Attention_MNIST_example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import essential packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorly as tl\n",
    "from tensorly.decomposition import parafac as CP ## CP\n",
    "from tensorly.decomposition import tucker  ## Tucker\n",
    "from tensorly.decomposition import tensor_train as TT ## Tensor-trian\n",
    "from tensorly import tucker_to_tensor\n",
    "import tensorly.tenalg as talg\n",
    "\n",
    "import numpy as np\n",
    "import scipy.linalg as la\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "import seaborn as sns\n",
    "import os\n",
    "from tqdm import trange\n",
    "from sklearn.metrics import mean_squared_error as MSE\n",
    "import numpy as np\n",
    "from sklearn.metrics import accuracy_score\n",
    "import argparse\n",
    "import torch\n",
    "import torch.utils.data as data_utils\n",
    "import torch.optim as optim\n",
    "from torch.autograd import Variable\n",
    "from torchvision import datasets, transforms\n",
    "# from dataloader import MnistBags\n",
    "from mnist_bags_loader import MnistBags\n",
    "from model import Attention, GatedAttention\n",
    "from DataTransform import convert_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_number = 9 # bags have a positive labels if they contain at least one 9\n",
    "mean_bag_length = 20 # average bag length\n",
    "var_bag_length = 0 # variance of bag length\n",
    "num_bags_train = 50 # the number of bags in training set\n",
    "num_bags_test = 1000 # the number of bags in testing set\n",
    "seed = 5 # random seed\n",
    "use_cuda = torch.cuda.is_available()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Load Train and Test Set\n"
     ]
    }
   ],
   "source": [
    "use_cuda = torch.cuda.is_available()\n",
    "print('Load Train and Test Set')\n",
    "# loader_kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}\n",
    "train_loader = data_utils.DataLoader(MnistBags(target_number= target_number,\n",
    "                                               mean_bag_length= mean_bag_length,\n",
    "                                               var_bag_length= var_bag_length,\n",
    "                                               num_bag= num_bags_train,\n",
    "                                               seed= seed,\n",
    "                                               train=True),\n",
    "                                     batch_size=1,\n",
    "                                     shuffle=True,)\n",
    "\n",
    "test_loader = data_utils.DataLoader(MnistBags(target_number= target_number,\n",
