[550] batch loss: 0.024\n",
      "epoch 15 loss: 19.820 -> 16.312\n",
      "\n",
      "[ 50] batch loss: 0.026\n",
      "[100] batch loss: 0.031\n",
      "[150] batch loss: 0.025\n",
      "[200] batch loss: 0.027\n",
      "[250] batch loss: 0.022\n",
      "[300] batch loss: 0.023\n",
      "[350] batch loss: 0.021\n",
      "[400] batch loss: 0.018\n",
      "[450] batch loss: 0.021\n",
      "[500] batch loss: 0.021\n",
      "[550] batch loss: 0.020\n",
      "epoch 16 loss: 16.312 -> 13.172\n",
      "\n",
      "[ 50] batch loss: 0.021\n",
      "[100] batch loss: 0.025\n",
      "[150] batch loss: 0.022\n",
      "[200] batch loss: 0.021\n",
      "[250] batch loss: 0.018\n",
      "[300] batch loss: 0.018\n",
      "[350] batch loss: 0.018\n",
      "[400] batch loss: 0.015\n",
      "[450] batch loss: 0.016\n",
      "[500] batch loss: 0.017\n",
      "[550] batch loss: 0.017\n",
      "epoch 17 loss: 13.172 -> 10.758\n",
      "\n",
      "[ 50] batch loss: 0.018\n",
      "[100] batch loss: 0.022\n",
      "[150] batch loss: 0.018\n",
      "[200] batch loss: 0.016\n",
      "[250] batch loss: 0.014\n",
      "[300] batch loss: 0.015\n",
      "[350] batch loss: 0.015\n",
      "[400] batch loss: 0.013\n",
      "[450] batch loss: 0.012\n",
      "[500] batch loss: 0.013\n",
      "[550] batch loss: 0.014\n",
      "epoch 18 loss: 10.758 -> 8.815\n",
      "\n",
      "[ 50] batch loss: 0.014\n",
      "[100] batch loss: 0.019\n",
      "[150] batch loss: 0.015\n",
      "[200] batch loss: 0.014\n",
      "[250] batch loss: 0.012\n",
      "[300] batch loss: 0.012\n",
      "[350] batch loss: 0.012\n",
      "[400] batch loss: 0.011\n",
      "[450] batch loss: 0.010\n",
      "[500] batch loss: 0.011\n",
      "[550] batch loss: 0.012\n",
      "epoch 19 loss: 8.815 -> 7.284\n",
      "\n",
      "[ 50] batch loss: 0.010\n",
      "[100] batch loss: 0.015\n",
      "[150] batch loss: 0.013\n",
      "[200] batch loss: 0.011\n",
      "[250] batch loss: 0.010\n",
      "[300] batch loss: 0.010\n",
      "[350] batch loss: 0.010\n",
      "[400] batch loss: 0.009\n",
      "[450] batch loss: 0.008\n",
      "[500] batch loss: 0.008\n",
      "[550] batch loss: 0.010\n",
      "epoch 20 loss: 7.284 -> 5.935\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%skip $load_model\n",
    "\n",
    "prev_loss = np.float(\"inf\")\n",
    "total_epoch = 20\n",
    "reset(cnn_svhn)\n",
    "\n",
    "for epoch in range(total_epoch):\n",
    "    epoch_loss = 0.0\n",
    "    running_loss = 0.0\n",
    "    p = epoch * 1.0 / total_epoch\n",
    "    adjust_lr(optimizer, p)\n",
    "    for i, data in enumerate(trainloader_svhn):\n",
    "        inputs, labels = data\n",
    "        if (use_gpu):\n",
    "            inputs, labels = inputs.cuda(), labels.cuda()\n",
    "        inputs, labels = Variable(inputs), Variable(labels)\n",
    "        optimizer.zero_grad()\n",
    "        outputs, _, _ = cnn_svhn(inputs)\n",
    "        loss = criterion(outputs, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        epoch_loss += loss.item()\n",
    "        running_loss += loss.item()\n",
    "        if i % 50 == 49:    # print every 50 mini-batches\n",
    "            print('[%3d] batch loss: %.3f' %\n",
    "                  (i + 1, running_loss / 50))\n",
    "            running_loss = 0.0\n",
    "    print(\"epoch %d loss: %.3f -> %.3f\\n\" % (epoch + 1, prev_loss, epoch_loss))\n",
    "    if prev_loss - epoch_loss < 0.1:\n",
    "        break\n",
    "    else:\n",
    "        prev_loss = epoch_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%skip $load_model\n",
    "\n",
    "torch.save(cnn_svhn.state_dict(), para_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5oBeLwnRLrcY"
   },
   "source": [
    "## Testing on SVHN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_accuracy(model, dataloader):\n",
    "    model.eval()\n",
    "    correct = 0\n",
    "    total = 0\n",
    "    for i, data in enumerate(dataloader):\n",
    "        inputs, labels = data\n",
    "        if (use_gpu):\n",
    "            inputs, labels = inputs.cuda(), labels.cuda()\n",
    "        with torch.no_grad():\n",
    "            #inputs, labels = Variable(inputs, volatile=True), Variable(labels, volatile=True)\n",
    "            inputs, labels = Variable(inputs), Variable(labels)\n",
    "            outputs, _, _ = model(inputs)\n",
    "            correct += (torch.max(outputs.data, 1)[1] == labels.data).sum().item()\n",
    "            total += labels.size()[0]\n",
    "    acc = correct * 1.0 / total\n",
    "    return acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1632,
     "status": "ok",
     "timestamp": 1523144440407,
     "user": {
      "displayName": "Yuan Qi",
      "photoUrl": "//lh6.googleusercontent.com/--bd6SE8_hDo/AAAAAAAAAAI/AAAAAAAAAE0/J27oawL5omk/s50-c-k-no/photo.jpg",
      "userId": "112219197582513023329"
     },
     "user_tz": 420
    },
    "id": "Rn4sOe0iLuKI",
    "outputId": "f31ce94e-ef18-4564-9772-a3a9b22c1272"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on MNIST test set (source only): 0.9085356484326982\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy on SVHN test set (source only): \" + str(evaluate_accuracy(cnn_svhn, testloader_svhn)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MNIST Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 128\n",
    "root_dir = \"./data/mnist/\"\n",
    "\n",
    "transform = transforms.Compose([\n",
    "    transforms.Resize((32, 32)),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.5, ), (0.5, )),\n",
    "    #transforms.Normalize((0.13066047712053577, ), (1, )),\n",
    "    transforms.Lambda(lambda x: torch.cat((x, x, x), dim=0))\n",
    "])\n",
    "\n",
    "trainset = torchvision.datasets.MNIST(root_dir, train=True, transform=transform,\n",
    "                                      target_transform=None, download=True)\n",
    "trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,\n",
    "                                          shuffle=False, num_workers=0)\n",
    "testset = torchvision.datasets.MNIST(root_dir, train=False, transform=transform,\n",
    "                                      target_transform=None, download=True)\n",
    "testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,\n",
    "                                          shuffle=False, num_workers=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The 17th image in the first 128 images in the training set:\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 72x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Its correspondent label:\n",
      "tensor(8)\n"
     ]
    }
   ],
   "source": [
    "# randomly plot a sample from training set\n",
    "\n",
    "inputs = None\n",
    "labels = None\n",
    "for i, data in enumerate(trainloader):\n",
    "    inputs, labels = data\n",
    "    break\n",
    "    \n",
    "idx = np.random.randint(0, batch_size)\n",
    "print(\"The \" + str(idx) + \"th image in the first \" + str(batch_size) +\\\n",
    "      \" images in the training set:\")\n",
    "\n",
    "plt_img = convert_to_plt(inputs[idx].permute(1, 2, 0).cpu().data.numpy())\n",
    "plt.figure(figsize=(1,1))\n",
    "plt.axis(\"off\")\n",
    "plt.imshow(plt_img)\n",
    "plt.show()\n",
    "print(\"Its correspondent label:\\n\" + str(labels[idx]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing on MNIST-M Dataset (trained on source only)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on MNIST-M test set (source only): 0.6043\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy on MNIST-M test set (source only): \" + str(evaluate_accuracy(cnn_svhn, testloader)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import manifold\n",
    "\n",
