{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.10","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# AML21: 01 Deep Neural Network for MNIST\nBased on https://github.com/Atcold/pytorch-Deep-Learning\n","metadata":{}},{"cell_type":"markdown","source":"## Data and Libraries","metadata":{}},{"cell_type":"code","source":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom torchvision import datasets, transforms\nimport matplotlib.pyplot as plt\nimport numpy\n\n# this 'device' will be used for training our model\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\nprint(device)","metadata":{"execution":{"iopub.status.busy":"2021-10-04T07:43:25.310329Z","iopub.execute_input":"2021-10-04T07:43:25.311087Z","iopub.status.idle":"2021-10-04T07:43:29.963263Z","shell.execute_reply.started":"2021-10-04T07:43:25.310995Z","shell.execute_reply":"2021-10-04T07:43:29.962490Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"cuda:0\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### **Load the MNIST dataset**\nObserve that we set `shuffle=True`, which means that data is randomized","metadata":{}},{"cell_type":"code","source":"input_size  = 28*28   # images are 28x28 pixels\noutput_size = 10      # there are 10 classes\n\ntrain_loader = torch.utils.data.DataLoader(\n    datasets.MNIST('../data', train=True, download=True,\n                   transform=transforms.Compose([\n                       transforms.ToTensor(),\n                       transforms.Normalize((0.1307,), (0.3081,))\n                   ])),\n    batch_size=64, shuffle=True)\n\ntest_loader = torch.utils.data.DataLoader(\n    datasets.MNIST('../data', train=False, transform=transforms.Compose([\n                       transforms.ToTensor(),\n                       transforms.Normalize((0.1307,), (0.3081,))\n                   ])),\n    batch_size=1000, shuffle=True)","metadata":{"execution":{"iopub.status.busy":"2021-10-04T07:43:34.040407Z","iopub.execute_input":"2021-10-04T07:43:34.040688Z","iopub.status.idle":"2021-10-04T07:43:35.312948Z","shell.execute_reply.started":"2021-10-04T07:43:34.040657Z","shell.execute_reply":"2021-10-04T07:43:35.312206Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stdout","text":"Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ../data/MNIST/raw/train-images-idx3-ubyte.gz\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"0it [00:00, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f6f81a57526f47868f337388cb5e99a7"}},"metadata":{}},{"name":"stdout","text":"Extracting ../data/MNIST/raw/train-images-idx3-ubyte.gz to ../data/MNIST/raw\nDownloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ../data/MNIST/raw/train-labels-idx1-ubyte.gz\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"0it [00:00, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"556c415011b34a9ea150cc234730469c"}},"metadata":{}},{"name":"stdout","text":"Extracting ../data/MNIST/raw/train-labels-idx1-ubyte.gz to ../data/MNIST/raw\nDownloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ../data/MNIST/raw/t10k-images-idx3-ubyte.gz\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"0it [00:00, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b41042e2e9e24ca58dfe092f999dddf3"}},"metadata":{}},{"name":"stdout","text":"Extracting ../data/MNIST/raw/t10k-images-idx3-ubyte.gz to ../data/MNIST/raw\nDownloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ../data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"0it [00:00, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"fb917fe2fc82439fb3a784adcd335f68"}},"metadata":{}},{"name":"stdout","text":"Extracting ../data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ../data/MNIST/raw\nProcessing...\nDone!\n","output_type":"stream"},{"name":"stderr","text":"/opt/conda/lib/python3.7/site-packages/torchvision/datasets/mnist.py:480: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  /pytorch/torch/csrc/utils/tensor_numpy.cpp:141.)\n  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n","output_type":"stream"}]},{"cell_type":"code","source":"# show some training images\nplt.figure(figsize=(16, 4))\n\n# fetch a batch of train images; RANDOM\nimage_batch, label_batch = next(iter(train_loader))\n\nfor i in range(20):\n    image = image_batch[i]\n    label = label_batch[i].item()\n    plt.subplot(2, 10, i + 1)\n    #image, label = train_loader.dataset.__getitem__(i)\n    plt.imshow(image.squeeze().numpy())\n    plt.axis('off')\n    plt.title(label)","metadata":{"execution":{"iopub.status.busy":"2021-10-04T07:43:38.780311Z","iopub.execute_input":"2021-10-04T07:43:38.780985Z","iopub.status.idle":"2021-10-04T07:43:39.776063Z","shell.execute_reply.started":"2021-10-04T07:43:38.780948Z","shell.execute_reply":"2021-10-04T07:43:39.775354Z"},"trusted":true},"execution_count":3,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1152x288 with 20 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"## A 2-hidden layer Fully Connected Neural Network\n","metadata":{}},{"cell_type":"markdown","source":"### Helper functions for training and testing","metadata":{}},{"cell_type":"code","source":"# function to count number of parameters\ndef get_n_params(model):\n    np=0\n    for p in list(model.parameters()):\n        np += p.nelement()\n    return np\n\naccuracy_list = []\n# we pass a model object to this trainer, and it trains this model for one epoch\ndef train(epoch, model):\n    model.train()\n    for batch_idx, (data, target) in enumerate(train_loader):\n        # send to device\n        data, target = data.to(device), target.to(device)\n        \n        optimizer.zero_grad()\n        output = model(data)\n        loss = F.nll_loss(output, target)\n        loss.backward()\n        optimizer.step()\n        if batch_idx % 100 == 0:\n            print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n                epoch, batch_idx * len(data), len(train_loader.dataset),\n                