    "                                              mean_bag_length= mean_bag_length,\n",
    "                                              var_bag_length= var_bag_length,\n",
    "                                              num_bag= num_bags_test,\n",
    "                                              seed= seed,\n",
    "                                              train=False),\n",
    "                                    batch_size=1,\n",
    "                                    shuffle=False,)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "is cuda available:  True\n"
     ]
    }
   ],
   "source": [
    "from net import *\n",
    "from modules.criterion import *\n",
    "data_type = \"matrix\" # matrix / tensor,  default = matrix\n",
    "model_type = \"attention\" # attention\n",
    "gate = False\n",
    "img_sz = (28,28)\n",
    "model = MIL(img_sz,data_type,model_type, gate)\n",
    "### setting GPU\n",
    "use_cuda = torch.cuda.is_available()\n",
    "print(\"is cuda available: \",use_cuda)\n",
    "if use_cuda:\n",
    "    model = model.cuda()\n",
    "learning_rate = 1 * 1e-4 ## 4\n",
    "### define the optimization algorithm\n",
    "optimizer = optim.Adam(model.parameters(), lr= learning_rate)\n",
    "## define criterion\n",
    "criterion = attention_obj()\n",
    "## the total number of iteration \n",
    "num_epochs = 50 ## 50\n",
    "## weight decay\n",
    "decay_weight = 1 # 0.98\n",
    "\n",
    "_lambda = 0 * 1e-3  ## 0.01 default"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Module.parameters of MIL(\n",
       "  (feature_extractor_part1): Sequential(\n",
       "    (0): Conv2d(1, 20, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
       "    (1): BatchNorm2d(20, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (2): ReLU()\n",
       "    (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (4): Conv2d(20, 50, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
       "    (5): BatchNorm2d(50, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (6): ReLU()\n",
       "    (7): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  )\n",
       "  (feature_extractor_part2): Sequential(\n",
       "    (0): Linear(in_features=2450, out_features=512, bias=True)\n",
       "    (1): ReLU()\n",
       "  )\n",
       "  (attention_net): Attn_Net(\n",
       "    (module): Sequential(\n",
       "      (0): Linear(in_features=512, out_features=256, bias=True)\n",
       "      (1): Tanh()\n",
       "      (2): Linear(in_features=256, out_features=1, bias=True)\n",
       "    )\n",
       "  )\n",
       "  (classifier): Sequential(\n",