    "def extract_features(model, dataloader):\n",
    "    model.eval()\n",
    "    features = None\n",
    "    lasthidden = None\n",
    "    labels = None\n",
    "    for i, data in enumerate(dataloader):\n",
    "        inputs, labels_ = data\n",
    "        if (use_gpu):\n",
    "            inputs = inputs.cuda()\n",
    "        with torch.no_grad():\n",
    "            inputs = Variable(inputs, requires_grad=False)\n",
    "            _, f, l = model(inputs)\n",
    "            if i == 0:\n",
    "                features = f\n",
    "                lasthidden = l\n",
    "                labels = labels_\n",
    "            else:\n",
    "                features = torch.cat((features, f))\n",
    "                lasthidden = torch.cat((lasthidden, l))\n",
    "                labels = torch.cat((labels, labels_))\n",
    "    features = features.data\n",
    "    lasthidden = lasthidden.data\n",
    "    if use_gpu:\n",
    "        features = features.cpu()\n",
    "        lasthidden = lasthidden.cpu()\n",
    "    return features, lasthidden, labels\n",
    "\n",
    "def visualize_single_dataset(data, labels, perplexity=50, sample_num=None):\n",
    "    total_num = labels.shape[0]\n",
    "    if sample_num:\n",
    "        idx = np.random.choice(total_num, sample_num, replace=False)\n",
    "        data, labels = data[idx, :], labels[idx]\n",
    "        total_num = sample_num\n",
    "    tsne = manifold.TSNE(n_components=2, init='random',\n",
    "                     random_state=0, perplexity=perplexity)\n",
    "    X = tsne.fit_transform(data)\n",
    "    colors = [\"red\", \"orange\", \"goldenrod\", \"yellow\", \"yellowgreen\", \"green\", \"teal\", \"blue\", \"violet\", \"purple\"]\n",
    "    for i in range(10):\n",
    "        plt.scatter(X[labels == i, 0], X[labels == i, 1], c=colors[i], alpha=0.4)\n",
    "    plt.axis(\"off\")\n",
    "    \n",
    "def visualize_da(source, target, perplexity=50, sample_num=None, save=None):\n",
    "    source_num = source.shape[0]\n",
    "    target_num = target.shape[0]\n",
    "    if sample_num:\n",
    "        source, target = source[:sample_num, :], target[:sample_num, :]\n",
    "        \n",
    "    data = np.vstack((source, target))\n",
    "\n",
    "    tsne = manifold.TSNE(n_components=2, init='random',\n",
    "                         random_state=0, perplexity=perplexity)\n",
    "    X = tsne.fit_transform(data)\n",
    "    plt.scatter(X[:sample_num, 0], X[:sample_num, 1], c=\"blue\", edgecolors=None, alpha=0.4)\n",
    "    plt.scatter(X[sample_num:, 0], X[sample_num:, 1], c=\"red\", edgecolors=None, alpha=0.4)\n",
    "    plt.axis(\"off\")\n",
    "    if save:\n",
    "        plt.savefig(save)\n",
    "    plt.show()\n",
    "    plt.clf()\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "f_svhn, lh_svhn, l_svhn = extract_features(cnn_svhn, testloader_svhn)\n",
    "f, lh, l = extract_features(cnn_svhn, testloader)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Features of SVHN test set (extracted by source only model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_single_dataset(f_svhn.numpy(), l_svhn.numpy(), perplexity=50, sample_num=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Features of MNIST test set (extracted by source only model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_single_dataset(f.numpy(), l.numpy(), perplexity=50, sample_num=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Last hidden layer of SVHN test set (extracted by source only model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_da(f_svhn.numpy(), f.numpy(), perplexity=50, sample_num=500,\n",
    "             save=\"./pics/SVHN_to_MNIST_features_NonAdapted.svg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Last hidden layer of MNIST test set (extracted by source only model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_da(lh_svhn.numpy(), lh.numpy(), perplexity=50, sample_num=500,\n",
    "             save=\"./pics/SVHN_to_MNIST_lasthidden_NonAdapted.svg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training on MNIST Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CNN(\n",
      "  (C1): Conv2d(3, 96, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
      "  (C2): Conv2d(96, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (C3): Conv2d(144, 256, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
      "  (FC1): Linear(in_features=4096, out_features=512, bias=True)\n",
      "  (FC2): Linear(in_features=512, out_features=10, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "cnn = CNN()\n",
    "if (use_gpu):\n",
    "    cnn.cuda()\n",
    "print(cnn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load_model = True\n"
     ]
    }
   ],
   "source": [
    "para_file = \"./parameters/cnn3_mnist.pt\"\n",
    "load_model = os.path.isfile(para_file)\n",
    "print(\"load_model = \" + str(load_model))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%skip (not $load_model)\n",
    "cnn.load_state_dict(torch.load(para_file))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.optim as optim\n",
    "\n",
    "lr_init = 0.01\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.SGD(cnn.parameters(), lr=lr_init, momentum=0.9)\n",
    "\n",
    "def adjust_lr(optimizer, p):\n",
    "    global lr_init\n",
    "    lr_0 = lr_init\n",
    "    alpha = 10\n",
    "    beta = 0.75\n",
    "    lr = lr_0 / (1 + alpha * p) ** beta\n",
    "    for param_group in optimizer.param_groups:\n",
    "        param_group['lr'] = lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 50] batch loss: 2.179\n",
      "[100] batch loss: 0.737\n",
      "[150] batch loss: 0.230\n",
      "[200] batch loss: 0.124\n",
      "[250] batch loss: 0.124\n",
      "[300] batch loss: 0.101\n",
      "[350] batch loss: 0.105\n",
      "[400] batch loss: 0.107\n",
      "[450] batch loss: 0.064\n",
      "epoch 1 loss: inf -> 189.650\n",
      "\n",
      "[ 50] batch loss: 0.066\n",
      "[100] batch loss: 0.066\n",
      "[150] batch loss: 0.055\n",
      "[200] batch loss: 0.041\n",
      "[250] batch loss: 0.048\n",
      "[300] batch loss: 0.051\n",
      "[350] batch loss: 0.042\n",
      "[400] batch loss: 0.050\n",
      "[450] batch loss: 0.037\n",
      "epoch 2 loss: 189.650 -> 23.489\n",
      "\n",
      "[ 50] batch loss: 0.043\n",
      "[100] batch loss: 0.045\n",
      "[150] batch loss: 0.036\n",
      "[200] batch loss: 0.027\n",
      "[250] batch loss: 0.032\n",
      "[300] batch loss: 0.036\n",
      "[350] batch loss: 0.028\n",
      "[400] batch loss: 0.033\n",
      "[450] batch loss: 0.027\n",
      "epoch 3 loss: 23.489 -> 15.893\n",
      "\n",
      "[ 50] batch loss: 0.032\n",
      "[100] batch loss: 0.029\n",
      "[150] batch loss: 0.022\n",
      "[200] batch loss: 0.019\n",
      "[250] batch loss: 0.024\n",
      "[300] batch loss: 0.027\n",
      "[350] batch loss: 0.022\n",
      "[400] batch loss: 0.024\n",
      "[450] batch loss: 0.021\n",
      "epoch 4 loss: 15.893 -> 11.613\n",
      "\n",
      "[ 50] batch loss: 0.024\n",
      "[100] batch loss: 0.021\n",
      "[150] batch loss: 0.016\n",
      "[200] batch loss: 0.014\n",
      "[250] batch loss: 0.019\n",
      "[300] batch loss: 0.022\n",
      "[350] batch loss: 0.018\n",
      "[400] batch loss: 0.020\n",
      "[450] batch loss: 0.016\n",
      "epoch 5 loss: 11.613 -> 9.039\n",
      "\n",
      "[ 50] batch loss: 0.018\n",
      "[100] batch loss: 0.016\n",
      "[150] batch loss: 0.013\n",
      "[200] batch loss: 0.011\n",
      "[250] batch loss: 0.015\n",
      "[300] batch loss: 0.018\n",
      "[350] batch loss: 0.015\n",
      "[400] batch loss: 0.016\n",
      "[450] batch loss: 0.013\n",
      "epoch 6 loss: 9.039 -> 7.119\n",
      "\n",
      "[ 50] batch loss: 0.014\n",