100. * batch_idx / len(train_loader), loss.item()))\n            \ndef test(model):\n    model.eval()\n    test_loss = 0\n    correct = 0\n    for data, target in test_loader:\n        # send to device\n        data, target = data.to(device), target.to(device)\n        \n        output = model(data)\n        test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss\n        pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability                                                                 \n        correct += pred.eq(target.data.view_as(pred)).cpu().sum().item()\n\n    test_loss /= len(test_loader.dataset)\n    accuracy = 100. * correct / len(test_loader.dataset)\n    accuracy_list.append(accuracy)\n    print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\n'.format(\n        test_loss, correct, len(test_loader.dataset),\n        accuracy))","metadata":{"execution":{"iopub.status.busy":"2021-10-04T07:43:59.195463Z","iopub.execute_input":"2021-10-04T07:43:59.195913Z","iopub.status.idle":"2021-10-04T07:43:59.207531Z","shell.execute_reply.started":"2021-10-04T07:43:59.195880Z","shell.execute_reply":"2021-10-04T07:43:59.206541Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"markdown","source":"### Defining the Fully Connected Network","metadata":{}},{"cell_type":"code","source":"class FC2Layer(nn.Module):\n    def __init__(self, input_size, output_size):\n        super(FC2Layer, self).__init__()\n        self.input_size = input_size\n        self.network = nn.Sequential(\n            nn.Linear(input_size, 200), \n            nn.ReLU(), \n            nn.Linear(200,100),\n            nn.ReLU(),\n            nn.Linear(100,60), \n            nn.ReLU(), \n            nn.Linear(60, output_size), \n            nn.LogSoftmax(dim=1)\n        )\n\n    def forward(self, x):\n        x = x.view(-1, self.input_size)\n        return self.network(x)","metadata":{"execution":{"iopub.status.busy":"2021-10-04T07:44:07.035220Z","iopub.execute_input":"2021-10-04T07:44:07.035487Z","iopub.status.idle":"2021-10-04T07:44:07.042180Z","shell.execute_reply.started":"2021-10-04T07:44:07.035455Z","shell.execute_reply":"2021-10-04T07:44:07.041479Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"markdown","source":"### Train the Network","metadata":{}},{"cell_type":"code","source":"print(\"Training on \", device)\nmodel_fnn = FC2Layer(input_size, output_size)\nmodel_fnn.to(device)\noptimizer = optim.SGD(model_fnn.parameters(), lr=0.01, momentum=0.5)\nprint('Number of parameters: {}'.format(get_n_params(model_fnn)))\n\nfor epoch in range(0, 10):\n    train(epoch, model_fnn)\n    test(model_fnn)","metadata":{"scrolled":true,"execution":{"iopub.status.busy":"2021-10-04T07:44:13.536295Z","iopub.execute_input":"2021-10-04T07:44:13.536552Z","iopub.status.idle":"2021-10-04T07:46:29.162879Z","shell.execute_reply.started":"2021-10-04T07:44:13.536523Z","shell.execute_reply":"2021-10-04T07:46:29.162123Z"},"trusted":true},"execution_count":6,"outputs":[{"name":"stdout","text":"Training on  cuda:0\nNumber of parameters: 183770\nTrain Epoch: 0 [0/60000 (0%)]\tLoss: 2.306556\nTrain Epoch: 0 [6400/60000 (11%)]\tLoss: 2.177184\nTrain Epoch: 0 [12800/60000 (21%)]\tLoss: 1.290147\nTrain Epoch: 0 [19200/60000 (32%)]\tLoss: 0.755577\nTrain Epoch: 0 [25600/60000 (43%)]\tLoss: 0.440915\nTrain Epoch: 0 [32000/60000 (53%)]\tLoss: 0.562211\nTrain Epoch: 0 [38400/60000 (64%)]\tLoss: 0.252071\nTrain Epoch: 0 [44800/60000 (75%)]\tLoss: 0.517577\nTrain Epoch: 0 [51200/60000 (85%)]\tLoss: 0.182727\nTrain Epoch: 0 [57600/60000 (96%)]\tLoss: 0.118172\n\nTest set: Average loss: 0.2922, Accuracy: 9142/10000 (91%)\n\nTrain Epoch: 1 [0/60000 (0%)]\tLoss: 0.274308\nTrain Epoch: 1 [6400/60000 (11%)]\tLoss: 0.183375\nTrain Epoch: 1 [12800/60000 (21%)]\tLoss: 0.226720\nTrain Epoch: 1 [19200/60000 (32%)]\tLoss: 0.272361\nTrain Epoch: 1 [25600/60000 (43%)]\tLoss: 0.397380\nTrain Epoch: 1 [32000/60000 (53%)]\tLoss: 0.206675\nTrain Epoch: 1 [38400/60000 (64%)]\tLoss: 0.181382\nTrain Epoch: 1 [44800/60000 (75%)]\tLoss: 0.211664\nTrain Epoch: 1 [51200/60000 (85%)]\tLoss: 0.189578\nTrain Epoch: 1 [57600/60000 (96%)]\tLoss: 0.245390\n\nTest set: Average loss: 0.1939, Accuracy: 9418/10000 (94%)\n\nTrain Epoch: 2 [0/60000 (0%)]\tLoss: 0.109253\nTrain Epoch: 2 [6400/60000 (11%)]\tLoss: 0.100005\nTrain Epoch: 2 [12800/60000 (21%)]\tLoss: 0.141641\nTrain Epoch: 2 [19200/60000 (32%)]\tLoss: 0.134167\nTrain Epoch: 2 [25600/60000 (43%)]\tLoss: 0.176373\nTrain Epoch: 2 [32000/60000 (53%)]\tLoss: 0.222804\nTrain Epoch: 2 [38400/60000 (64%)]\tLoss: 0.079777\nTrain Epoch: 2 [44800/60000 (75%)]\tLoss: 0.196300\nTrain Epoch: 2 [51200/60000 (85%)]\tLoss: 0.167849\nTrain Epoch: 2 [57600/60000 (96%)]\tLoss: 0.114829\n\nTest set: Average loss: 0.1536, Accuracy: 9545/10000 (95%)\n\nTrain Epoch: 3 [0/60000 (0%)]\tLoss: 0.183243\nTrain Epoch: 3 [6400/60000 (11%)]\tLoss: 0.086997\nTrain Epoch: 3 [12800/60000 (21%)]\tLoss: 0.105706\nTrain Epoch: 3 [19200/60000 (32%)]\tLoss: 0.146819\nTrain Epoch: 3 [25600/60000 (43%)]\tLoss: 0.223931\nTrain Epoch: 3 [32000/60000 (53%)]\tLoss: 0.118980\nTrain Epoch: 3 [38400/60000 (64%)]\tLoss: 0.081233\nTrain Epoch: 3 [44800/60000 (75%)]\tLoss: 0.078058\nTrain Epoch: 3 [51200/60000 (85%)]\tLoss: 0.229820\nTrain Epoch: 3 [57600/60000 (96%)]\tLoss: 0.056344\n\nTest set: Average loss: 0.1339, Accuracy: 9591/10000 (96%)\n\nTrain Epoch: 4 [0/60000 (0%)]\tLoss: 0.040184\nTrain Epoch: 4 [6400/60000 (11%)]\tLoss: 0.072593\nTrain Epoch: 4 [12800/60000 (21%)]\tLoss: 0.073783\nTrain Epoch: 4 [19200/60000 (32%)]\tLoss: 0.044806\nTrain Epoch: 4 [25600/60000 (43%)]\tLoss: 0.154575\nTrain Epoch: 4 [32000/60000 (53%)]\tLoss: 0.110681\nTrain Epoch: 4 [38400/60000 (64%)]\tLoss: 0.042032\nTrain Epoch: 4 [44800/60000 (75%)]\tLoss: 0.163611\nTrain Epoch: 4 [51200/60000 (85%)]\tLoss: 0.089801\nTrain Epoch: 4 [57600/60000 (96%)]\tLoss: 0.031802\n\nTest