       "    (0): Linear(in_features=512, out_features=1, bias=True)\n",
       "    (1): Sigmoid()\n",
       "  )\n",
       ")>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 5, Loss: 0.69761968, Train auc: 0.480000\n",
      "Epoch: 10, Loss: 0.46348853, Train auc: 0.800000\n",
      "Epoch: 15, Loss: 0.03157507, Train auc: 1.000000\n",
      "Epoch: 20, Loss: 0.00527025, Train auc: 1.000000\n",
      "Epoch: 25, Loss: 0.00206924, Train auc: 1.000000\n",
      "Epoch: 30, Loss: 0.00100436, Train auc: 1.000000\n",
      "Epoch: 35, Loss: 0.00056255, Train auc: 1.000000\n",
      "Epoch: 40, Loss: 0.00035505, Train auc: 1.000000\n",
      "Epoch: 45, Loss: 0.00024201, Train auc: 1.000000\n",
      "Epoch: 50, Loss: 0.00017324, Train auc: 1.000000\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "# def train(epoch):\n",
    "for epoch in range(1,num_epochs+1):\n",
    "    model.train()\n",
    "    train_loss = 0.\n",
    "    # train_acc = 0.\n",
    "    Y_label = []\n",
    "    Y_hat_label = []\n",
    "    for batch_idx,(data,label) in enumerate(train_loader):\n",
    "        # data = torch.unsqueeze(data,2)\n",
    "        bag_label = label[0] + 0.  ## convert true-false to 1-0\n",
    "        ins_label = torch.tensor([0])\n",
    "        if use_cuda:\n",
    "            data, bag_label = data.cuda(), bag_label.cuda()\n",
    "        # data, bag_label = Variable(data), Variable(bag_label)\n",
    "        optimizer.zero_grad()\n",
    "        # calculate loss and metrics\n",
    "        y_hat,A = model(data) #data.float()\n",
    "        loss = criterion(y_hat,bag_label)\n",
    "        train_loss += loss.item()\n",
    "        Y_label.append(bag_label.squeeze().detach().cpu().numpy())\n",
    "        y_hat_label = torch.ge(y_hat, 0.5).float().detach().cpu().numpy()\n",
    "        Y_hat_label.append(y_hat_label.squeeze())\n",
    "        # backward pass\n",
    "        loss.backward()\n",
    "        # step\n",
    "        optimizer.step()\n",
    "    # calculate loss and error for epoch\n",
    "    train_loss /= len(train_loader)\n",
    "    # train_acc = accuracy_score(Y_label,Y_hat_label)        \n",
    "    fpr, tpr, thresholds = metrics.roc_curve(Y_label, Y_hat_label)\n",
    "    train_auc = metrics.auc(fpr, tpr)   \n",
    "    # train_error /= len(train_loader)\n",
    "    if epoch % 5 == 0:\n",
    "    # print('Epoch: {}, Loss: {:.4f}, Train error: {:.4f}'.format(epoch, train_loss.cpu().numpy()[0], train_error))\n",
    "        print('Epoch: {}, Loss: {:.8f}, Train auc: {:.6f}'.format(epoch, train_loss, train_auc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.eval()\n",