      "[100] batch loss: 0.013\n",
      "[150] batch loss: 0.010\n",
      "[200] batch loss: 0.009\n",
      "[250] batch loss: 0.012\n",
      "[300] batch loss: 0.015\n",
      "[350] batch loss: 0.012\n",
      "[400] batch loss: 0.012\n",
      "[450] batch loss: 0.011\n",
      "epoch 7 loss: 7.119 -> 5.709\n",
      "\n",
      "[ 50] batch loss: 0.012\n",
      "[100] batch loss: 0.010\n",
      "[150] batch loss: 0.007\n",
      "[200] batch loss: 0.007\n",
      "[250] batch loss: 0.010\n",
      "[300] batch loss: 0.012\n",
      "[350] batch loss: 0.010\n",
      "[400] batch loss: 0.010\n",
      "[450] batch loss: 0.009\n",
      "epoch 8 loss: 5.709 -> 4.632\n",
      "\n",
      "[ 50] batch loss: 0.010\n",
      "[100] batch loss: 0.009\n",
      "[150] batch loss: 0.006\n",
      "[200] batch loss: 0.006\n",
      "[250] batch loss: 0.008\n",
      "[300] batch loss: 0.010\n",
      "[350] batch loss: 0.008\n",
      "[400] batch loss: 0.007\n",
      "[450] batch loss: 0.008\n",
      "epoch 9 loss: 4.632 -> 3.797\n",
      "\n",
      "[ 50] batch loss: 0.008\n",
      "[100] batch loss: 0.008\n",
      "[150] batch loss: 0.004\n",
      "[200] batch loss: 0.005\n",
      "[250] batch loss: 0.007\n",
      "[300] batch loss: 0.008\n",
      "[350] batch loss: 0.007\n",
      "[400] batch loss: 0.006\n",
      "[450] batch loss: 0.006\n",
      "epoch 10 loss: 3.797 -> 3.158\n",
      "\n",
      "[ 50] batch loss: 0.006\n",
      "[100] batch loss: 0.007\n",
      "[150] batch loss: 0.003\n",
      "[200] batch loss: 0.004\n",
      "[250] batch loss: 0.006\n",
      "[300] batch loss: 0.007\n",
      "[350] batch loss: 0.006\n",
      "[400] batch loss: 0.005\n",
      "[450] batch loss: 0.005\n",
      "epoch 11 loss: 3.158 -> 2.641\n",
      "\n",
      "[ 50] batch loss: 0.005\n",
      "[100] batch loss: 0.006\n",
      "[150] batch loss: 0.003\n",
      "[200] batch loss: 0.003\n",
      "[250] batch loss: 0.005\n",
      "[300] batch loss: 0.006\n",
      "[350] batch loss: 0.005\n",
      "[400] batch loss: 0.004\n",
      "[450] batch loss: 0.005\n",
      "epoch 12 loss: 2.641 -> 2.238\n",
      "\n",
      "[ 50] batch loss: 0.004\n",
      "[100] batch loss: 0.005\n",
      "[150] batch loss: 0.002\n",
      "[200] batch loss: 0.003\n",
      "[250] batch loss: 0.004\n",
      "[300] batch loss: 0.005\n",
      "[350] batch loss: 0.004\n",
      "[400] batch loss: 0.004\n",
      "[450] batch loss: 0.004\n",
      "epoch 13 loss: 2.238 -> 1.911\n",
      "\n",
      "[ 50] batch loss: 0.004\n",
      "[100] batch loss: 0.004\n",
      "[150] batch loss: 0.002\n",
      "[200] batch loss: 0.002\n",
      "[250] batch loss: 0.004\n",
      "[300] batch loss: 0.004\n",
      "[350] batch loss: 0.003\n",
      "[400] batch loss: 0.003\n",
      "[450] batch loss: 0.003\n",
      "epoch 14 loss: 1.911 -> 1.640\n",
      "\n",
      "[ 50] batch loss: 0.003\n",
      "[100] batch loss: 0.003\n",
      "[150] batch loss: 0.002\n",
      "[200] batch loss: 0.002\n",
      "[250] batch loss: 0.003\n",
      "[300] batch loss: 0.003\n",
      "[350] batch loss: 0.003\n",
      "[400] batch loss: 0.003\n",
      "[450] batch loss: 0.003\n",
      "epoch 15 loss: 1.640 -> 1.420\n",
      "\n",
      "[ 50] batch loss: 0.003\n",
      "[100] batch loss: 0.003\n",
      "[150] batch loss: 0.002\n",
      "[200] batch loss: 0.002\n",
      "[250] batch loss: 0.003\n",
      "[300] batch loss: 0.003\n",
      "[350] batch loss: 0.003\n",
      "[400] batch loss: 0.003\n",
      "[450] batch loss: 0.002\n",
      "epoch 16 loss: 1.420 -> 1.224\n",
      "\n",
      "[ 50] batch loss: 0.002\n",
      "[100] batch loss: 0.002\n",
      "[150] batch loss: 0.002\n",
      "[200] batch loss: 0.002\n",
      "[250] batch loss: 0.003\n",
      "[300] batch loss: 0.002\n",
      "[350] batch loss: 0.002\n",
      "[400] batch loss: 0.002\n",
      "[450] batch loss: 0.002\n",
      "epoch 17 loss: 1.224 -> 1.061\n",
      "\n",
      "[ 50] batch loss: 0.002\n",
      "[100] batch loss: 0.002\n",
      "[150] batch loss: 0.001\n",
      "[200] batch loss: 0.001\n",
      "[250] batch loss: 0.002\n",
      "[300] batch loss: 0.002\n",
      "[350] batch loss: 0.002\n",
      "[400] batch loss: 0.002\n",
      "[450] batch loss: 0.002\n",
      "epoch 18 loss: 1.061 -> 0.931\n",
      "\n",
      "[ 50] batch loss: 0.002\n",
      "[100] batch loss: 0.001\n",
      "[150] batch loss: 0.001\n",
      "[200] batch loss: 0.001\n",
      "[250] batch loss: 0.002\n",
      "[300] batch loss: 0.002\n",
      "[350] batch loss: 0.002\n",
      "[400] batch loss: 0.002\n",
      "[450] batch loss: 0.002\n",
      "epoch 19 loss: 0.931 -> 0.825\n",
      "\n",
      "[ 50] batch loss: 0.001\n",
      "[100] batch loss: 0.001\n",
      "[150] batch loss: 0.001\n",
      "[200] batch loss: 0.001\n",
      "[250] batch loss: 0.002\n",
      "[300] batch loss: 0.002\n",
      "[350] batch loss: 0.002\n",
      "[400] batch loss: 0.002\n",
      "[450] batch loss: 0.002\n",
      "epoch 20 loss: 0.825 -> 0.735\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%skip $load_model\n",
    "\n",
    "prev_loss = np.float(\"inf\")\n",
    "total_epoch = 50\n",
    "\n",
    "for epoch in range(total_epoch):\n",
    "    epoch_loss = 0.0\n",
    "    running_loss = 0.0\n",
    "    p = epoch * 1.0 / total_epoch\n",
    "    adjust_lr(optimizer, p)\n",
    "    for i, data in enumerate(trainloader):\n",
    "        inputs, labels = data\n",
    "        if (use_gpu):\n",
    "            inputs, labels = inputs.cuda(), labels.cuda()\n",
    "        \n",
    "        \n",
    "        inputs, labels = Variable(inputs), Variable(labels)\n",
    "        optimizer.zero_grad()\n",
    "        outputs, _, _ = cnn(inputs)\n",
    "        loss = criterion(outputs, labels)\n",
    "        \n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        epoch_loss += loss.item()\n",
    "        running_loss += loss.item()\n",
    "        if i % 50 == 49:    # print every 50 mini-batches\n",
    "            print('[%3d] batch loss: %.3f' %\n",
    "                  (i + 1, running_loss / 50))\n",
    "            running_loss = 0.0\n",
    "    print(\"epoch %d loss: %.3f -> %.3f\\n\" % (epoch + 1, prev_loss, epoch_loss))\n",
    "    if prev_loss - epoch_loss < 0.1:\n",
    "        prev_loss = epoch_loss\n",
    "        break\n",
    "    else:\n",
    "        prev_loss = epoch_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%skip $load_model\n",
    "\n",
    "torch.save(cnn.state_dict(), para_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on MNIST-M test set (train on target): 0.9929\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy on MNIST-M test set (train on target): \" + str(evaluate_accuracy(cnn, testloader)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Unsupervised Domain Adaptation\n",
    "\n",
    "## Structure (GRL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from my_dataset import ST_Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 128\n",
    "\n",
    "trainset_da = ST_Dataset(trainset_svhn, trainset, batch_size)\n",
    "trainloader_da = torch.utils.data.DataLoader(trainset_da, batch_size=batch_size,\n",
    "                                          shuffle=False, num_workers=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The 48th image in the first 128 images in the training set:\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 72x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Its correspondent label:\n",
      "2\n",
      "\n",
      "From domain:\n",
      "Source\n"
     ]
    }
   ],
   "source": [
    "# randomly plot a sample from test set\n",
    "\n",
    "inputs = None\n",
    "labels = None\n",
    "for i, data in enumerate(trainloader_da):\n",