set: Average loss: 0.1071, Accuracy: 9660/10000 (97%)\n\nTrain Epoch: 5 [0/60000 (0%)]\tLoss: 0.124848\nTrain Epoch: 5 [6400/60000 (11%)]\tLoss: 0.026435\nTrain Epoch: 5 [12800/60000 (21%)]\tLoss: 0.030513\nTrain Epoch: 5 [19200/60000 (32%)]\tLoss: 0.132711\nTrain Epoch: 5 [25600/60000 (43%)]\tLoss: 0.063918\nTrain Epoch: 5 [32000/60000 (53%)]\tLoss: 0.145042\nTrain Epoch: 5 [38400/60000 (64%)]\tLoss: 0.038947\nTrain Epoch: 5 [44800/60000 (75%)]\tLoss: 0.111130\nTrain Epoch: 5 [51200/60000 (85%)]\tLoss: 0.170105\nTrain Epoch: 5 [57600/60000 (96%)]\tLoss: 0.063851\n\nTest set: Average loss: 0.0982, Accuracy: 9708/10000 (97%)\n\nTrain Epoch: 6 [0/60000 (0%)]\tLoss: 0.048078\nTrain Epoch: 6 [6400/60000 (11%)]\tLoss: 0.118412\nTrain Epoch: 6 [12800/60000 (21%)]\tLoss: 0.047448\nTrain Epoch: 6 [19200/60000 (32%)]\tLoss: 0.038427\nTrain Epoch: 6 [25600/60000 (43%)]\tLoss: 0.040424\nTrain Epoch: 6 [32000/60000 (53%)]\tLoss: 0.057466\nTrain Epoch: 6 [38400/60000 (64%)]\tLoss: 0.032205\nTrain Epoch: 6 [44800/60000 (75%)]\tLoss: 0.066095\nTrain Epoch: 6 [51200/60000 (85%)]\tLoss: 0.059640\nTrain Epoch: 6 [57600/60000 (96%)]\tLoss: 0.173306\n\nTest set: Average loss: 0.0955, Accuracy: 9716/10000 (97%)\n\nTrain Epoch: 7 [0/60000 (0%)]\tLoss: 0.121431\nTrain Epoch: 7 [6400/60000 (11%)]\tLoss: 0.058270\nTrain Epoch: 7 [12800/60000 (21%)]\tLoss: 0.042627\nTrain Epoch: 7 [19200/60000 (32%)]\tLoss: 0.018401\nTrain Epoch: 7 [25600/60000 (43%)]\tLoss: 0.014917\nTrain Epoch: 7 [32000/60000 (53%)]\tLoss: 0.073664\nTrain Epoch: 7 [38400/60000 (64%)]\tLoss: 0.039156\nTrain Epoch: 7 [44800/60000 (75%)]\tLoss: 0.046560\nTrain Epoch: 7 [51200/60000 (85%)]\tLoss: 0.037850\nTrain Epoch: 7 [57600/60000 (96%)]\tLoss: 0.035984\n\nTest set: Average loss: 0.0949, Accuracy: 9706/10000 (97%)\n\nTrain Epoch: 8 [0/60000 (0%)]\tLoss: 0.033080\nTrain Epoch: 8 [6400/60000 (11%)]\tLoss: 0.011930\nTrain Epoch: 8 [12800/60000 (21%)]\tLoss: 0.012878\nTrain Epoch: 8 [19200/60000 (32%)]\tLoss: 0.021536\nTrain Epoch: 8 [25600/60000 (43%)]\tLoss: 0.071350\nTrain Epoch: 8 [32000/60000 (53%)]\tLoss: 0.037414\nTrain Epoch: 8 [38400/60000 (64%)]\tLoss: 0.051778\nTrain Epoch: 8 [44800/60000 (75%)]\tLoss: 0.024658\nTrain Epoch: 8 [51200/60000 (85%)]\tLoss: 0.032960\nTrain Epoch: 8 [57600/60000 (96%)]\tLoss: 0.023755\n\nTest set: Average loss: 0.0847, Accuracy: 9735/10000 (97%)\n\nTrain Epoch: 9 [0/60000 (0%)]\tLoss: 0.012284\nTrain Epoch: 9 [6400/60000 (11%)]\tLoss: 0.009137\nTrain Epoch: 9 [12800/60000 (21%)]\tLoss: 0.055716\nTrain Epoch: 9 [19200/60000 (32%)]\tLoss: 0.024814\nTrain Epoch: 9 [25600/60000 (43%)]\tLoss: 0.022164\nTrain Epoch: 9 [32000/60000 (53%)]\tLoss: 0.007180\nTrain Epoch: 9 [38400/60000 (64%)]\tLoss: 0.007540\nTrain Epoch: 9 [44800/60000 (75%)]\tLoss: 0.038185\nTrain Epoch: 9 [51200/60000 (85%)]\tLoss: 0.046960\nTrain Epoch: 9 [57600/60000 (96%)]\tLoss: 0.033422\n\nTest set: Average loss: 0.0778, Accuracy: 9747/10000 (97%)\n\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### Show some predictions of the test network","metadata":{}},{"cell_type":"code","source":"def visualize_pred(img, pred_prob, real_label):\n    ''' Function for viewing an image and it's predicted classes.\n    '''\n    #pred_prob = pred_prob.data.numpy().squeeze()\n\n    fig, (ax1, ax2) = plt.subplots(figsize=(6,9), ncols=2)\n    ax1.imshow(img.numpy().squeeze())\n    ax1.axis('off')\n    pred_label = numpy.argmax(pred_prob)\n    ax1.set_title([real_label, pred_label])\n    \n    ax2.barh(numpy.arange(10), pred_prob)\n    ax2.set_aspect(0.1)\n    ax2.set_yticks(numpy.arange(10))\n    ax2.set_yticklabels(numpy.arange(10))\n    ax2.set_title('Prediction Probability')\n    ax2.set_xlim(0, 1.1)\n    plt.tight_layout()\n\n","metadata":{"scrolled":true,"execution":{"iopub.status.busy":"2021-10-04T07:46:37.610306Z","iopub.execute_input":"2021-10-04T07:46:37.610571Z","iopub.status.idle":"2021-10-04T07:46:37.620015Z","shell.execute_reply.started":"2021-10-04T07:46:37.610540Z","shell.execute_reply":"2021-10-04T07:46:37.619209Z"},"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"code","source":"model_fnn.to('cpu') \n\n# fetch a batch of test images\nimage_batch, label_batch = next(iter(test_loader))\n\n# Turn off gradients to speed up this part\nwith torch.no_grad():\n    log_pred_prob_batch = model_fnn(image_batch)\nfor i in range(10):\n    img = image_batch[i]\n    real_label = label_batch[i].item()\n    log_pred_prob = log_pred_prob_batch[i]\n    # Output of the network are log-probabilities, need to take exponential for probabilities\n    pred_prob = torch.exp(log_pred_prob).data.numpy().squeeze()\n    visualize_pred(img, pred_prob, real_label)","metadata":{"execution":{"iopub.status.busy":"2021-10-04T07:46:44.652046Z","iopub.execute_input":"2021-10-04T07:46:44.652784Z","iopub.status.idle":"2021-10-04T07:46:47.939347Z","shell.execute_reply.started":"2021-10-04T07:46:44.652748Z","shell.execute_reply":"2021-10-04T07:46:47.938679Z"},"trusted":true},"execution_count":8,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x648 with 2 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"## Network with Dropout","metadata":{}},{"cell_type":"code","source":"class FC2LayerDropout(nn.Module):\n    def __init__(self, input_size, output_size):\n        super(FC2LayerDropout, self).