    "Y_label = []\n",
    "Y_hat_label = []\n",
    "ty_hat_list = []\n",
    "tdata_list = []\n",
    "ins_label_list = []\n",
    "Y_hat_prob = []\n",
    "for batch_idx, (data, label) in enumerate(test_loader):\n",
    "    # data = torch.unsqueeze(data,2)\n",
    "    bag_label = label[0] + 0  ## convert true-false to 1-0\n",
    "    if use_cuda:\n",
    "        data, bag_label = data.cuda(), bag_label.cuda()\n",
    "    y_hat,attention_weights = model(data.float())\n",
    "    y_hat_label = torch.ge(y_hat, 0.5).float()\n",
    "    \n",
    "    Y_label.append(bag_label.squeeze().detach().cpu().numpy())\n",
    "    Y_hat_label.append(y_hat_label.squeeze().detach().cpu().numpy())\n",
    "    Y_hat_prob.append(y_hat.squeeze().detach().cpu().numpy())\n",
    "    tdata = data.detach().cpu().numpy().squeeze().squeeze()\n",
    "    tdata_list.append(tdata)\n",
    "    \n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "fig,ax = plt.subplots(figsize = (8,4))\n",
    "### presence-based\n",
    "plt.subplot(1,3,1)\n",
    "sns.distplot(Y_hat_prob,bins = 20)\n",
    "plt.title(\"Y-hat-prob\")\n",
    "## lower-bound\n",
    "plt.subplot(1,3,2)\n",
    "plt.title(\"Y-hat-label\")\n",
    "sns.distplot(Y_hat_label,bins = 20)\n",
    "plt.subplot(1,3,3)\n",
    "sns.distplot(Y_label,bins = 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test auc:  0.90548\n",
      "the acc of testing:  0.862\n",
      "the tpr of testing:  0.778\n",
      "the tnr of testing:  0.946\n"
     ]
    }
   ],
   "source": [
    "from utils import *\n",
    "from sklearn import metrics\n",
    "\n",
    "Y_hat_prob_ = [1 if x >=0.5 else 0 for x in Y_hat_prob]\n",
    "fpr, tpr, thresholds = metrics.roc_curve(Y_label, Y_hat_prob)\n",
    "TPR,TNR,ACC = cls_measure(Y_label,Y_hat_prob_)\n",
    "test_auc = np.round(metrics.auc(fpr, tpr),5)\n",
    "print(\"test auc: \",test_auc)\n",
    "print(\"the acc of testing: \",ACC)\n",
    "print(\"the tpr of testing: \",np.round(TPR,5))\n",
    "print(\"the tnr of testing: \",np.round(TNR,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3 Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ground truth:  1\n",