    "    inputs, labels, domain = data\n",
    "    if i == 2:\n",
    "        break\n",
    "\n",
    "idx = np.random.randint(0, batch_size)\n",
    "print(\"The \" + str(idx) + \"th image in the first \" + str(batch_size) +\\\n",
    "      \" images in the training set:\")\n",
    "plt_img = convert_to_plt(inputs[idx].permute(1, 2, 0).cpu().data.numpy())\n",
    "plt.figure(figsize=(1,1))\n",
    "plt.axis(\"off\")\n",
    "plt.imshow(plt_img)\n",
    "plt.show()\n",
    "print(\"Its correspondent label:\")\n",
    "if labels[idx].item() == -1:\n",
    "    print(\"I don't know :)\\n\")\n",
    "else:\n",
    "    print(str(labels[idx].item()) + \"\\n\")\n",
    "\n",
    "print(\"From domain:\")\n",
    "if domain[idx].item() == 0:\n",
    "    print(\"Source\")\n",
    "else:\n",
    "    print(\"Target\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "class GRL_func(torch.autograd.Function):\n",
    "\n",
    "    @staticmethod\n",
    "    def forward(ctx, inputs, lamda):\n",
    "        ctx.save_for_backward(lamda)\n",
    "        return inputs\n",
    "\n",
    "    @staticmethod\n",
    "    def backward(ctx, grad_outputs):\n",
    "        lamda, = ctx.saved_tensors\n",
    "        return -lamda * grad_outputs, None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "class GRL(nn.Module):\n",
    "    \n",
    "    def __init__(self, lamda_init):\n",
    "        super(GRL, self).__init__()\n",
    "        self.GRL_func = GRL_func.apply\n",
    "        self.lamda = nn.Parameter(torch.Tensor(1), requires_grad=False)\n",
    "        self.set_lamda(lamda_init)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        return self.GRL_func(x, self.lamda)\n",
    "    \n",
    "    def set_lamda(self, lamda_new):\n",
    "        self.lamda[0] = lamda_new"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![SVHN-MNIST Structure](https://c1.staticflickr.com/1/945/41945025492_f9db7ac20a_o.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CNN_DA(nn.Module):\n",
    "    \n",
    "    def __init__(self, lamda_init=0):\n",
    "        super(CNN_DA, self).__init__()\n",
    "        # lamda\n",
    "        self.lamda = lamda_init\n",
    "        # feature extractor\n",
    "        self.C1 = nn.Conv2d(3, 96, kernel_size=5, padding=2)\n",
    "        self.C2 = nn.Conv2d(96, 144, kernel_size=5, padding=2)\n",
    "        self.C3 = nn.Conv2d(144, 256, kernel_size=5, padding=2)\n",
    "        # label classifier\n",
    "        self.LC_FC1 = nn.Linear(256 * 4 * 4, 512)\n",
    "        self.LC_FC2 = nn.Linear(512, 10)\n",
    "        # domain classifier\n",
    "        self.GRL_layer = GRL(lamda_init)\n",
    "        self.DC_FC1 = nn.Linear(256 * 4 * 4, 1024)\n",
    "        self.DC_FC2 = nn.Linear(1024, 1024)\n",
    "        self.DC_FC3 = nn.Linear(1024, 1)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        # C1\n",
    "        x = F.relu(self.C1(x))\n",
    "        # M1\n",
    "        x = F.max_pool2d(x, (2, 2), stride=(2, 2))\n",
    "        # C2\n",
    "        x = F.relu(self.C2(x))\n",
    "        # M2\n",
    "        x = F.max_pool2d(x, (2, 2), stride=(2, 2))\n",
    "        # C3\n",
    "        x = F.relu(self.C3(x))\n",
    "        # M3\n",
    "        x = F.max_pool2d(x, (2, 2), stride=(2, 2))\n",
    "        # x's size is (128, 256, 4, 4)\n",
    "        # flatten\n",
    "        x = x.view(-1, 256 * 4 * 4)\n",
    "        f = x\n",
    "        # label classifier\n",
    "        # LC_FC1\n",
    "        x_l = F.relu(self.LC_FC1(x))\n",
    "        lh = x_l\n",
    "        # LC_FC2\n",
    "        x_l = self.LC_FC2(x_l)\n",
    "        # domain classifier\n",
    "        # GRL\n",
    "        x_d = self.GRL_layer(x)\n",
    "        # DC_FC1\n",
    "        x_d = F.relu(self.DC_FC1(x_d))\n",
    "        # DC_FC2\n",
    "        x_d = F.relu(self.DC_FC2(x_d))\n",
    "        # DC_FC3\n",
    "        x_d = F.sigmoid(self.DC_FC3(x_d))\n",
    "        return x_l, x_d, f, lh\n",
    "    \n",
    "    def set_lamda(self, lamda_new):\n",
    "        self.GRL_layer.set_lamda(lamda_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_features_da(model, dataloader):\n",
    "    features = None\n",
    "    lasthidden = None\n",
    "    labels = None\n",
    "    for i, data in enumerate(dataloader):\n",
    "        inputs, labels_ = data\n",
    "        if (use_gpu):\n",
    "            inputs = inputs.cuda()\n",
    "        with torch.no_grad():\n",
    "            inputs = Variable(inputs, requires_grad=False)\n",
    "            _, _, f, l = model(inputs)\n",
    "            if i == 0:\n",
    "                features = f\n",
    "                lasthidden = l\n",
    "                labels = labels_\n",
    "            else:\n",
    "                features = torch.cat((features, f))\n",
    "                lasthidden = torch.cat((lasthidden, l))\n",
    "                labels = torch.cat((labels, labels_))\n",
    "    features = features.data\n",
    "    lasthidden = lasthidden.data\n",
    "    if use_gpu:\n",
    "        features = features.cpu()\n",
    "        lasthidden = lasthidden.cpu()\n",
    "    return features, lasthidden, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CNN_DA(\n",
      "  (C1): Conv2d(3, 96, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
      "  (C2): Conv2d(96, 144, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
      "  (C3): Conv2d(144, 256, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
      "  (LC_FC1): Linear(in_features=4096, out_features=512, bias=True)\n",
      "  (LC_FC2): Linear(in_features=512, out_features=10, bias=True)\n",
      "  (GRL_layer): GRL()\n",
      "  (DC_FC1): Linear(in_features=4096, out_features=1024, bias=True)\n",
      "  (DC_FC2): Linear(in_features=1024, out_features=1024, bias=True)\n",
      "  (DC_FC3): Linear(in_features=1024, out_features=1, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "cnn_da = CNN_DA(0)\n",
    "if (use_gpu):\n",
    "    cnn_da.cuda()\n",
    "print(cnn_da)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load_model_da = True\n"
     ]
    }
   ],
   "source": [
    "para_file_da = \"./parameters/cnn_svhn_to_mnist.pt\"\n",
    "load_model_da = os.path.isfile(para_file_da)\n",
    "print(\"load_model_da = \" + str(load_model_da))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%skip (not $load_model_da)\n",
    "cnn_da.load_state_dict(torch.load(para_file_da))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from math import exp\n",
    "\n",
    "lr_init = 0.01\n",
    "criterion_LC = nn.CrossEntropyLoss()\n",
    "criterion_DC = nn.BCELoss()\n",
    "optimizer = optim.SGD(filter(lambda p: p.requires_grad, cnn_da.parameters()), lr=lr_init, momentum=0.9)\n",
    "\n",
    "def adjust_lr(optimizer, p):\n",
    "    global lr_init\n",
    "    lr_0 = lr_init\n",
    "    alpha = 10\n",
    "    beta = 0.75\n",
    "    lr = lr_0 / (1 + alpha * p) ** beta\n",
    "    for param_group in optimizer.param_groups:\n",
    "        param_group['lr'] = lr\n",
    "    return lr\n",
    "        \n",
    "def adjust_lamda(model, p):\n",
    "    gamma = 10\n",
    "    lamda = 2 / (1 + exp(- gamma * p)) - 1\n",
    "    model.set_lamda(lamda)\n",
    "    return lamda"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 50] batch loss: 2.667\n",
      "[100] batch loss: 2.236\n",
      "[150] batch loss: 1.857\n",
      "[200] batch loss: 1.161\n",
      "[250] batch loss: 0.869\n",
      "[300] batch loss: 0.732\n",
      "[350] batch loss: 0.711\n",
      "[400] batch loss: 0.611\n",
      "[450] batch loss: 0.587\n",
      "[500] batch loss: 0.565\n",
      "[550] batch loss: 0.534\n",
      "[600] batch loss: 0.504\n",
      "[650] batch loss: 0.487\n",
      "[700] batch loss: 0.486\n",