__init__()\n        self.input_size = input_size\n        self.network = nn.Sequential(\n            nn.Linear(input_size, 200),\n            nn.Dropout(0.25),\n            nn.ReLU(), \n            nn.Linear(200, 100),\n            nn.Dropout(0.25),\n            nn.ReLU(), \n            nn.Linear(100,60),\n            nn.ReLU(),\n            nn.Linear(60, output_size), \n            nn.LogSoftmax(dim=1)\n        )\n\n    def forward(self, x):\n        x = x.view(-1, self.input_size)\n        return self.network(x)","metadata":{"execution":{"iopub.status.busy":"2021-10-04T07:47:00.780210Z","iopub.execute_input":"2021-10-04T07:47:00.780482Z","iopub.status.idle":"2021-10-04T07:47:00.787582Z","shell.execute_reply.started":"2021-10-04T07:47:00.780452Z","shell.execute_reply":"2021-10-04T07:47:00.786791Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"code","source":"print(\"Training on \", device)\nmodel = FC2LayerDropout(input_size, output_size)\nmodel.to(device)\noptimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)\nprint('Number of parameters: {}'.format(get_n_params(model)))\n\nfor epoch in range(0, 10):\n    model.train() # model in training mode. Turns on dropout, batch-norm etc during training\n    train(epoch, model)\n    model.eval() # model in evaluation mode. Turn off dropout, batch-norm etc during validation/testing\n    test(model)","metadata":{"scrolled":true,"execution":{"iopub.status.busy":"2021-10-04T07:47:06.100209Z","iopub.execute_input":"2021-10-04T07:47:06.100469Z","iopub.status.idle":"2021-10-04T07:49:18.249457Z","shell.execute_reply.started":"2021-10-04T07:47:06.100440Z","shell.execute_reply":"2021-10-04T07:49:18.248039Z"},"trusted":true},"execution_count":10,"outputs":[{"name":"stdout","text":"Training on  cuda:0\nNumber of parameters: 183770\nTrain Epoch: 0 [0/60000 (0%)]\tLoss: 2.303042\nTrain Epoch: 0 [6400/60000 (11%)]\tLoss: 2.232655\nTrain Epoch: 0 [12800/60000 (21%)]\tLoss: 1.750074\nTrain Epoch: 0 [19200/60000 (32%)]\tLoss: 1.052040\nTrain Epoch: 0 [25600/60000 (43%)]\tLoss: 0.538419\nTrain Epoch: 0 [32000/60000 (53%)]\tLoss: 0.605327\nTrain Epoch: 0 [38400/60000 (64%)]\tLoss: 0.599045\nTrain Epoch: 0 [44800/60000 (75%)]\tLoss: 0.408984\nTrain Epoch: 0 [51200/60000 (85%)]\tLoss: 0.586994\nTrain Epoch: 0 [57600/60000 (96%)]\tLoss: 0.338343\n\nTest set: Average loss: 0.3262, Accuracy: 9040/10000 (90%)\n\nTrain Epoch: 1 [0/60000 (0%)]\tLoss: 0.416737\nTrain Epoch: 1 [6400/60000 (11%)]\tLoss: 0.392879\nTrain Epoch: 1 [12800/60000 (21%)]\tLoss: 0.272427\nTrain Epoch: 1 [19200/60000 (32%)]\tLoss: 0.236031\nTrain Epoch: 1 [25600/60000 (43%)]\tLoss: 0.406714\nTrain Epoch: 1 [32000/60000 (53%)]\tLoss: 0.402870\nTrain Epoch: 1 [38400/60000 (64%)]\tLoss: 0.303852\nTrain Epoch: 1 [44800/60000 (75%)]\tLoss: 0.196804\nTrain Epoch: 1 [51200/60000 (85%)]\tLoss: 0.449149\nTrain Epoch: 1 [57600/60000 (96%)]\tLoss: 0.218142\n\nTest set: Average loss: 0.2100, Accuracy: 9365/10000 (94%)\n\nTrain Epoch: 2 [0/60000 (0%)]\tLoss: 0.211032\nTrain Epoch: 2 [6400/60000 (11%)]\tLoss: 0.210309\nTrain Epoch: 2 [12800/60000 (21%)]\tLoss: 0.338311\nTrain Epoch: 2 [19200/60000 (32%)]\tLoss: 0.130013\nTrain Epoch: 2 [25600/60000 (43%)]\tLoss: 0.380897\nTrain Epoch: 2 [32000/60000 (53%)]\tLoss: 0.233913\nTrain Epoch: 2 [38400/60000 (64%)]\tLoss: 0.429006\nTrain Epoch: 2 [44800/60000 (75%)]\tLoss: 0.248526\nTrain Epoch: 2 [51200/60000 (85%)]\tLoss: 0.128082\nTrain Epoch: 2 [57600/60000 (96%)]\tLoss: 0.178169\n\nTest set: Average loss: 0.1601, Accuracy: 9524/10000 (95%)\n\nTrain Epoch: 3 [0/60000 (0%)]\tLoss: 0.194061\nTrain Epoch: 3 [6400/60000 (11%)]\tLoss: 0.272792\nTrain Epoch: 3 [12800/60000 (21%)]\tLoss: 0.186156\nTrain Epoch: 3 [19200/60000 (32%)]\tLoss: 0.300879\nTrain Epoch: 3 [25600/60000 (43%)]\tLoss: 0.061202\nTrain Epoch: 3 [32000/60000 (53%)]\tLoss: 0.267650\nTrain Epoch: 3 [38400/60000 (64%)]\tLoss: 0.132290\nTrain Epoch: 3 [44800/60000 (75%)]\tLoss: 0.165557\nTrain Epoch: 3 [51200/60000 (85%)]\tLoss: 0.169942\nTrain Epoch: 3 [57600/60000 (96%)]\tLoss: 0.233818\n\nTest set: Average loss: 0.1276, Accuracy: 9618/10000 (96%)\n\nTrain Epoch: 4 [0/60000 (0%)]\tLoss: 0.090799\nTrain Epoch: 4 [6400/60000 (11%)]\tLoss: 0.092053\nTrain Epoch: 4 [12800/60000 (21%)]\tLoss: 0.124873\nTrain Epoch: 4 [19200/60000 (32%)]\tLoss: 0.314530\nTrain Epoch: 4 [25600/60000 (43%)]\tLoss: 0.159503\nTrain Epoch: 4 [32000/60000 (53%)]\tLoss: 0.080379\nTrain Epoch: 4 [38400/60000 (64%)]\tLoss: 0.155144\nTrain Epoch: 4 [44800/60000 (75%)]\tLoss: 0.133378\nTrain Epoch: 4 [51200/60000 (85%)]\tLoss: 0.100745\nTrain Epoch: 4 [57600/60000 (96%)]\tLoss: 0.148948\n\nTest set: Average loss: 0.1130, Accuracy: 9661/10000 (97%)\n\nTrain Epoch: 5 [0/60000 (0%)]\tLoss: 0.127338\nTrain Epoch: 5 [6400/60000 (11%)]\tLoss: 0.076443\nTrain Epoch: 5 [12800/60000 (21%)]\tLoss: 0.087274\nTrain Epoch: 5 [19200/60000 (32%)]\tLoss: 0.032357\nTrain Epoch: 5 [25600/60000 (43%)]\tLoss: 0.060095\nTrain Epoch: 5 [32000/60000 (53%)]\tLoss: 0.136934\nTrain Epoch: 5 [38400/60000 (64%)]\tLoss: 0.107147\nTrain Epoch: 5 [44800/60000 (75%)]\tLoss: 0.264836\nTrain Epoch: 5 [51200/60000 (85%)]\tLoss: 0.116272\nTrain Epoch: 5 [57600/60000 (96%)]\tLoss: 0.305326\n\nTest set: Average loss: 0.1035, Accuracy: 9698/10000 (97%)\n\nTrain Epoch: 6 [0/60000 (0%)]\tLoss: 0.139701\nTrain Epoch: 6 [6400/60000 (11%)]\tLoss: 0.136242\nTrain Epoch: 6 [12800/60000 (21%)]\tLoss: 0.097588\nTrain Epoch: 6 [19200/60000 (32%)]\tLoss: 0.135472\nTrain Epoch: 6 [25600/60000 (43%)]\tLoss: 0.136464\nTrain Epoch: 6 [32000/60000 (53%)]\tLoss: 0.049524\nTrain Epoch: 6 [38400/60000 (64%)]\tLoss: 0.092915\nTrain Epoch: 6 [44800/60000 (75%)]\tLoss: 0.049124\nTrain Epoch: 6 [51200/60000 (85%)]\tLoss: 0.119248\nTrain Epoch: 6 [57600/60000 (96%)]\tLoss: 0.191502\n\nTest set: Average loss: 0.0926, Accuracy: 9719/10000 (97%)\n\nTrain Epoch: 7 [0/60000 (0%)]\tLoss: 0.062154\nTrain Epoch: 7 [6400/60000 (11%)]\tLoss: 0.089640\nTrain Epoch: 7 [12800/60000 (21%)]\tLoss: 0.137574\nTrain Epoch: 7 [19200/60000 (32%)]\tLoss: 0.195233\nTrain Epoch: 7 [25600/60000 (43%)]\tLoss: 0.032105\nTrain Epoch: 7 [32000/60000 (53%)]\tLoss: 0.069613\nTrain Epoch: 7 [38400/60000 (64%)]\tLoss: 0.073453\nTrain Epoch: 7 [44800/60000 (75%)]\tLoss: 0.099157\nTrain Epoch: 7 [51200/60000 (85%)]\tLoss: 0.060516\nTrain Epoch: 7 [57600/60000 (96%)]\tLoss: 0.142676\n\nTest set: Average loss: 0.0867, Accuracy: 9741/10000 (97%)\n\nTrain Epoch: 8 [0/60000 (0%)]\tLoss: 0.049860\nTrain Epoch: 8 [6400/60000 (11%)]\tLoss: 