      "the probability for a bag including number 9:  1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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DdDvl7jpuWroLLZBUV/UsqYmyT2YpFX/dzOU1uUa99nIN4Buqqh7uLjf+N7NvyU1Me0SR+85X/yXXXTczKVj45WlSVK62wdJKrnvsRMUnIJOW/lxqKfd+JbvaZlDfG6b6dp2U8tffJXKNIN3kunePkzu/8Byl7MOIC32ubCF3XZ0p1xhxi9xQkVVynCN1DvmksW7mu65sK/eD82dy9eMf0Wvfq+qJUl3lGg4v19KTuScl60c7uUaKJ+UaF5b1HJL7rs538mVTje4Xlea6lGVLM4t339zDzKab2Rbmumn9I9GFxcx1q2lnrivo52Z2UpHl6Bx1b6kw14VskpntlSNv26grSmZ528wGWlW3U0XdWWZZvJtQZnnHzK43sxZmdoC5LkYdomOH+90mOqfO5rqIZ7rOtDCzh6KuMS3MdcmRue5sP5nrvtbYzA42140nM/Tjj+a6lq9trqvNo1Y13GNNc91uekfbtjDXvW6NHO9Bd3Nd8HY2s5bmuv09ksjT08zmmetWVyNddVhSv1RaOupzH3N/zxubq6+v2NJd4zJLCzPbxFz962Ku6+iVQfo65rquNjazvc11f+0epbUxV3+PMLNG5urtmGD7XaP8W0TrW5rZDKv6bLkkOueO0fZHm6szbaP0g811J2wUbTPH4t1em0flnxylt4jOo9R/Byy1u1RaOurZkebqjMwN83vNXPfvbHnbWv7rZiNzf797m+ty28KqrnsyV3eamqtzN5vrFppJWzWxbzN3Hc902W0S7e8qc9e8FlY1pLJQ/T/S3GfJRlnOqXVU1qOjsjU1d93OllfmrvOzzOyg6LjXWHwoaKH6zlI/lkpLR/2tq+ukLH/9bR9tXxGV5dMs57CGuaEbyWEZbS3/58o95oY5rxQd/0JzXdQz2/a2qs+DI81df3MNX2Op/0ullV/dzHddaW7x+nGmuWEUq0Xpnc0N0fhzjn3vH5W/wsy2NVd3+kdpbczsP2b2z2U4hy7mhn40i8p8bvRerhyUq9jv5Mu01NYfhczNi/C1uS/gT1v8i7SZ2RlmNsHcl4EbrOpLfBdz4466ZDmOzGyXqCzzzewLcx9YYfqz5j7gsm072pYea3u4uRuYbF8aukXbLIiOlev8u0XnFM5Rca8t7dggfWcz+yQ61/ctPtZI5sbYTo+WYeb+ADNp25m70ZwZpT+T5/2SuS9nE819sD9h8XF/Mjf+MDnvBUv9XiotHfVZ5m5WpplrULvHqsaVy8zGWVUdb2tmH5v7O/7e3JeZxkHefmY21dxnw4fmbmzC4/zOzN4zd6H43szuMtcQmEk/3dz4+TnRuZwTpLUwsyHmxv3NNrOxFh/T90a039nmxsYfluX9TupWzf8zlvJbKi0d9ezv5hrJ5kX/3mlVNxqy6l03e9nSRgfpD5urC7PMbLi5xolc70/yPRiUZd+DorS2lr/+f2Nu3PHcYLk9SN/A3LVyevT+vWJVDZPZlj3M3bAuiM6vW5BWqL6z1I+l0tJRf2V1d53MV3/XN3cvPN/cffPALOX8iy09H0y2ZbTFP1dWNjcX2w/mflR409wXLpn7gveeuWvzz+a+3O1ZQ//HLOW5VFr51U1Z/utKuBxr8caUS6Jyzk0smfSHo7LPjfZ/RpDWP9p2XmLb8JxynUN3q/o8mWGuQadHsF2h7+TLvVSY8YAHAAAAAACQDjU5RwUAAAAAAMByoaECAAAAAACkBg0VAAAAAAAgNWioAAAAAAAAqdGkLg5SUVHBjJ0lYGY8Yxp5UTdLg7qJQqibpUHdRCHUzdKgbqIQ6mZp1GbdpEcFAAAAAABIDRoqAAAAAABAatBQAQAAAAAAUoOGCgAAAAAAkBo0VAAAAAAAgNSgoQIAAAAAAKQGDRUAAAAAACA1aKgAAAAAAACpQUMFAAAAAABIjSalLgAAAChve++9t4+feOIJHz/yyCOxfKeffrqPZ8+eXfsFAxq4QYMG+fiSSy7x8W677RbLN3r06DoqEQAUhx4VAAAAAAAgNWioAAAAAAAAqcHQjxJad911Y+unnHKKjw866KBY2tZbb+3jGTNm1G7BgDrWo0cPH19zzTU+TnZNffLJJ318/fXX+/jNN9+sxdIBSOrYsWNs/cwzz/Rx06ZNfXz00UfH8lVWVvr4b3/7W+0UDmjAwqEeUny4R6hXr16xdYZ+AEgbelQAAAAAAIDUoKECAAAAAACkBg0VAAAAAAAgNZijooY0alTV5tO+fftY2korreTjoUOH+njzzTeP5WvTpo2Pf/vtt1iamdVIOYE0+Mtf/hJbD8fQNmvWzMfJv/s//OEPPt511119fNlll8Xy3XTTTTVSTgBVwnkpXnzxxVjapptumnWbX375Jbb+3nvv1XzBAHjhtRFA7bv88stzpl188cV1WJL6hx4VAAAAAAAgNWioAAAAAAAAqcHQj2ro2rVrbP20007zcffu3X3cp0+fovZXUVERW//yyy99fN9998XSZs6cWXQ5gTS66KKLfHzhhRfG0sLHGYYmTJgQW//www99vPHGG/s4fFSpJLVs2dLHV1xxRbXLCmBpt99+u49zDfVISg7Leuqpp2q0TACkV1991cfJx44CqHnhsP5knVtllVV8zNCP5UOPCgAAAAAAkBo0VAAAAAAAgNSoqIunSVRUVKT6kRVNmsRHwJx88sk+vuCCC3wcPpVDklq3bu3jfO/jDz/84ON3333Xx48//ngs34gRI3w8f/78QsUuyMwqCudCQ1abdXPDDTeMrYez/a+44oqxtDlz5vg4rH+PPfZYLN+iRYt8/Kc//cnHN998cyzfyy+/7ONwKNaSJUuKKXqto26ikLRcNzt16uTjzz//3MejR4+O5VuwYEHWfFdeeWUs36+//lrDJaxZ1E0Ukpa6GVqWe/nk8OO0o26ikLqsm3vvvbePn3766Vja119/7eP111+/ropUMrVZN+lRAQAAAAAAUoOGCgAAAAAAkBo0VAAAAAAAgNTg8aSSLrnkkth68tGJ1TV06NDY+qWXXurjKVOmLNe+gXIRjlOXpLffftvHyTlYTjrpJB9Pnz69qP1/8cUXPp46dWosbaONNvJxOB/G3Llzi9o30FA1ahT//SJ8rHA4L1Py0cFnnXVWrZYLQNyyPIY0vB8FsOx23333UhdBK6+8cmx9xowZJSpJ7aFHBQAAAAAASA0aKgAAAAAAQGo02KEfHTt29HGyC+taa63l47CbXP/+/WP5xo8f7+ObbrrJx7fddluNlROoL3r37l2j+3vhhRd8/Mknn+TMx3APoHi77rprbP3UU0/Nmi/5eFIAdSs5bLkYgwYNqvmCAA1Ay5YtY+t9+/bNmTf5uNKa1K1bNx+/8sorsbS77rrLx1dddVWtlaEu0aMCAAAAAACkBg0VAAAAAAAgNWioAAAAAAAAqdFg56iYNm2aj++5555YWviYtSOPPNLHyfGAgwcP9vGsWbNquIQAipV8pOkuu+zi4/DxpMnHogKI69evX860hQsX+njs2LF1URwAkeT8EsvyeNJlkTxOuB7eFycffcp8GKhPjj766Nh6OJ9hUvgds6Z16dLFx127do2lbbrpprV23FKhRwUAAAAAAEgNGioAAAAAAEBqNNihH6G99947tn7uuef6+N133/XxkCFDYvkY7gGkw5gxY2LrYRe9Fi1a+JihH0B+u+22W860Ro2qftvYc889Y2lDhw6ttTIBWPqRwMU+nnRZHiUcDtso9jjJfOFxeZwxyl337t1zpoXDIiVp1KhRtVya7MaPH1+S49YmelQAAAAAAIDUoKECAAAAAACkRoMd+nHRRRf5ONldLewefvDBB/t45syZtV8wANX2hz/8odRFAOq9N99808errbZaje67Xbt2sfXtt9/ex2G32pdeeqlGjwuUi2V9ysdrr71W7W2KHe6RT1hehn6g3CX/hk899VQff/HFF7G05HpNmjp1qo/nzZsXS9t///19/Le//a3WylCX6FEBAAAAAABSg4YKAAAAAACQGjRUAAAAAACA1GhQc1Ssv/76Pr788st9vGDBgli+fv36+XjatGm1XzAA1da8eXMfd+rUqYQlARqGtdZay8fjxo1b7v01btzYx/fdd18sLZx3Zu7cuT7u2rVrLB9zRwHLJjnnRU3MSxEKH3EKlLtXXnkltv7DDz/4uFu3brG0s846y8ezZs3y8VdffRXLN2bMGB8vXry4qHKEjyAN911f0aMCAAAAAACkBg0VAAAAAAAgNer10I/VV189th4+oqmiosLHhx56aCzfBx984OOw+86BBx6Y81ibbrqpj1daaaVY2oMPPujjl19+2cf33ntvzv0ByG+vvfby8eabbx5LC+t3OCyEbuLAsmvTpo2Pv/nmm2XaRzjcI+xqnu8Rw61atfJx7969Y2mPPvqoj4vtOgtg6aEey/r404xLL710ubYH0uynn36KrYff7c4+++xY2g033FDUPh966CEf33bbbT5+++23l6WI6tKli48ff/xxHyfvfY8//vhl2n8p0KMCAAAAAACkBg0VAAAAAAAgNer10I8bb7wxtr7qqqv6OOxmuuaaa8bynX/++T7u2bNnzv2HM5H/9ttvPjazWL4jjjjCx/vuu6+PGfoBVM8aa6zh41122cXHyToXeuqpp3x88cUXx9IeeOCBGiwdUL8dfPDBPv7oo4+WaR/HHHOMj5P1sRjJ2dW32WYbH7/zzjvLVCagoXj11Vd9vLxDPYCG7M9//rOPZ8yYEUsLv0eGQyaTjjzyyKxxOHxZkqZOnerjlVde2cfNmjXLue/999/fx8mnW4ZDUz777LOc+0gDelQAAAAAAIDUoKECAAAAAACkBg0VAAAAAAAgNer1HBW77757bP3bb7/18ZgxY3x86623xvJ17tzZx5dffrmPx44dG8v34Ycf+vj777/3cZ8+fWL5wn1ssskmxRQdgKR+/frF1q+++mofd+3atah9hI9r+te//hVLa926tY/DR0MBWFo4R0yxunfvHlv/4x//mDXfG2+8EVv/7rvvfLzbbrv5+Icffojl23PPPX3MHBXA0sLHkI4ePbpG9x3ur6b3DZSLq666KrYezpG40UYb+figgw6K5dtvv/18nO/74WqrrZb19Xzzs911110+Ts6JmPZ5KUL0qAAAAAAAAKlBQwUAAAAAAEiNejf047DDDvNxu3btYmn333+/j8ePH+/jyZMnx/KFXXE+/vhjHy9ZsqSoMjz55JOx9TPPPNPHZ599dlH7ABqq4447zse33357LK1Jk+wfWV988UVs/dJLL/Vx+Pim5DCvm2++2cdh/b7jjjuKLzDQQITDqIp11FFHxdZ32mmnrPm++eab2PqoUaN8HD4Wddttt43lu+CCC6pdJqChqulHkobDsgA4v/76q4/DaQLCWIoPGenfv7+Pd9hhh1i+Nddc08fTp0/38eabbx7L99tvv/n4tNNO8/HixYuLLHn60KMCAAAAAACkBg0VAAAAAAAgNcp+6EeLFi1i6+eee66PJ02aFEu77LLLfDxw4EAfb7/99rF8yW7k1bXHHnvE1nv06OHjiy++eLn2DdR34VMBcg31kOJd68L6LEnPPfdc1m0++eST2Przzz/v43BYCEM/0FC98MILsfUNNtjAx1tvvXW19/fYY4/F1sOhGvPnz/fxlVdeGcv317/+1ceNGlX9pjJhwoRYvp9++qnaZQJQvOTTPMKhlQCWXXgNDJ88V+xT6K6//vrY+umnn+7jdddd18fL+722lOhRAQAAAAAAUoOGCgAAAAAAkBo0VAAAAAAAgNQo+zkqWrVqFVvfcsstffz555/H0g455BAfV1RU+LhDhw6xfMWO5WnTpo2Pw0fMnHLKKbF8H330kY+//vrrovYNNCRDhgzxcfj4QjPLuU04TjbXnBRJyUdD7bPPPj5+7733spZHij/mCajPzjnnnNh6+DjD7t27+zg5t9M777yTdX+9e/fOeax///vfPl577bVjaeF1OZyXIjleHkDtSs5JQR0E0qlp06Y+zjfHWzmhRwUAAAAAAEgNGioAAAAAAEBqlH2/kIMOOii2HnYV//jjj2NpI0eO9PF1113n44MPPjiWL9xulVVW8XH4WDVJ2muvvXwcDkG55pprYvkGDx7s42nTpmU5C6BhadeuXWz9+OOP93FYh5NDPz777DMf33vvvctdjvHjx/s4fMxhOCREklZccUUfh4+TAuqbhQsXxtbDx4Ree+21Pn722Wdj+fbff38fh0Mck8OtQvvuu6+PjzrqqJz5nn76aR+/++67OfMB9VlyyMWuu+7q43CIVk3Ybbfdch4XQDp99dVXPq4vUw3QowIAAAAAAKQGDRUAAAAAACA1yn7ox0orrZQzLdlFO+waF842/qc//SmWL3w6SPv27X3crFmzWL4XXnjBx+FM6WH3dABL69u3b2w9nKk4FD4xR5L22GMPH8+cOXO5yzFr1iwfX3TRRT6+9dZbY/m22WYbH7/22mvLfVygXDz55JM+Hjt2rI9feeWVWL5c9WL69Ok59x3W+y+//DKWtnjxYh8PGDCguMIC9VhyCEZ4T7ssQz+S+wuf7sFwD6D8tG7d2sfhlAS//PJLKYpTI+hRAQAAAAAAUoOGCgAAAAAAkBo0VAAAAAAAgNQo+zkq8unfv3/O9YqKCh8nH4E4YcIEH998880+fuqpp2L5KisrfVzO43+AuhaOnctnxIgRsfWamJcil/DxxTfddFMsLXw08ZtvvhlLC8fSA/XZ5MmTfZwcE3/aaaf5eK211vLx5ptvHsvXoUMHH4ePGE7OFQUgv0GDBvk436NKc803Ec5JkS8fgPKw2mqr+bhjx44+/vHHH0tRnBpBjwoAAAAAAJAaNFQAAAAAAIDUKPuhHw8++GBsvUWLFj7efffdY2nh48/uueceH++7776xfDfccIOPa7OrOdBQPf/887H1qVOn+jis09dff32dlSnsGnf66afH0u68804f33///bG0I488snYLBqRQWGel+ON9AdSt3XbbrdRFAFDHGjduXOoi1Dp6VAAAAAAAgNSgoQIAAAAAAKQGDRUAAAAAACA1KpKP5qyVg1RU1P5BsBQzqyicCw0ZdbM0qJsohLpZGtRNFELdLA3qJgppaHVz0qRJsfVOnTr5eLPNNvPxuHHjarUctVk36VEBAAAAAABSg4YKAAAAAACQGgz9qMfoJodCqJulQd1EIdTN0qBuohDqZmlQN1EIdbM0GPoBAAAAAAAaBBoqAAAAAABAatBQAQAAAAAAUoOGCgAAAAAAkBo0VAAAAAAAgNSgoQIAAAAAAKQGDRUAAAAAACA1aKgAAAAAAACpQUMFAAAAAABIjSZ1dJwfJX1bR8eC07XUBUBZoG7WPeomikHdrHvUTRSDuln3qJsoBnWz7tVq3awws9rcPwAAAAAAQNEY+gEAAAAAAFKjnBoqTNK6pS4EUI9Rx4DyRh0GSos6CNQv1OkSqu2GikpJCyTNlTRN0r2SWtXyMXM5W9L3kmZLultS8zx5T5A0Xq7cz0nqFKS1lXSfpB+iZVBi256S/iNpjqSPJe2USO8g6SFJsyT9JOnBIK2zpCckzZQ0WdKAxLZbSPpA0vzo3y0S5zchOr+pkm5S3c1BgtKpVDrq2CaSnpcbH1jMeLLfSRor9/c6QdJJQdpukj6R9LOkGZIel6sbGe0lDY/SfpSrQ20S+z9T0jeS5kn6n6T1o9dXl/SkXB0xSd0S2+Xb96qSHo62nSXpLUnbFXGuuVRIuiY61oworgjSG0u6IjreHEn/lfv8Qf1SqXTUYak8rpP3SvotOm5maRykryjpVrn6O0vS61nK3kzuc2FyjnM7Ru7z4YQc6ahfKpWOOthc7t5tqtzf/a2SmubJf6ekLyQtkXRslvR89flyuevsIi1dP3tF+wzrWP9EnsPk6tA8SV9L2jlI6xelzZH0maS+ec6hkOZR2WdH5zIwkV5MfUfDU6l01GkpHdfVCkkXSZoYleMRxe+b89WzbnLXw/Dz4OI851Cj6qJHxR/k/ji2ktRD0l+z5KntL9S9JV0gaXe5ST/WlnRpjry9JF0paX+5Ly3fyH05ybhJ7oOxm6RtJR0t6bgorb2kpyRdJ/cHdW203i7Y/t9yfwRd