      "[750] batch loss: 0.447\n",
      "[800] batch loss: 0.450\n",
      "[850] batch loss: 0.475\n",
      "[900] batch loss: 0.437\n",
      "epoch 1 loss: inf -> 806.050\n",
      "LC loss: inf -> 768.382\n",
      "DC loss: inf -> 37.668\n",
      "Label classifier accuracy on MNIST test set (DA): 0.509900\n",
      "Domain classifier accuracy on MNIST test set (DA): 0.997260\n",
      "\n",
      "[ 50] batch loss: 0.397\n",
      "[100] batch loss: 0.424\n",
      "[150] batch loss: 0.403\n",
      "[200] batch loss: 0.369\n",
      "[250] batch loss: 0.361\n",
      "[300] batch loss: 0.379\n",
      "[350] batch loss: 0.423\n",
      "[400] batch loss: 0.370\n",
      "[450] batch loss: 0.337\n",
      "[500] batch loss: 0.335\n",
      "[550] batch loss: 0.323\n",
      "[600] batch loss: 0.308\n",
      "[650] batch loss: 0.317\n",
      "[700] batch loss: 0.301\n",
      "[750] batch loss: 0.277\n",
      "[800] batch loss: 0.305\n",
      "[850] batch loss: 0.322\n",
      "[900] batch loss: 0.302\n",
      "epoch 2 loss: 806.050 -> 322.430\n",
      "LC loss: 768.382 -> 319.452\n",
      "DC loss: 37.668 -> 2.978\n",
      "Label classifier accuracy on MNIST test set (DA): 0.546000\n",
      "Domain classifier accuracy on MNIST test set (DA): 0.999299\n",
      "\n",
      "[ 50] batch loss: 0.269\n",
      "[100] batch loss: 0.374\n",
      "[150] batch loss: 0.355\n",
      "[200] batch loss: 0.296\n",
      "[250] batch loss: 0.291\n",
      "[300] batch loss: 0.274\n",
      "[350] batch loss: 0.332\n",
      "[400] batch loss: 0.280\n",
      "[450] batch loss: 0.241\n",
      "[500] batch loss: 0.248\n",
      "[550] batch loss: 0.235\n",
      "[600] batch loss: 0.223\n",
      "[650] batch loss: 0.234\n",
      "[700] batch loss: 0.230\n",
      "[750] batch loss: 0.196\n",
      "[800] batch loss: 0.210\n",
      "[850] batch loss: 0.238\n",
      "[900] batch loss: 0.233\n",
      "epoch 3 loss: 322.430 -> 244.957\n",
      "LC loss: 319.452 -> 231.446\n",
      "DC loss: 2.978 -> 13.511\n",
      "Label classifier accuracy on MNIST test set (DA): 0.591500\n",
      "Domain classifier accuracy on MNIST test set (DA): 0.997935\n",
      "\n",
      "[ 50] batch loss: 0.195\n",
      "[100] batch loss: 0.231\n",
      "[150] batch loss: 0.230\n",
      "[200] batch loss: 0.211\n",
      "[250] batch loss: 0.198\n",
      "[300] batch loss: 0.190\n",
      "[350] batch loss: 0.258\n",
      "[400] batch loss: 0.204\n",
      "[450] batch loss: 0.181\n",
      "[500] batch loss: 0.177\n",
      "[550] batch loss: 0.158\n",
      "[600] batch loss: 0.156\n",
      "[650] batch loss: 0.167\n",
      "[700] batch loss: 0.164\n",
      "[750] batch loss: 0.147\n",
      "[800] batch loss: 0.143\n",
      "[850] batch loss: 0.167\n",
      "[900] batch loss: 0.158\n",
      "epoch 4 loss: 244.957 -> 171.360\n",
      "LC loss: 231.446 -> 164.725\n",
      "DC loss: 13.511 -> 6.635\n",
      "Label classifier accuracy on MNIST test set (DA): 0.593100\n",
      "Domain classifier accuracy on MNIST test set (DA): 0.997702\n",
      "\n",
      "[ 50] batch loss: 0.138\n",
      "[100] batch loss: 0.171\n",
      "[150] batch loss: 0.166\n",
      "[200] batch loss: 0.164\n",
      "[250] batch loss: 0.138\n",
      "[300] batch loss: 0.129\n",
      "[350] batch loss: 0.195\n",
      "[400] batch loss: 0.141\n",
      "[450] batch loss: 0.134\n",
      "[500] batch loss: 0.132\n",
      "[550] batch loss: 0.115\n",
      "[600] batch loss: 0.122\n",
      "[650] batch loss: 0.111\n",
      "[700] batch loss: 0.131\n",
      "[750] batch loss: 0.103\n",
      "[800] batch loss: 0.111\n",
      "[850] batch loss: 0.119\n",
      "[900] batch loss: 0.116\n",
      "epoch 5 loss: 171.360 -> 125.754\n",
      "LC loss: 164.725 -> 120.004\n",
      "DC loss: 6.635 -> 5.751\n",
      "Label classifier accuracy on MNIST test set (DA): 0.637000\n",
      "Domain classifier accuracy on MNIST test set (DA): 0.998546\n",
      "\n",
      "[ 50] batch loss: 0.102\n",
      "[100] batch loss: 0.136\n",
      "[150] batch loss: 0.134\n",
      "[200] batch loss: 0.112\n",
      "[250] batch loss: 0.106\n",
      "[300] batch loss: 0.108\n",
      "[350] batch loss: 0.137\n",
      "[400] batch loss: 0.123\n",
      "[450] batch loss: 0.107\n",
      "[500] batch loss: 0.101\n",
      "[550] batch loss: 0.090\n",
      "[600] batch loss: 0.085\n",
      "[650] batch loss: 0.079\n",
      "[700] batch loss: 0.094\n",
      "[750] batch loss: 0.070\n",
      "[800] batch loss: 0.089\n",
      "[850] batch loss: 0.083\n",
      "[900] batch loss: 0.106\n",
      "epoch 6 loss: 125.754 -> 96.243\n",
      "LC loss: 120.004 -> 93.856\n",
      "DC loss: 5.751 -> 2.386\n",
      "Label classifier accuracy on MNIST test set (DA): 0.627700\n",
      "Domain classifier accuracy on MNIST test set (DA): 0.987179\n",
      "\n",
      "[ 50] batch loss: 0.074\n",
      "[100] batch loss: 0.098\n",
      "[150] batch loss: 0.102\n",
      "[200] batch loss: 0.083\n",
      "[250] batch loss: 0.070\n",
      "[300] batch loss: 0.099\n",
      "[350] batch loss: 0.090\n",
      "[400] batch loss: 0.101\n",
      "[450] batch loss: 0.084\n",
      "[500] batch loss: 0.077\n",
      "[550] batch loss: 0.062\n",
      "[600] batch loss: 0.067\n",
      "[650] batch loss: 0.068\n",
      "[700] batch loss: 0.057\n",
      "[750] batch loss: 0.059\n",
      "[800] batch loss: 0.062\n",
      "[850] batch loss: 0.062\n",
      "[900] batch loss: 0.052\n",
      "epoch 7 loss: 96.243 -> 71.468\n",
      "LC loss: 93.856 -> 69.443\n",
      "DC loss: 2.386 -> 2.025\n",
      "Label classifier accuracy on MNIST test set (DA): 0.621900\n",
      "Domain classifier accuracy on MNIST test set (DA): 0.995967\n",
      "\n",
      "[ 50] batch loss: 0.056\n",
      "[100] batch loss: 0.072\n",
      "[150] batch loss: 0.118\n",
      "[200] batch loss: 0.074\n",
      "[250] batch loss: 0.069\n",
      "[300] batch loss: 0.069\n",
      "[350] batch loss: 0.082\n",
      "[400] batch loss: 0.079\n",
      "[450] batch loss: 0.062\n",
      "[500] batch loss: 0.072\n",
      "[550] batch loss: 0.068\n",
      "[600] batch loss: 0.061\n",
      "[650] batch loss: 0.060\n",
      "[700] batch loss: 0.070\n",
      "[750] batch loss: 0.046\n",
      "[800] batch loss: 0.047\n",
      "[850] batch loss: 0.098\n",
      "[900] batch loss: 0.063\n",
      "epoch 8 loss: 71.468 -> 65.735\n",
      "LC loss: 69.443 -> 62.415\n",
      "DC loss: 2.025 -> 3.320\n",
      "Label classifier accuracy on MNIST test set (DA): 0.656200\n",
      "Domain classifier accuracy on MNIST test set (DA): 0.999473\n",
      "\n",
      "[ 50] batch loss: 0.046\n",
      "[100] batch loss: 0.068\n",
      "[150] batch loss: 0.078\n",
      "[200] batch loss: 0.057\n",
      "[250] batch loss: 0.060\n",
      "[300] batch loss: 0.063\n",
      "[350] batch loss: 0.060\n",
      "[400] batch loss: 0.050\n",
      "[450] batch loss: 0.051\n",
      "[500] batch loss: 0.059\n",
      "[550] batch loss: 0.046\n",
      "[600] batch loss: 0.062\n",
      "[650] batch loss: 0.051\n",
      "[700] batch loss: 0.049\n",
      "[750] batch loss: 0.048\n",
      "[800] batch loss: 0.036\n",
      "[850] batch loss: 0.062\n",
      "[900] batch loss: 0.050\n",
      "epoch 9 loss: 65.735 -> 51.372\n",
      "LC loss: 62.415 -> 48.899\n",
      "DC loss: 3.320 -> 2.473\n",
      "Label classifier accuracy on MNIST test set (DA): 0.623000\n",
      "Domain classifier accuracy on MNIST test set (DA): 0.998330\n",
      "\n",
      "[ 50] batch loss: 0.044\n",
      "[100] batch loss: 0.067\n",
      "[150] batch loss: 0.071\n",
      "[200] batch loss: 0.053\n",
      "[250] batch loss: 0.044\n",
      "[300] batch loss: 0.037\n",
      "[350] batch loss: 0.055\n",