0.055813\nTrain Epoch: 8 [12800/60000 (21%)]\tLoss: 0.123922\nTrain Epoch: 8 [19200/60000 (32%)]\tLoss: 0.048202\nTrain Epoch: 8 [25600/60000 (43%)]\tLoss: 0.187207\nTrain Epoch: 8 [32000/60000 (53%)]\tLoss: 0.275413\nTrain Epoch: 8 [38400/60000 (64%)]\tLoss: 0.053153\nTrain Epoch: 8 [44800/60000 (75%)]\tLoss: 0.031433\nTrain Epoch: 8 [51200/60000 (85%)]\tLoss: 0.107852\nTrain Epoch: 8 [57600/60000 (96%)]\tLoss: 0.021837\n\nTest set: Average loss: 0.0824, Accuracy: 9757/10000 (98%)\n\nTrain Epoch: 9 [0/60000 (0%)]\tLoss: 0.073799\nTrain Epoch: 9 [6400/60000 (11%)]\tLoss: 0.075757\nTrain Epoch: 9 [12800/60000 (21%)]\tLoss: 0.164681\nTrain Epoch: 9 [19200/60000 (32%)]\tLoss: 0.098372\nTrain Epoch: 9 [25600/60000 (43%)]\tLoss: 0.107419\nTrain Epoch: 9 [32000/60000 (53%)]\tLoss: 0.069053\nTrain Epoch: 9 [38400/60000 (64%)]\tLoss: 0.100473\nTrain Epoch: 9 [44800/60000 (75%)]\tLoss: 0.137091\nTrain Epoch: 9 [51200/60000 (85%)]\tLoss: 0.084864\nTrain Epoch: 9 [57600/60000 (96%)]\tLoss: 0.040560\n\nTest set: Average loss: 0.0776, Accuracy: 9767/10000 (98%)\n\n","output_type":"stream"}]},{"cell_type":"code","source":"model.to('cpu') \n\n# fetch a batch of test images\nimage_batch, label_batch = next(iter(test_loader))\n\n# Turn off gradients to speed up this part\nwith torch.no_grad():\n    log_pred_prob_batch = model(image_batch)\nfor i in range(10):\n    img = image_batch[i]\n    real_label = label_batch[i].item()\n    log_pred_prob = log_pred_prob_batch[i]\n    # Output of the network are log-probabilities, need to take exponential for probabilities\n    pred_prob = torch.exp(log_pred_prob).data.numpy().squeeze()\n    visualize_pred(img, pred_prob, real_label)","metadata":{"scrolled":true,"execution":{"iopub.status.busy":"2021-10-04T07:55:08.806150Z","iopub.execute_input":"2021-10-04T07:55:08.806428Z","iopub.status.idle":"2021-10-04T07:55:11.830614Z","shell.execute_reply.started":"2021-10-04T07:55:08.806399Z","shell.execute_reply":"2021-10-04T07:55:11.829975Z"},"trusted":true},"execution_count":11,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x648 with 2 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure 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SrTFyKFM76VPhjarG4+gzMyskjyCqoC1r78vWev/g/Qnli/r4h00Jw97JFm77PCJydrIM3yZuZlVg0dQZmZWSQ4oMzOrJAeUmZlVkgPKzMwqyQFlZmaV5IAyawJJLZIekHRD2b2Y1ZUvM6+4d15xdLL2yCHf79E+P3bY7cnafVeMS9ba587r0fH6qOOBWcBbym7ErK48gjIrmKRRwH7ABWX3YlZnDiiz4p0FfAlY0Vmx8Rt129raerUxszpxQJkVSNL+wLMRMSO1TkRMjogJETFhxIgRvdidWb04oMyKtSNwgKS5wBXA7pJ+Vm5LZvXkgDIrUER8OSJGRUQrcChwa0QcUXJbZrXkgDIzs0ryZeYVt+npf07Wjv7A7snaeaNvTdZOGf7HZG27Q3ZN1kZ+25eZr46IuB24veQ2zGrLIygzM6skB5SZmVWSA8rMzCrJAWVmZpXkgDIr0UMLF9N6yq/KbsOskhxQZmZWSb7MvOKWP/9Csjbtlx9Ib/j59GXmXXnXfo8na698u0e7NDPrEY+gzMyskhxQZgWSNEjSfZL+KGmmpG+U3ZNZXXmKz6xYrwO7R8TLkvoDd0r6dUTcW3ZjZnXjgDIrUEQE8HL+a//8J8rryKy+PMVnVjBJLZIeBJ4FbomIaSW3ZFZLDiizgkXE8ojYGhgFbC9p88Z64zfqLl+yuJQezerAAWXWJBGxCLgN2LvD8je+Ubdl8NBSejOrAweUWYEkjZC0Xn57bWBP4NFSmzKrKV8kYVasDYFLJLWQvQC8KiJuKLkns1pyQJkVKCL+BGxTdh9mawJP8ZmZWSU5oMzMrJIcUGYl2mLkUOaesV/ZbZhVUp89B/X4T7ZN1kb8dmCytt5P7mlGO0kt4zZO1o445HeFH++Rp9+erI2lrfDjmZmleARlZmaV5IAyM7NKckCZmVklOaDMzKySHFBmZlZJDiizAkkaLek2SY/k36h7fNk9mdVVn73MfNDjg5K1H3zjrGTtkL0nJWvvPGt5stZv3rPpZgYOSJYmXv/HZO2Y9R9L77OHNpqc7sW6pR04KSLul7QuMEPSLRHxSNmNmdWNR1BmBYqIpyLi/vz234BZwMhyuzKrJweUWZNIaiX74NhpHZa/8YWFbW1+87NZigPKrAkkDQGuBU6IiJcaa41fWDhixIhyGjSrAQeUWcEk9ScLp0sj4rqy+zGrKweUWYEkCbgQmBUR3y27H7M667NX8W186ZPJ2q2HjE/WZu5yfnqnu6RLJz/1gWTt7QNeSta+sP7jydqK9OG6tOXFxyVr4x6dl6y19/B4fcyOwCeBhyQ9mC/794i4sbyWzOqpzwaUWTNExJ2Ayu7DbE3gKT4zM6skB5SZmVWSA8rMzCrJAWVmZpXkgDIzs0rqs1fxtc+Zm6z9+PIPJWufOfqhZG3wWv2Tte9seHe3+vr/0q8hFq9YmqydMG//ZG3c9+cka+1PP9O9tszMmswjKDMzqyQHlJmZVZIDyqxAki6S9Kykh8vuxazuHFBmxboY2LvsJszWBA4oswJFxFTghbL7MFsTOKDMzKyS+uxl5l0ZfXr6kvA9nz4pWdvr2LuStVM3mNGjXo56YmKy9tez3p2sDbnq3h4dz5pP0iRgEsCYMWNK7sasujyCMutl/kZds+5xQJmZWSU5oMwKJOly4B5gU0kLJH267J7M6srnoMwKFBGHld2D2ZrCIygzM6skB5SZmVWSp/hW07AL70nWZlyYzvsDeG8Pj7goWRmCLyU3szWXR1BmZlZJDigzM6skB5SZmVWSA8rMzCrJAWVmZpXkgDIzs0pyQJkVTNLekh6TNFvSKWX3Y1ZXDiizAklqAc4F9gE2Aw6TtFm5XZnVkwPKrFjbA7MjYk5ELAWuAA4suSezWnJAmRVrJDC/4fcF+bI3SJokabqk6W1tbb3anFmdOKDMepm/sNCsexxQZsVaCIxu+H1UvszMVpMDyqxYfwA2kbSxpAHAocCUknsyqyV/mrlZgSKiXdKxwE1AC3BRRMwsuS2zWnJAmRUsIm4Ebiy7D7O68xSfmZlVkgPKzMwqyQFlZmaV5IAyM7NKckCZmVklOaDMzKySHFBmZlZJDigzM6skB5SZmVWSA8rMzCrJH3VkVqIZM2a8LOmxsvtoMBx4ruwmcu6lc2tiL2M7W+iAMivXYxExoewmVpI0vSr9uJfO9aVeugyoW1ZcrWYd2MzMrCs+B2VmZpXkgDIr1+SyG+igSv24l871mV4UEc3cv5mZWY94BGVmZpXkgDLrBZL2lvSYpNmSTumkPlDSlXl9mqTWEnv5oqRHJP1J0u8kdXoJcG/00rDeRyWFpKZevdadfiR9PH98Zkq6rKxeJI2RdJukB/J/q32b1MdFkp6V9HCiLkln533+SdK2hR08IvzjH/808QdoAf4CvAMYAPwR2KzDOp8HzstvHwpcWWIvuwGD89ufK7OXfL11ganAvcCEkv+dNgEeANbPf9+gxF4mA5/Lb28GzG1SLx8EtgUeTtT3BX4NCNgBmFbUsT2CMmu+7YHZETEnIpYCVwAHdljnQOCS/PY1wERJzXibxyp7iYjbImJJ/uu9wKgm9NGtXnKnAd8GXmtSH6vTz2eBcyPiRYCIeLbEXgJ4S357KPBkMxqJiKnAC12sciDwk8jcC6wnacMiju2AMmu+kcD8ht8X5Ms6XSci2oHFwLCSemn0abJXx82wyl7y6aLREfGrJvWwWv0A7wLeJekuSfdK2rvEXr4OHCFpAXAj8IUm9bIqq/v/VLf5kyTMrFOSjgAmALuUdPy1gO8CR5Zx/IR+ZNN8u5KNLKdK2iIiFpXQy2HAxRFxpqT3Az+VtHlErCihl6bwCMqs+RYCoxt+H5Uv63QdSf3IpmyeL6kXJO0BfAU4ICJeb0If3ellXWBz4HZJc8nOb0xp4oUS3XlsFgBTImJZRPwV+DNZYJXRy6eBqwAi4h5gENln4/W2bv0/1RMOKLPm+wOwiaSNJQ0guwhiSod1pgD/nN8+GLg18jPQvd2LpG2AH5GFU7POsayyl4hYHBHDI6I1IlrJzocdEBHTy+gndz3Z6AlJw8mm/OaU1Ms8YGLey3iygGprQi+rMgX4VH413w7A4oh4qogde4rPrMkiol3SscBNZFdnXRQRMyX9JzA9IqYAF5JN0cwmOyF9aIm9fAcYAlydX6cxLyIOKKmXXtPNfm4C9pL0CLAcODkiCh/pdrOXk4DzJZ1IdsHEkc14USPpcrJQHp6f7zoV6J/3eR7Z+a99gdnAEuCowo7dnBdpZmZm/xhP8ZmZWSU5oMzMrJIcUGZmVkkOKDMzqyQHlJmZVZIDyszMKskBZWZmleSAMjOzSvo/oDC/Ispz/sQAAAAASUVORK5CYII=\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure 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r6P5Cxe01v7k2Jtaa31aSn7nX9yntbZyxZWjaGd99AbgQuD+XTcijStHUNKQJVkAPBf4dNe9SOPMgJKG78PA24B7pip6RV1pegwoaYiSPA+4tqqWt23jFXWl6TGgpOF6CrBvkkuBLwDPTPK5bluSxpMBJQ1RVf1DVS2oqsXA/sCpVXVgx21JY8mAkiT1ksvM19EvDmn/xPKlC5fOYidw+crftdZufvei1tqGK1pPj2iIquo04LSO25DGliMoSVIvGVCSpF4yoCRJvWRASZJ6yYCSJPWSASVJ6iWXmU/h8uMe3Vr7/m4fWMOe9x1+M2uw76Fva61t+19nzmInkjR8jqAkSb1kQElDlGTjJD9I8uMkFyT5p657ksaVU3zScN0JPLOqbkuyIXBGkq9X1dldNyaNGwNKGqKqKmD15ZM3bL6qu46k8eUUnzRkSeYlOQ+4FvhWVZ3TcUvSWDKgpCGrqlVV9VhgAbBrkp0n1r2irjQ96+0U37w1XMn0+Ccc1lrbaoPZXUp+5K3bttbu3OW21trVJ+00inaGasOvbdFa2+pTZ81eIyNSVTcn+Q6wF3D+hPsPAw4DWLJkidN/UgtHUNIQJdkmyRbN7fsCzwZ+3mlT0phab0dQ0og8GDgqyTwGbwC/WFVf7bgnaSwZUNIQVdVPgMd13Yc0FzjFJ0nqJQNKktRLBpQkqZfW23NQv/j7h7XW/tuGG81iJ2t20P2vaq/tfuTsNTICNz3+jtbaXve8pbW21eHjvwRd0to5gpIk9dJ6O4KS+uCnV97C4rd/res2pBm59P3PHenjO4KSJPWSASVJ6iUDSpLUSwaUNERJFib5TpKfNVfUfUPXPUnjak4vkvjty3ZrrS3f/9/XsGd/lpnPZVuu4ZPhv/1PH2qt7fPbN7XWNvti5xeuXQm8uarOTXI/YHmSb1XVz7puTBo3jqCkIaqq31TVuc3t3wIXAtt125U0ngwoaUSSLGbwwbHnTLr/DxcsXPW7WzrpTRoHBpQ0Akk2A04A3lhVt06sVdVhVbWkqpbM22TzbhqUxoABJQ1Zkg0ZhNPRVXVi1/1I48qAkoYoSYDDgQurqn2lh6S1mtOr+K5/TFprm8WVen12d93TWtv4+rtnsZN19hTgFcBPk5zX3Pc/qurk7lqSxtOcDihptlXVGUD7OyNJ0+YUnySplxxBSR169Habs2zEnwgtjStHUJKkXjKgJEm9ZEBJknrJc1A9d1vd2Vq7p2pGj7nr917bWtvg1+0f4DpsOz31ktbaRac+rLW26NQzR9GOpJ5xBCVJ6iUDSpLUSwaUNERJjkhybZLzu+5FGncGlDRcRwJ7dd2ENBcYUNIQVdXpwI1d9yHNBQaUJKmX5vQy823PWNlau/UVv2+t3X+DjYfey8O/cXBrbf4NG7bWdvjEb1prKy+5dEa9PJTzZrTfsN2xhtoirpm1PmZbkoOBgwEWLVrUcTdSfzmCkmbZxCvqbrPNNl23I/WWASVJ6iUDShqiJMcAZwE7JlmR5FVd9ySNqzl9DkqabVV1QNc9SHOFIyhJUi8ZUJKkXprTU3wbnfzD1tr+C588i53ADiyb0X7tC+UlaW5zBCVJ6iUDSpLUSwaUJKmXDChJUi8ZUJKkXjKgJEm9ZEBJQ5ZkryQXJbk4ydu77kcaVwaUNERJ5gGHAnsDjwQOSPLIbruSxpMBJQ3XrsDFVXVJVd0FfAHYr+OepLFkQEnDtR1wxYTvVzT3/UGSg5MsS7Lsuuuum9XmpHFiQEmzzAsWStNjQEnDdSWwcML3C5r7JK0jA0oarh8Cj0jykCT3AfYHlnbckzSW5vSnmUuzrapWJjkEOAWYBxxRVRd03JY0lgwoaciq6mTg5K77kMadU3ySpF4yoCRJvWRASZJ6yYCSJPWSASVJ6iUDSpLUSwaUJKmXDChJUi8ZUJKkXjKgJEm95EcdSR1avnz5bUku6rqPCbYGru+6iYa9TG0u9rL9VHcaUFK3LqqqJV03sVqSZX3px16mtj71ssaA+tY9x2VUB5YkaU08ByVJ6iUDSurWYV03MEmf+rGXqa03vaSqRvn4kiTNiCMoSVIvGVDSLEiyV5KLklyc5O1T1DdKcmxTPyfJ4g57+bskP0vykyTfTjLlEuDZ6GXCdi9OUklGunptOv0keWnz87kgyee76iXJoiTfSfKj5v/VPiPq44gk1yY5v6WeJP/R9PmTJLsM7eBV5Zdffo3wC5gH/Ap4KHAf4MfAIydt81rgE83t/YFjO+zlGcAmze2/6bKXZrv7AacDZwNLOv7/9AjgR8CWzfcP7LCXw4C/aW4/Erh0RL3sAewCnN9S3wf4OhBgN+CcYR3bEZQ0ersCF1fVJVV1F/AFYL9J2+wHHNXcPh7YM8ko/sxjrb1U1Xeq6nfNt2cDC0bQx7R6abwH+Ffg9yPqY136eTVwaFXdBFBV13bYSwH3b25vDlw1ikaq6nTgxjVssh/wf2rgbGCLJA8exrENKGn0tgOumPD9iua+KbepqpXALcBWHfUy0asYvDsehbX20kwXLayqr42oh3XqB9gB2CHJ95OcnWSvDnt5F3BgkhXAycDrR9TL2qzr79S0+UkSkqaU5EBgCfC0jo6/AfAh4KAujt9iPoNpvqczGFmenuTRVXVzB70cABxZVR9M8iTgs0l2rqp7OuhlJBxBSaN3JbBwwvcLmvum3CbJfAZTNjd01AtJngW8A9i3qu4cQR/T6eV+wM7AaUkuZXB+Y+kIF0pM52ezAlhaVXdX1a+BXzAIrC56eRXwRYCqOgvYmMFn4822af1OzYQBJY3eD4FHJHlIkvswWASxdNI2S4H/3tx+CXBqNWegZ7uXJI8DPskgnEZ1jmWtvVTVLVW1dVUtrqrFDM6H7VtVy7rop3ESg9ETSbZmMOV3SUe9XA7s2fSyE4OAum4EvazNUuAvm9V8uwG3VNVvhvHATvFJI1ZVK5McApzCYHXWEVV1QZJ3A8uqailwOIMpmosZnJDev8Ne/g3YDDiuWadxeVXt21Evs2aa/ZwC/FmSnwGrgLdW1dBHutPs5c3Ap5K8icGCiYNG8aYmyTEMQnnr5nzXO4ENmz4/weD81z7AxcDvgFcO7dijeZMmSdIfxyk+SVIvGVCSpF4yoCRJvWRASZJ6yYCSJPWSASVJ6iUDSpLUSwaUJKmX/i9AKqAbWjZgDQAAAABJRU5ErkJggg==\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure 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Does the Fully Connected Network use \"Visual Information\" ?","metadata":{}},{"cell_type":"code","source":"fixed_perm = torch.randperm(784) # Fix a permutation of the image pixels; We apply the same permutation to all images\n\n# show some training images\nplt.figure(figsize=(8, 8))\n\n# fetch a batch of train images; RANDOM\nimage_batch, label_batch = next(iter(train_loader))\n\nfor i in range(6):\n    image = image_batch[i]\n    image_perm = image.view(-1, 28*28).clone()\n    image_perm = image_perm[:, fixed_perm]\n    image_perm = image_perm.view(-1, 1, 28, 28)\n    \n    label = label_batch[i].item()\n    plt.subplot(3,4 , 2*i + 1)\n    #image, label = train_loader.dataset.__getitem__(i)\n    plt.imshow(image.squeeze().numpy())\n    plt.axis('off')\n    plt.title(label)\n    plt.subplot(3, 4, 2*i+2)\n    plt.imshow(image_perm.squeeze().numpy())\n    plt.axis('off')\n    plt.title(label)\n","metadata":{"execution":{"iopub.status.busy":"2021-10-04T07:55:35.392525Z","iopub.execute_input":"2021-10-04T07:55:35.392902Z","iopub.status.idle":"2021-10-04T07:55:36.104389Z","shell.execute_reply.started":"2021-10-04T07:55:35.392866Z","shell.execute_reply":"2021-10-04T07:55:36.103718Z"},"trusted":true},"execution_count":12,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 576x576 with 12 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"accuracy_list = []\n\ndef scramble_train(epoch, model, perm=torch.arange(0, 784).long()):\n    model.train()\n    for batch_idx, (data, target) in enumerate(train_loader):\n        # send to device\n        data, target = data.to(device), target.to(device)\n        \n        # permute pixels\n        data = data.view(-1, 28*28)\n        data = data[:, perm]\n        data = data.view(-1, 1, 28, 28)\n\n        optimizer.zero_grad()\n        output = model(data)\n        loss = F.nll_loss(output, target)\n        loss.backward()\n        optimizer.step()\n        if batch_idx % 100 == 0:\n            print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n                epoch, batch_idx * len(data), len(train_loader.dataset),\n                100. * batch_idx / len(train_loader), loss.item()))\n            \ndef scramble_test(model, perm=torch.arange(0, 784).long()):\n    model.eval()\n    test_loss = 0\n    correct = 0\n    for data, target in test_loader:\n        # send to device\n        data, target = data.to(device), target.to(device)\n        \n        # permute pixels\n        data = data.view(-1, 28*28)\n        data = data[:, perm]\n        data = data.view(-1, 1, 28, 28)\n        \n        output = model(data)\n        test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss                                                               \n        pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability                                                                 \n        correct += pred.eq(target.data.view_as(pred)).cpu().sum().item()\n\n    test_loss /= len(test_loader.dataset)\n    accuracy = 100. * correct / len(test_loader.dataset)\n    accuracy_list.append(accuracy)\n    print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\n'.format(\n        test_loss, correct, len(test_loader.dataset),\n        accuracy))","metadata":{"execution":{"iopub.status.busy":"2021-10-04T07:55:41.795357Z","iopub.execute_input":"2021-10-04T07:55:41.795752Z","iopub.status.idle":"2021-10-04T07:55:41.812869Z","shell.execute_reply.started":"2021-10-04T07:55:41.795719Z","shell.execute_reply":"2021-10-04T07:55:41.811764Z"},"trusted":true},"execution_count":13,"outputs":[]},{"cell_type":"code","source":"print(\"Training on \", device)\nmodel_fnn_2 = FC2Layer(input_size, output_size)\nmodel_fnn_2.to(device)\noptimizer = optim.SGD(model_fnn_2.parameters(), lr=0.01, momentum=0.5)\nprint('Number of parameters: {}'.format(get_n_params(model_fnn_2)))\n\nfor epoch in range(0, 10):\n    scramble_train(epoch, model_fnn_2, fixed_perm)\n    scramble_test(model_fnn_2, fixed_perm)","metadata":{"scrolled":true,"execution":{"iopub.status.busy":"2021-10-04T07:55:49.980302Z","iopub.execute_input":"2021-10-04T07:55:49.980556Z","iopub.status.idle":"2021-10-04T07:58:00.860167Z","shell.execute_reply.started":"2021-10-04T07:55:49.980527Z","shell.execute_reply":"2021-10-04T07:58:00.859371Z"},"trusted":true},"execution_count":14,"outputs":[{"name":"stdout","text":"Training on  cuda:0\nNumber of parameters: 183770\nTrain Epoch: 0 [0/60000 (0%)]\tLoss: 2.313312\nTrain Epoch: 0 [6400/60000 (11%)]\tLoss: 2.184037\nTrain Epoch: 0 [12800/60000 (21%)]\tLoss: 1.399919\nTrain Epoch: 0 [19200/60000 (32%)]\tLoss: 0.641634\nTrain Epoch: 0 [25600/60000 (43%)]\tLoss: 0.427648\nTrain Epoch: 0 [32000/60000 (53%)]\tLoss: 0.735366\nTrain Epoch: 0 [38400/60000 (64%)]\tLoss: 0.300395\nTrain Epoch: 0 [44800/60000 (75%)]\tLoss: 0.339064\nTrain Epoch: 0 [51200/60000 (85%)]\tLoss: 0.508439\nTrain Epoch: 0 [57600/60000 (96%)]\tLoss: 0.271234\n\nTest set: Average loss: 0.2958, Accuracy: 9120/10000 (91%)\n\nTrain Epoch: 1 [0/60000 (0%)]\tLoss: 0.219611\nTrain Epoch: 1 [6400/60000 (11%)]\tLoss: 0.141608\nTrain Epoch: 1 [12800/60000 (21%)]\tLoss: 0.402851\nTrain Epoch: 1 [19200/60000 (32%)]\tLoss: 0.232927\nTrain Epoch: 1 [25600/60000 (43%)]\tLoss: 0.297120\nTrain Epoch: 1 [32000/60000 (53%)]\tLoss: 0.332926\nTrain Epoch: 1 [38400/60000 (64%)]\tLoss: 0.262555\nTrain Epoch: 1 [44800/60000 (75%)]\tLoss: 0.299975\nTrain Epoch: 1 [51200/60000 (85%)]\tLoss: 0.257360\nTrain Epoch: 1 [57600/60000 (96%)]\tLoss: 0.175640\n\nTest set: Average loss: 0.1975, Accuracy: 9432/10000 (94%)\n\nTrain Epoch: 2 [0/60000 (0%)]\tLoss: 0.170990\nTrain Epoch: 2 [6400/60000 (11%)]\tLoss: 0.247841\nTrain Epoch: 2 [12800/60000 (21%)]\tLoss: 0.189010\nTrain Epoch: 2 [19200/60000 (32%)]\tLoss: 0.282354\nTrain Epoch: 2 [25600/60000 (43%)]\tLoss: 0.429647\nTrain Epoch: 2 [32000/60000 (53%)]\tLoss: 0.141841\nTrain Epoch: 2 [38400/60000 (64%)]\tLoss: 0.183931\nTrain Epoch: 2 [44800/60000 (75%)]\tLoss: 0.198016\nTrain Epoch: 2 [51200/60000 (85%)]\tLoss: 0.075450\nTrain Epoch: 2 [57600/60000 (96%)]\tLoss: 0.162376\n\nTest set: Average loss: 0.1636, Accuracy: 9519/10000 (95%)\n\nTrain Epoch: 3 [0/60000 (0%)]\tLoss: 0.372585\nTrain Epoch: 3 [6400/60000 (11%)]\tLoss: 0.097617\nTrain Epoch: 3 [12800/60000 (21%)]\tLoss: 0.189798\nTrain Epoch: 3 [19200/60000 (32%)]\tLoss: 0.145662\nTrain Epoch: 3 [25600/60000 (43%)]\tLoss: 0.077773\nTrain Epoch: 3 [32000/60000 (53%)]\tLoss: 0.106603\nTrain Epoch: 3 [38400/60000 (64%)]\tLoss: 0.144856\nTrain Epoch: 3 [44800/60000 (75%)]\tLoss: 0.241724\nTrain Epoch: 3 [51200/60000 (85%)]\tLoss: 0.128318\nTrain Epoch: 3 [57600/60000 (96%)]\tLoss: 0.065174\n\nTest set: Average loss: 0.1243, Accuracy: 9631/10000 (96%)\n\nTrain Epoch: 4 [0/60000 (0%)]\tLoss: 0.065198\nTrain Epoch: 4 [6400/60000 (11%)]\tLoss: 0.128174\nTrain Epoch: 4 [12800/60000 (21%)]\tLoss: 0.033851\nTrain Epoch: 4 [19200/60000 (32%)]\tLoss: 0.065818\nTrain Epoch: 4 [25600/60000 (43%)]\tLoss: 0.069995\nTrain Epoch: 4 [32000/60000 (53%)]\tLoss: 0.160783\nTrain Epoch: 4 [38400/60000 (64%)]\tLoss: 0.147025\nTrain Epoch: 4 [44800/60000 (75%)]\tLoss: 0.288517\nTrain Epoch: 4 [51200/60000 (85%)]\tLoss: 0.102338\nTrain Epoch: 4 [57600/60000 (96%)]\tLoss: 0.102671\n\nTest set: Average loss: 0.1099, Accuracy: 9679/10000 (97%)\n\nTrain Epoch: 5 [0/60000 (0%)]\tLoss: 0.086134\nTrain Epoch: 5 [6400/60000 (11%)]\tLoss: 0.054850\nTrain Epoch: 5 [12800/60000 (21%)]\tLoss: 0.053114\nTrain Epoch: 5 [19200/60000 (32%)]\tLoss: 0.094749\nTrain Epoch: 5 [25600/60000 (43%)]\tLoss: 0.120391\nTrain Epoch: 5 [32000/60000 (53%)]\tLoss: 0.069578\nTrain Epoch: 5 [38400/60000 (64%)]\tLoss: 0.052634\nTrain Epoch: 5 [44800/60000 (75%)]\tLoss: 0.171941\nTrain Epoch: 5 [51200/60000 (85%)]\tLoss: 0.147429\nTrain Epoch: 5 [57600/60000 (96%)]\tLoss: 0.029881\n\nTest set: Average loss: 0.0999, Accuracy: 9688/10000 (97%)\n\nTrain Epoch: 6 [0/60000 (0%)]\tLoss: 0.071289\nTrain Epoch: 6 [6400/60000 (11%)]\tLoss: 0.103085\nTrain Epoch: 6 [12800/60000 (21%)]\tLoss: 0.024409\nTrain Epoch: 6 [19200/60000 (32%)]\tLoss: 0.020256\nTrain Epoch: 6 [25600/60000 (43%)]\tLoss: 0.010267\nTrain Epoch: 6 [32000/60000 (53%)]\tLoss: 0.074697\nTrain Epoch: 6 [38400/60000 (64%)]\tLoss: 0.092260\nTrain Epoch: 6 [44800/60000 (75%)]\tLoss: 0.065542\nTrain Epoch: 6 [51200/60000 (85%)]\tLoss: 0.104234\nTrain Epoch: 6 [57600/60000 (96%)]\tLoss: 0.044166\n\nTest set: Average loss: 0.0921, Accuracy: 9712/10000 (97%)\n\nTrain Epoch: 7 [0/60000 (0%)]\tLoss: 0.033976\nTrain Epoch: 7 [6400/60000 (11%)]\tLoss: 0.021100\nTrain Epoch: 7 [12800/60000 (21%)]\tLoss: 0.008134\nTrain Epoch: 7 [19200/60000 (32%)]\tLoss: 0.017355\nTrain Epoch: 7 [25600/60000 (43%)]\tLoss: 0.084864\nTrain Epoch: 7 [32000/60000 (53%)]\tLoss: 