5L74XB+kPRAdr6OkfaNy7BalNZNrxHgg2t990XqzKP1Jufe4jdyXxs0lnZHjHFG/pKGOLZT0qKTji8jbVK7x4Q5JK0k6VNKNcn+zkrux6S1XhzpJ+krSbcH2V8jVgbUkrSNXXwYF6SdE5dhX7n35vdxNjORuvp6TdFCOsuXbdytJ70naWq6u3yfpGS37xe8kuRu4zSVtJvf/eHKQfqnchWcHuXp9tKRflvFYSLc01OFyuU4q2qZVsCwO0u6MjrFR9O/ZWcp/rqTpOc6tnaQLJY3LkY76KQ118ILo2JvINa5vlaMcGR9JOlWu0T+pUH0eL+k8uWtYNlMVr2P3BWl7yjWsHyeptaRd5H5wkNyPCg/IfdFpI1fXHpKrx8tikKT1onPYLSpznyC9mPqOhikNdTot19Vjovw7yt1XryDplmDfg5S/ninab+bz4PK8Z12TzKw2l0oz2yNYv87Mno5iM7PTzOwrM/smeu1EMxtvZjPN7Ekz6xRsa2Z2hplNMLMfo301KrIcD5nZlcH67mb2fY6815vZkGC9U3TsdaL1H81smyD9QjN7I4p/b2bjEvv70syOj+K9ovekcZbjtoqO0yF47U4zGxZsO8XMKoL0iWbWJ8u+Vjazl8zs1hr8v2RJ51Jp6ahjmWXdaD/58nSM8qwYvPaemR2eJW9zM7vKzD4LXnvWzE4N1k8zs+ejuJGZTTJXx/OVoUlUhm6J1/PtO9sy28y2Dtb/aGb/M7Ofou265tn2bTM7KVg/3szeieJ2ZjbXqj53WOrvUmnpqMPlcJ2Umd1rZlfkSNvQXJ1sk+c81zJXR/c2s8lZ0m839xkw2sxOWI7/V5byWSotHXXwfTM7JFg/wtz1rNB2b5rZsYnXiq3PD5jZoMRrvSx73cgsb1tVfU0u25nZD4nXppvZDlHcyMwuMLOvzWyGmT1qZu3zHGuquc+EzPrlZvZIFBdT31ka5lJp6ajTabmujjSzc4O0nmb2i1Xdh+erZ92icjQpxf9lXc5RsaakfeS6L2f0les6vbFcV/Cr5LqMrS73eJlHEvs4QK5VbCu5Fqc/Rq93kesm3iXHsbvLtTxnfCT3S+nKOfJXZIk3yZOeKy2Zvr1cN7375Lp6vydp18R2ufbdXa4rT9it/uPo9Ywj5Lrt/Cj3K+0dQkNSyjpWHdPkWomPk+uyvYNcK+6bQZ7M8RZI+rNc63DGELleEu2i5SBJz0Zpa0TLJpImybVIX6rie4/l23fSFnI9msZH6/vL/RJ7oFzX9TcUbw1Pyva5lKnPm8p1yT1Y7pflLyWdVuQ5oHxxncx/ncw4VW6I5AeK947aVu49uVTuOviJlu49dYtcPV2Q5Zy2lXvvbs+Shoah1NfRZL1ZQ67nYXVVtz4nrSp3rf5G7pfcltHrjeXOrYPctW+ypH/K/UIrSe/LDfvYL8rbV9KvcverkvSn6LVd5X7Z/UnuuptNO7n3ONd1spj6DnBdzb5tc7leFIXqWca3cvX9Hkmr5Ch/zavllpBKc78K/mxm35r7hX8Fq2qh+l2Qd6iZXRustzKzhVb1i6dZvPfAqWb2cpHl+DqxbdNof92y5N3DXKvVZlFZ7zCzJVb1a+8DZvZvM2tt7tfjr83s1yht5ehcD4+O0T/a9o4o/c7ouMdH6YdF+VeJ0t80s1vMrIWZbWWuZe+LKO1iq2rdyiwP2tIt4TKz9cy1hq1Wg/+XLOlcKi0ddSyzFNOjQmb2BzObZmaLouXEHPnam9n5ZrZ98Foncz2GlkTLi2bWLErrGR3/GTNrG53bl1n2n6tHRb59h0sbM/vEzP4SvPasxX9lamRm8y13r4rF5n4RyqyvF5WpwtwvaRb9n61g7vNoupntWc3/D5b0L5WWjjpcLtfJraJ9NDG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      "text/plain": [
       "<Figure size 1440x864 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize = (20,12))\n",
    "k = 0\n",
    "print(\"Ground truth: \",Y_label[k])\n",
    "print(\"the probability for a bag including number 9: \",Y_hat_label[k])\n",
    "for i in range(tdata.shape[0]):\n",
    "    plt.subplot(6,5,i+1)\n",
    "    plt.imshow(tdata[i,:,:],\"gray\")\n",
    "    plt.title(\"Prob: \" + str(Y_hat_prob[i]), color = \"white\")\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.5"
  },
  "vscode": {
   "interpreter": {
    "hash": "ad2bdc8ecc057115af97d19610ffacc2b4e99fae6737bb82f5d7fb13d2f2c186"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}