      "[400] batch loss: 0.047\n",
      "[450] batch loss: 0.041\n",
      "[500] batch loss: 0.062\n",
      "[550] batch loss: 0.054\n",
      "[600] batch loss: 0.036\n",
      "[650] batch loss: 0.047\n",
      "[700] batch loss: 0.034\n",
      "[750] batch loss: 0.060\n",
      "[800] batch loss: 0.058\n",
      "[850] batch loss: 0.057\n",
      "[900] batch loss: 0.044\n",
      "epoch 10 loss: 51.372 -> 47.214\n",
      "LC loss: 48.899 -> 45.144\n",
      "DC loss: 2.473 -> 2.070\n",
      "Label classifier accuracy on MNIST test set (DA): 0.641400\n",
      "Domain classifier accuracy on MNIST test set (DA): 0.999112\n",
      "\n",
      "[ 50] batch loss: 0.035\n",
      "[100] batch loss: 0.031\n",
      "[150] batch loss: 0.051\n",
      "[200] batch loss: 0.054\n",
      "[250] batch loss: 0.046\n",
      "[300] batch loss: 0.035\n",
      "[350] batch loss: 0.051\n",
      "[400] batch loss: 0.032\n",
      "[450] batch loss: 0.035\n",
      "[500] batch loss: 0.033\n",
      "[550] batch loss: 0.031\n",
      "[600] batch loss: 0.051\n",
      "[650] batch loss: 0.060\n",
      "[700] batch loss: 0.033\n",
      "[750] batch loss: 0.029\n",
      "[800] batch loss: 0.021\n",
      "[850] batch loss: 0.040\n",
      "[900] batch loss: 0.039\n",
      "epoch 11 loss: 47.214 -> 36.862\n",
      "LC loss: 45.144 -> 34.753\n",
      "DC loss: 2.070 -> 2.109\n",
      "Label classifier accuracy on MNIST test set (DA): 0.673500\n",
      "Domain classifier accuracy on MNIST test set (DA): 0.998614\n",
      "\n",
      "[ 50] batch loss: 0.029\n",
      "[100] batch loss: 0.025\n",
      "[150] batch loss: 0.027\n",
      "[200] batch loss: 0.027\n",
      "[250] batch loss: 0.024\n",
      "[300] batch loss: 0.031\n",
      "[350] batch loss: 0.028\n",
      "[400] batch loss: 0.035\n",
      "[450] batch loss: 0.022\n",
      "[500] batch loss: 0.034\n",
      "[550] batch loss: 0.031\n",
      "[600] batch loss: 0.027\n",
      "[650] batch loss: 0.024\n",
      "[700] batch loss: 0.031\n",
      "[750] batch loss: 0.020\n",
      "[800] batch loss: 0.023\n",
      "[850] batch loss: 0.024\n",
      "[900] batch loss: 0.028\n",
      "epoch 12 loss: 36.862 -> 25.289\n",
      "LC loss: 34.753 -> 24.002\n",
      "DC loss: 2.109 -> 1.288\n",
      "Label classifier accuracy on MNIST test set (DA): 0.632700\n",
      "Domain classifier accuracy on MNIST test set (DA): 0.998439\n",
      "\n",
      "[ 50] batch loss: 0.018\n",
      "[100] batch loss: 0.021\n",
      "[150] batch loss: 0.018\n",
      "[200] batch loss: 0.018\n",
      "[250] batch loss: 0.021\n",
      "[300] batch loss: 0.017\n",
      "[350] batch loss: 0.023\n",
      "[400] batch loss: 0.027\n",
      "[450] batch loss: 0.018\n",
      "[500] batch loss: 0.021\n",
      "[550] batch loss: 0.028\n",
      "[600] batch loss: 0.028\n",
      "[650] batch loss: 0.014\n",
      "[700] batch loss: 0.014\n",
      "[750] batch loss: 0.022\n",
      "[800] batch loss: 0.025\n",
      "[850] batch loss: 0.031\n",
      "[900] batch loss: 0.029\n",
      "epoch 13 loss: 25.289 -> 20.854\n",
      "LC loss: 24.002 -> 20.304\n",
      "DC loss: 1.288 -> 0.550\n",
      "Label classifier accuracy on MNIST test set (DA): 0.641800\n",
      "Domain classifier accuracy on MNIST test set (DA): 0.999537\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 50] batch loss: 0.024\n",
      "[100] batch loss: 0.019\n",
      "[150] batch loss: 0.033\n",
      "[200] batch loss: 0.022\n",
      "[250] batch loss: 0.023\n",
      "[300] batch loss: 0.014\n",
      "[350] batch loss: 0.034\n",
      "[400] batch loss: 0.020\n",
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      "[700] batch loss: 0.023\n",
      "[750] batch loss: 0.019\n",
      "[800] batch loss: 0.020\n",
      "[850] batch loss: 0.019\n",
      "[900] batch loss: 0.014\n",
      "epoch 14 loss: 20.854 -> 21.259\n",
      "LC loss: 20.304 -> 19.929\n",
      "DC loss: 0.550 -> 1.329\n",
      "Label classifier accuracy on MNIST test set (DA): 0.634700\n",
      "Domain classifier accuracy on MNIST test set (DA): 0.998066\n",
      "\n",
      "[ 50] batch loss: 0.012\n",
      "[100] batch loss: 0.025\n",
      "[150] batch loss: 0.015\n",
      "[200] batch loss: 0.016\n",
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      "[750] batch loss: 0.014\n",
      "[800] batch loss: 0.013\n",
      "[850] batch loss: 0.036\n",
      "[900] batch loss: 0.016\n",
      "epoch 15 loss: 21.259 -> 18.031\n",
      "LC loss: 19.929 -> 17.292\n",
      "DC loss: 1.329 -> 0.739\n",
      "Label classifier accuracy on MNIST test set (DA): 0.670000\n",
      "Domain classifier accuracy on MNIST test set (DA): 0.999222\n",
      "\n",
      "[ 50] batch loss: 0.012\n",
      "[100] batch loss: 0.016\n",
      "[150] batch loss: 0.017\n",
      "[200] batch loss: 0.014\n",
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      "[700] batch loss: 0.013\n",
      "[750] batch loss: 0.008\n",
      "[800] batch loss: 0.009\n",
      "[850] batch loss: 0.015\n",
      "[900] batch loss: 0.018\n",
      "epoch 16 loss: 18.031 -> 14.106\n",
      "LC loss: 17.292 -> 13.317\n",
      "DC loss: 0.739 -> 0.789\n",
      "Label classifier accuracy on MNIST test set (DA): 0.642500\n",
      "Domain classifier accuracy on MNIST test set (DA): 0.998889\n",
      "\n",
      "[ 50] batch loss: 0.022\n",
      "[100] batch loss: 0.025\n",
      "[150] batch loss: 0.023\n",
      "[200] batch loss: 0.011\n",
      "[250] batch loss: 0.016\n",
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      "[350] batch loss: 0.014\n",
      "[400] batch loss: 0.016\n",
      "[450] batch loss: 0.009\n",
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      "[550] batch loss: 0.010\n",
      "[600] batch loss: 0.013\n",
      "[650] batch loss: 0.025\n",
      "[700] batch loss: 0.011\n",
      "[750] batch loss: 0.010\n",
      "[800] batch loss: 0.017\n",
      "[850] batch loss: 0.024\n",
      "[900] batch loss: 0.017\n",
      "epoch 17 loss: 14.106 -> 14.791\n",
      "LC loss: 13.317 -> 12.982\n",
      "DC loss: 0.789 -> 1.809\n",
      "Label classifier accuracy on MNIST test set (DA): 0.632100\n",
      "Domain classifier accuracy on MNIST test set (DA): 0.995765\n",
      "\n",
      "[ 50] batch loss: 0.017\n",
      "[100] batch loss: 0.021\n",
      "[150] batch loss: 0.012\n",
      "[200] batch loss: 0.009\n",
      "[250] batch loss: 0.011\n",
      "[300] batch loss: 0.007\n",
      "[350] batch loss: 0.022\n",
      "[400] batch loss: 0.016\n",
      "[450] batch loss: 0.029\n",
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      "[600] batch loss: 0.014\n",
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      "[700] batch loss: 0.016\n",
      "[750] batch loss: 0.018\n",
      "[800] batch loss: 0.014\n",
      "[850] batch loss: 0.018\n",
      "[900] batch loss: 0.014\n",
      "epoch 18 loss: 14.791 -> 15.367\n",
      "LC loss: 12.982 -> 14.548\n",
      "DC loss: 1.809 -> 0.818\n",
      "Label classifier accuracy on MNIST test set (DA): 0.619800\n",
      "Domain classifier accuracy on MNIST test set (DA): 0.998665\n",
      "\n",
      "[ 50] batch loss: 0.012\n",
      "[100] batch loss: 0.007\n",
      "[150] batch loss: 0.014\n",
      "[200] batch loss: 0.011\n",
      "[250] batch loss: 0.008\n",
      "[300] batch loss: 0.010\n",
      "[350] batch loss: 0.020\n",
      "[400] batch loss: 0.011\n",
      "[450] batch loss: 0.010\n",
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      "[600] batch loss: 0.006\n",
      "[650] batch loss: 0.006\n",
      "[700] batch loss: 0.007\n",
      "[750] batch loss: 0.008\n",
      "[800] batch loss: 0.006\n",
      "[850] batch loss: 0.005\n",
      "[900] batch loss: 0.009\n",
      "epoch 19 loss: 15.367 -> 9.102\n",
      "LC loss: 14.548 -> 8.756\n",
      "DC loss: 0.818 -> 0.346\n",
      "Label classifier accuracy on MNIST test set (DA): 0.620600\n",
      "Domain classifier accuracy on MNIST test set (DA): 0.999240\n",
      "\n",
      "[ 50] batch loss: 0.006\n",
      "[100] batch loss: 0.005\n",
      "[150] batch loss: 0.005\n",
      "[200] batch loss: 0.011\n",
      "[250] batch loss: 0.012\n",
      "[300] batch loss: 0.009\n",
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      "[400] batch loss: 0.010\n",
      "[450] batch loss: 0.008\n",
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      "[600] batch loss: 0.005\n",
      "[650] batch loss: 0.008\n",
      "[700] batch loss: 0.018\n",
      "[750] batch loss: 0.011\n",
      "[800] batch loss: 0.016\n",
      "[850] batch loss: 0.016\n",
      "[900] batch loss: 0.014\n",
      "epoch 20 loss: 9.102 -> 9.439\n",
      "LC loss: 8.756 -> 8.756\n",
      "DC loss: 0.346 -> 0.682\n",
      "Label classifier accuracy on MNIST test set (DA): 0.593400\n",
      "Domain classifier accuracy on MNIST test set (DA): 0.999059\n",
      "\n",
      "[ 50] batch loss: 0.008\n",
      "[100] batch loss: 0.010\n",
      "[150] batch loss: 0.012\n",
      "[200] batch loss: 0.014\n",
      "[250] batch loss: 0.007\n",
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      "[700] batch loss: 0.010\n",
      "[750] batch loss: 0.018\n",
      "[800] batch loss: 0.010\n",
      "[850] batch loss: 0.009\n",
      "[900] batch loss: 0.014\n",
      "epoch 21 loss: 9.439 -> 9.121\n",
      "LC loss: 8.756 -> 8.743\n",
      "DC loss: 0.682 -> 0.379\n",
      "Label classifier accuracy on MNIST test set (DA): 0.602200\n",
      "Domain classifier accuracy on MNIST test set (DA): 0.998716\n",
      "\n",
      "[ 50] batch loss: 0.012\n",
      "[100] batch loss: 0.016\n",
      "[150] batch loss: 0.009\n",
      "[200] batch loss: 0.008\n",
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      "[300] batch loss: 0.004\n",
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      "[800] batch loss: 0.016\n",
      "[850] batch loss: 0.010\n",
      "[900] batch loss: 0.006\n",
      "epoch 22 loss: 9.121 -> 8.298\n",
      "LC loss: 8.743 -> 8.058\n",
      "DC loss: 0.379 -> 0.240\n",
      "Label classifier accuracy on MNIST test set (DA): 0.647800\n",
      "Domain classifier accuracy on MNIST test set (DA): 0.999261\n",
      "\n",
      "[ 50] batch loss: 0.007\n",
      "[100] batch loss: 0.006\n",
      "[150] batch loss: 0.008\n",
      "[200] batch loss: 0.007\n",
      "[250] batch loss: 0.007\n",
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      "[700] batch loss: 0.008\n",
      "[750] batch loss: 0.004\n",
      "[800] batch loss: 0.010\n",
      "[850] batch loss: 0.021\n",
      "[900] batch loss: 0.022\n",
      "epoch 23 loss: 8.298 -> 8.563\n",
      "LC loss: 8.058 -> 8.373\n",
      "DC loss: 0.240 -> 0.189\n",
      "Label classifier accuracy on MNIST test set (DA): 0.637100\n",
      "Domain classifier accuracy on MNIST test set (DA): 0.998835\n",
      "\n",
      "[ 50] batch loss: 0.005\n",
      "[100] batch loss: 0.014\n",
      "[150] batch loss: 0.009\n",
      "[200] batch loss: 0.009\n",
      "[250] batch loss: 0.006\n",
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      "[400] batch loss: 0.010\n",
      "[450] batch loss: 0.009\n",
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      "[700] batch loss: 0.006\n",
      "[750] batch loss: 0.004\n",
      "[800] batch loss: 0.004\n",
      "[850] batch loss: 0.006\n",
      "[900] batch loss: 0.008\n",
      "epoch 24 loss: 8.563 -> 6.099\n",
      "LC loss: 8.373 -> 5.902\n",
      "DC loss: 0.189 -> 0.197\n",
      "Label classifier accuracy on MNIST test set (DA): 0.656300\n",
      "Domain classifier accuracy on MNIST test set (DA): 0.999618\n",
      "\n",
      "[ 50] batch loss: 0.007\n",
      "[100] batch loss: 0.011\n",
      "[150] batch loss: 0.012\n",
      "[200] batch loss: 0.005\n",
      "[250] batch loss: 0.009\n",
      "[300] batch loss: 0.009\n",
      "[350] batch loss: 0.009\n",
      "[400] batch loss: 0.006\n",
      "[450] batch loss: 0.004\n",
      "[500] batch loss: 0.007\n",
      "[550] batch loss: 0.004\n",
      "[600] batch loss: 0.003\n",
      "[650] batch loss: 0.004\n",
      "[700] batch loss: 0.004\n",
      "[750] batch loss: 0.002\n",
      "[800] batch loss: 0.003\n",
      "[850] batch loss: 0.002\n",
      "[900] batch loss: 0.002\n",
      "epoch 25 loss: 6.099 -> 5.218\n",
      "LC loss: 5.902 -> 5.144\n",
      "DC loss: 0.197 -> 0.074\n",
      "Label classifier accuracy on MNIST test set (DA): 0.652700\n",
      "Domain classifier accuracy on MNIST test set (DA): 0.999193\n",
      "\n",
      "[ 50] batch loss: 0.001\n",
      "[100] batch loss: 0.001\n",
      "[150] batch loss: 0.002\n",
      "[200] batch loss: 0.002\n",
      "[250] batch loss: 0.003\n",
      "[300] batch loss: 0.002\n",
      "[350] batch loss: 0.004\n",
      "[400] batch loss: 0.004\n",
      "[450] batch loss: 0.003\n",
      "[500] batch loss: 0.002\n",
      "[550] batch loss: 0.003\n",
      "[600] batch loss: 0.004\n",
      "[650] batch loss: 0.012\n",
      "[700] batch loss: 0.020\n",
      "[750] batch loss: 0.021\n",
      "[800] batch loss: 0.015\n",
      "[850] batch loss: 0.012\n",
      "[900] batch loss: 0.006\n",
      "epoch 26 loss: 5.218 -> 6.218\n",
      "LC loss: 5.144 -> 6.146\n",
      "DC loss: 0.074 -> 0.072\n",
      "Label classifier accuracy on MNIST test set (DA): 0.644700\n",
      "Domain classifier accuracy on MNIST test set (DA): 0.999519\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 50] batch loss: 0.003\n",
      "[100] batch loss: 0.005\n",
      "[150] batch loss: 0.010\n",
      "[200] batch loss: 0.008\n",
      "[250] batch loss: 0.004\n",
      "[300] batch loss: 0.006\n",
      "[350] batch loss: 0.017\n",
      "[400] batch loss: 0.012\n",
      "[450] batch loss: 0.016\n",
      "[500] batch loss: 0.013\n",
      "[550] batch loss: 0.007\n",
      "[600] batch loss: 0.003\n",
      "[650] batch loss: 0.005\n",
      "[700] batch loss: 0.003\n",
      "[750] batch loss: 0.009\n",
      "[800] batch loss: 0.007\n",
      "[850] batch loss: 0.010\n",
      "[900] batch loss: 0.003\n",
      "epoch 27 loss: 6.218 -> 7.147\n",
      "LC loss: 6.146 -> 6.944\n",
      "DC loss: 0.072 -> 0.203\n",
      "Label classifier accuracy on MNIST test set (DA): 0.640000\n",
      "Domain classifier accuracy on MNIST test set (DA): 0.999583\n",
      "\n",
      "[ 50] batch loss: 0.002\n",
      "[100] batch loss: 0.003\n",
      "[150] batch loss: 0.006\n",
      "[200] batch loss: 0.003\n",
      "[250] batch loss: 0.014\n",
      "[300] batch loss: 0.023\n",
      "[350] batch loss: 0.014\n",
      "[400] batch loss: 0.016\n",
      "[450] batch loss: 0.007\n",
      "[500] batch loss: 0.010\n",
      "[550] batch loss: 0.006\n",
      "[600] batch loss: 0.017\n",
      "[650] batch loss: 0.013\n",
      "[700] batch loss: 0.007\n",
      "[750] batch loss: 0.018\n",
      "[800] batch loss: 0.033\n",
      "[850] batch loss: 0.103\n",
      "[900] batch loss: 0.112\n",
      "epoch 28 loss: 7.147 -> 23.962\n",
      "LC loss: 6.944 -> 16.131\n",
      "DC loss: 0.203 -> 7.832\n",
      "Label classifier accuracy on MNIST test set (DA): 0.625300\n",
      "Domain classifier accuracy on MNIST test set (DA): 0.987921\n",
      "\n",
      "[ 50] batch loss: 0.030\n",
      "[100] batch loss: 0.028\n",
      "[150] batch loss: 0.023\n",
      "[200] batch loss: 0.029\n",
      "[250] batch loss: 0.018\n",
      "[300] batch loss: 0.029\n",
      "[350] batch loss: 0.027\n",
      "[400] batch loss: 0.027\n",
      "[450] batch loss: 0.026\n",
      "[500] batch loss: 0.027\n",
      "[550] batch loss: 0.030\n",
      "[600] batch loss: 0.022\n",
      "[650] batch loss: 0.024\n",
      "[700] batch loss: 0.020\n",
      "[750] batch loss: 0.011\n",
      "[800] batch loss: 0.015\n",
      "[850] batch loss: 0.009\n",
      "[900] batch loss: 0.012\n",
      "epoch 29 loss: 23.962 -> 20.935\n",
      "LC loss: 16.131 -> 18.409\n",
      "DC loss: 7.832 -> 2.526\n",
      "Label classifier accuracy on MNIST test set (DA): 0.645500\n",
      "Domain classifier accuracy on MNIST test set (DA): 0.998604\n",
      "\n",
      "[ 50] batch loss: 0.014\n",
      "[100] batch loss: 0.012\n",
      "[150] batch loss: 0.012\n",
      "[200] batch loss: 0.012\n",
      "[250] batch loss: 0.017\n",
      "[300] batch loss: 0.014\n",
      "[350] batch loss: 0.013\n",
      "[400] batch loss: 0.006\n",
      "[450] batch loss: 0.007\n",
      "[500] batch loss: 0.015\n",
      "[550] batch loss: 0.009\n",
      "[600] batch loss: 0.009\n",
      "[650] batch loss: 0.021\n",
      "[700] batch loss: 0.014\n",
      "[750] batch loss: 0.006\n",
      "[800] batch loss: 0.006\n",
      "[850] batch loss: 0.013\n",
      "[900] batch loss: 0.011\n",
      "epoch 30 loss: 20.935 -> 10.935\n",
      "LC loss: 18.409 -> 10.130\n",
      "DC loss: 2.526 -> 0.805\n",
      "Label classifier accuracy on MNIST test set (DA): 0.618900\n",
      "Domain classifier accuracy on MNIST test set (DA): 0.999566\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#%%skip $load_model_da\n",
    "\n",
    "prev_loss = np.float(\"inf\")\n",
    "prev_loss_LC = np.float(\"inf\")\n",
    "prev_loss_DC = np.float(\"inf\")\n",
    "total_epoch = 30\n",
    "reset(cnn_da)\n",
    "\n",
    "for epoch in range(total_epoch):\n",
    "    epoch_loss = 0.0\n",
    "    running_loss = 0.0\n",
    "    epoch_loss_LC = 0.0\n",
    "    epoch_loss_DC = 0.0\n",
    "    p = epoch * 1.0 / total_epoch\n",
    "    # adjust_lr(optimizer, p)\n",
    "    lamda = adjust_lamda(cnn_da, p)\n",
    "    for i, data in enumerate(trainloader_da):\n",
    "        # get inputs\n",
    "        source_size = data[0].size()[0] // 2\n",
    "        inputs, labels, domains = data\n",
    "        domains = domains.to(torch.float32)\n",
    "        if (use_gpu):\n",
    "            inputs, labels, domains = inputs.cuda(), labels.cuda(), domains.cuda()\n",
    "        inputs, labels, domains = Variable(inputs), Variable(labels), Variable(domains)\n",
    "        optimizer.zero_grad()\n",
    "        # forward\n",
    "        outputs_LC, outputs_DC, f, l = cnn_da(inputs)\n",
    "        # compute loss\n",
    "        outputs_DC = outputs_DC.view(-1)\n",
    "        loss_LC = criterion_LC(outputs_LC[:source_size], labels[:source_size])\n",
    "        loss_DC = criterion_DC(outputs_DC, domains)\n",
    "        loss = loss_LC + loss_DC\n",
    "        # backward\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        # stat\n",
    "        epoch_loss += loss.item()\n",
    "        running_loss += loss.item()\n",
    "        epoch_loss_LC += loss_LC.item()\n",
    "        epoch_loss_DC += loss_DC.item()\n",
    "        if i % 50 == 49:    # print every 50 mini-batches\n",
    "            print('[%3d] batch loss: %.3f' %\n",
    "                  (i + 1, running_loss / 50))\n",
    "            running_loss = 0.0\n",
    "#     f_da, lh_da, _ = extract_features_da(cnn_da, testloader)\n",
    "#     f_m_da, lh_m_da, _ = extract_features_da(cnn_da, testloader_m)\n",
    "#     visualize_da(f_da.numpy(), f_m_da.numpy(), perplexity=30, sample_num=200,\n",
    "#                  save=\"./pics/f_\" + str(epoch + 1) + \".svg\")\n",
    "#     visualize_da(lh_da.numpy(), lh_m_da.numpy(), perplexity=30, sample_num=200,\n",
    "#                  save=\"./pics/lh_\" + str(epoch + 1) + \".svg\")\n",
    "    print(\"epoch %d loss: %.3f -> %.3f\" % (epoch + 1, prev_loss, epoch_loss))\n",
    "    print(\"LC loss: %.3f -> %.3f\" % (prev_loss_LC, epoch_loss_LC))\n",
    "    print(\"DC loss: %.3f -> %.3f\" % (prev_loss_DC, epoch_loss_DC))\n",
    "    print((\"Label classifier accuracy on MNIST test set (DA): %f\\n\"\n",
    "           \"Domain classifier accuracy on MNIST test set (DA): %f\\n\")\n",
    "          %evaluate_da_accuracy(cnn_da, testloader, source=False))\n",
    "    if prev_loss - epoch_loss < 0.1:\n",
    "        prev_loss = epoch_loss\n",
    "        prev_loss_LC = epoch_loss_LC\n",
    "        prev_loss_DC = epoch_loss_DC\n",
    "        pass\n",
    "    else:\n",
    "        prev_loss = epoch_loss\n",
    "        prev_loss_LC = epoch_loss_LC\n",
    "        prev_loss_DC = epoch_loss_DC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%skip $load_model_da\n",
    "\n",
    "torch.save(cnn_da.state_dict(), para_file_da)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test on SVHN and MNIST dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_da_accuracy(model, dataloader, source):\n",
    "    model.eval()\n",
    "    correct_LC = 0\n",
    "    correct_DC = 0\n",
    "    total = 0\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        for i, data in enumerate(dataloader):\n",
    "            inputs, labels = data\n",
    "            if (use_gpu):\n",
    "                inputs, labels = inputs.cuda(), labels.cuda()\n",
    "            inputs, labels = Variable(inputs), Variable(labels)\n",
    "            outputs_LC, outputs_DC, _, _ = model(inputs)\n",
    "            correct_LC += (torch.max(outputs_LC.data, 1)[1] == labels.data).sum().item()\n",
    "            if source:\n",
    "                correct_DC += labels.size()[0] - outputs_DC.data.sum().item()\n",
    "            else:\n",
    "                correct_DC += outputs_DC.data.sum().item()\n",
    "            total += labels.size()[0]\n",
    "        acc_LC = correct_LC / total\n",
    "        acc_DC = correct_DC / total\n",
    "    return acc_LC, acc_DC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label classifier accuracy on SVHN test set (DA): 0.919215\n",
      "Domain classifier accuracy on SVHN test set (DA): 0.999045\n"
     ]
    }
   ],
   "source": [
    "print((\"Label classifier accuracy on SVHN test set (DA): %f\\n\"\n",
    "       \"Domain classifier accuracy on SVHN test set (DA): %f\")\n",
    "      %evaluate_da_accuracy(cnn_da, testloader_svhn, source=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label classifier accuracy on MNIST test set (DA): 0.697800\n",
      "Domain classifier accuracy on MNIST test set (DA): 1.000000\n"
     ]
    }
   ],
   "source": [
    "print((\"Label classifier accuracy on MNIST test set (DA): %f\\n\"\n",
    "       \"Domain classifier accuracy on MNIST test set (DA): %f\")\n",
    "      %evaluate_da_accuracy(cnn_da, testloader, source=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "f_svhn_da, lh_svhn_da, l_svhn_da = extract_features_da(cnn_da, testloader_svhn)\n",
    "f_da, lh_da, l_da = extract_features_da(cnn_da, testloader)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Features of MNIST test set (extracted by DA model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_da(f_svhn_da.numpy(), f_da.numpy(), perplexity=50, sample_num=500,\n",
    "             save=\"./pics/SVHN_to_MNIST_features_Adapted.svg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Features of MNIST-M test set (extracted by DA model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_da(lh_svhn_da.numpy(), lh_da.numpy(), perplexity=50, sample_num=500,\n",
    "             save=\"./pics/SVHN_to_MNIST_lasthidden_Adapted.svg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "default_view": {},
   "name": "My_CNN_Tutorial.ipynb",
   "provenance": [],
   "version": "0.3.2",
   "views": {}
  },
  "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}