0.052808\nTrain Epoch: 7 [38400/60000 (64%)]\tLoss: 0.028968\nTrain Epoch: 7 [44800/60000 (75%)]\tLoss: 0.022372\nTrain Epoch: 7 [51200/60000 (85%)]\tLoss: 0.081928\nTrain Epoch: 7 [57600/60000 (96%)]\tLoss: 0.059890\n\nTest set: Average loss: 0.0946, Accuracy: 9696/10000 (97%)\n\nTrain Epoch: 8 [0/60000 (0%)]\tLoss: 0.036753\nTrain Epoch: 8 [6400/60000 (11%)]\tLoss: 0.128112\nTrain Epoch: 8 [12800/60000 (21%)]\tLoss: 0.059585\nTrain Epoch: 8 [19200/60000 (32%)]\tLoss: 0.078037\nTrain Epoch: 8 [25600/60000 (43%)]\tLoss: 0.088705\nTrain Epoch: 8 [32000/60000 (53%)]\tLoss: 0.089082\nTrain Epoch: 8 [38400/60000 (64%)]\tLoss: 0.064373\nTrain Epoch: 8 [44800/60000 (75%)]\tLoss: 0.029573\nTrain Epoch: 8 [51200/60000 (85%)]\tLoss: 0.060114\nTrain Epoch: 8 [57600/60000 (96%)]\tLoss: 0.028512\n\nTest set: Average loss: 0.0807, Accuracy: 9748/10000 (97%)\n\nTrain Epoch: 9 [0/60000 (0%)]\tLoss: 0.015103\nTrain Epoch: 9 [6400/60000 (11%)]\tLoss: 0.122875\nTrain Epoch: 9 [12800/60000 (21%)]\tLoss: 0.108078\nTrain Epoch: 9 [19200/60000 (32%)]\tLoss: 0.042668\nTrain Epoch: 9 [25600/60000 (43%)]\tLoss: 0.058161\nTrain Epoch: 9 [32000/60000 (53%)]\tLoss: 0.081582\nTrain Epoch: 9 [38400/60000 (64%)]\tLoss: 0.273529\nTrain Epoch: 9 [44800/60000 (75%)]\tLoss: 0.051074\nTrain Epoch: 9 [51200/60000 (85%)]\tLoss: 0.016274\nTrain Epoch: 9 [57600/60000 (96%)]\tLoss: 0.150063\n\nTest set: Average loss: 0.0895, Accuracy: 9728/10000 (97%)\n\n","output_type":"stream"}]},{"cell_type":"code","source":"model_fnn_2.to('cpu') \n\n# fetch a batch of test images\nimage_batch, label_batch = next(iter(test_loader))\nimage_batch_scramble = image_batch.view(-1, 28*28)\nimage_batch_scramble = image_batch_scramble[:, fixed_perm]\nimage_batch_scramble = image_batch_scramble.view(-1, 1, 28, 28)\n# Turn off gradients to speed up this part\nwith torch.no_grad():\n    log_pred_prob_batch = model_fnn_2(image_batch_scramble)\nfor i in range(10):\n    img = image_batch[i]\n    img_perm = image_batch_scramble[i]\n    real_label = label_batch[i].item()\n    log_pred_prob = log_pred_prob_batch[i]\n    # Output of the network are log-probabilities, need to take exponential for probabilities\n    pred_prob = torch.exp(log_pred_prob).data.numpy().squeeze()\n    visualize_pred(img_perm, pred_prob, real_label)","metadata":{"execution":{"iopub.status.busy":"2021-10-04T08:02:51.941561Z","iopub.execute_input":"2021-10-04T08:02:51.942411Z","iopub.status.idle":"2021-10-04T08:02:54.944605Z","shell.execute_reply.started":"2021-10-04T08:02:51.942376Z","shell.execute_reply":"2021-10-04T08:02:54.943941Z"},"trusted":true},"execution_count":15,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x648 with 2 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{"needs_backgrou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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure 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e1BmZlZJLlBmZlZJLlBmZlZJLlBmZlZJLlBmZlZJLlBmZlZJDdvMG7WSv3rUxGRs+DXTk7FOaCW3+qrSSt7IRgPXlJ2CmRXEIygzM6skryRhVjBJC4DlwBpgdUR4VQmzJrhAmbXGByPiz2UnYdbJPMVnZmaV5AJlVrwAbpM0S9KJPYN+YKFZ77hAmRXv/RGxO/Bh4HOS9qsN+oGFZr3T9D2oRq3krTBgxIhkrBNWop5/xa7J2LafnNO2PFol9baDdv+cDDvkybZer56IWJL/93lJvwL2Au4qNyuzzuMRlFmBJA2TNGLtx8CHgLnlZmXWmdzFZ1asvwF+JQmy/7+uiIhbyk3JrDO5QJkVKCKeBHYpOw+z/sBTfGZmVkkuUGZmVkkuUGZmVkktuQd16dN3J2NTxqVXSG+kna3kNy6ZlYxNHr1HU+fsD63kjTTTTn7zkvTq6F713sw8gjIzs0pyF59ZiR5c8jLjv3RT2WmYNWXB2R9p6fk9gjIzs0pygTIzs0pygTIzs0pygTJrAUkDJd0v6cayczHrVE03Sfx80T3J2HF7fqzBkc8lI0W3d887p/4K2wCPH3V+Mtbd5yv1HwOGDk3GuleuLPRaVWolb/Sz16TPA48Aby/6xGYbCo+gzAomaQzwEeCisnMx62QuUGbF+yFwOonBeO0TddesfLmtiZl1EhcoswJJmgw8HxHJOcPaJ+oOHLpJG7Mz6ywuUGbF2gc4TNIC4CrgAEm/KDcls87kAmVWoIj4ckSMiYjxwNHA7RFxbMlpmXUkFygzM6skRUQyePCAI9PBCkmtnt7syumNVtmev/q1ZOyUrfdp6npWbdO6r1Wrzr3xltvHlp/5YatOb9ZSRa3FJ2lWRHT13O4RlJmZVZJXMzcr0YTRmzCzxStCm3Uqj6DMzKySXKDMzKySXKDMzKySXKDMzKySGjZJfHn+A8nYt7fdORl79aj0KuLDr5nei7T6ptl28pRmV9k+ff6Dydh3t53QbDptdeszc5Kxv9tq17bl0ajVv9nvT6PV7bc7rfifSzNbPx5BmZlZJblAmRVI0hBJf5D0R0kPSfrfZedk1qn8PiizYr0OHBARr0oaDNwt6TcR4TlEsz5ygTIrUGRrh72afzo4/9cRS4aZVY2n+MwKJmmgpDnA88C0iJhRckpmHckFyqxgEbEmInYFxgB7SdqpNl77RN2lS5eWkqNZJ2j7auYj79ksGXthnxebOueNS+o/vHTy6D2aOl8jrWh/tmpbn9XMJX0NWBkR36sX7+rqipkzZzadm1l/4NXMzdpA0haSNs0/fhtwMPBoqUmZdSg3SZgVa0vgMkkDyf4AvCYibiw5J7OO5AJlVqCIeADYrew8zPoDT/GZmVkluUCZmVkluUCZmVkltf0e1OOXvicZG8m9TZ2zFe3k1v88c/r7krGtvvv7NmZiZr3hEZSZmVWSC5SZmVWSC5SZmVWSC5SZmVWSC5SZmVWSC5RZgSSNlXSHpIfzJ+p+vuyczDpV29vMR/6suVbyquiUFctPn/9gMrbfkFXJWKOW/Xau5P7CCXsnY83+DLWplXw18IWImC1pBDBL0rSIeLgdFzfrTzyCMitQRPwpImbnHy8HHgFGl5uVWWdygTJrEUnjyRaOndFjux9YaNYLLlBmLSBpOPBL4NSIeKU2FhEXRkRXRHRtscUW5SRo1gFcoMwKJmkwWXG6PCKuLzsfs07lAmVWIEkCLgYeiYgflJ2PWSdrSRffAQ+uSMZunzAsGRt2V3q6Y8V+nT1XP2DEiGSse/nywq/33W0npGMNjnvjoHQX36SCb/XfuGRWMja5zW0FT/z7e4s61T7APwAPSpqTb/tKRNxc1AXMNhR+oq5ZgSLibkBl52HWH3iKz8zMKskFyszMKskFyszMKskFyszMKskFyszMKkkRkQwePODIdPC3Y9JnPXDx+uRU168W/yEZ++iYvQq9VjsXRV2XATu/JxnrfuDRNmZSHcc//lQy9vUrPpWMjTuzucVip3Vf27KuvK6urpg5c2arTm/WESTNioiunts9gjIzs0pygTIzs0pygTIrkKRLJD0vaW7ZuZh1Ohcos2JdChxSdhJm/YELlFmBIuIuYFnZeZj1By5QZmZWSQ3bzN/ztXOSwbH/2lzLrvVNo5b3btLfu5tWbpKM/WT77dYrp/6o0crqG2/5ZJ/azPMn6d4YETsl4icCJwKMGzduj4ULF/bl9Gb9jtvMzSrCT9Q16x0XKDMzqyQXKLMCSboSuBd4t6TFko4vOyezTuUHFpoVKCKOKTsHs/7CIygzM6skFygzM6ukhlN8rWgl/9Sj6ZXOL39PgxXSG3j8/Pqrmb/rs+kV0DtFu1dPn/jHN5Kx6bsM7vP5GrVvTx69RzJ21lP3JWNnbLNnn/NYl0a5TOsu/HJm1gseQZmZWSW5QJmZWSW5QJmZWSW5QJmZWSW5QJmZWSW5QJmZWSW1fSWJny7YLxkbzpPJWKNVvSeNXq+UrEYzreSNNGrfbqQVreTtIukQ4FxgIHBRRJxdckpmHckjKLMCSRoI/Bj4MLAjcIykHcvNyqwzuUCZFWsvYF5EPBkRq4CrgMNLzsmsI7lAmRVrNLCo5vPF+bY3STpR0kxJM5cuXdrW5Mw6iQuUWZv5gYVmveMCZVasJcDYms/H5NvMrI9coMyKdR+wvaRtJG0EHA1MLTkns47UkjbzQX/7N8nYTpv/KRlb0OCc+5322WRsONN7kVUxGre7F7/yeLOrgb/xoa5kbPBtM9crJ0uLiNWSTgZuJWszvyQiHio5LbOO5CfqmhUsIm4Gbi47D7NO5yk+MzOrJBcoMzOrJBcoMzOrJBcoMzOrJBcoMzOrpJZ08a1+9rlkbOshryVjC3hbMjb8mva1kjfSilbyRppdDbwqreTtbss3s/7DIygzM6skFygzM6skFygzM6skFygzM6skL3VkVqJZs2a9KumxsvOoMQr4c9lJ5JxLff0xl63rbXSBMivXYxGRXtm3zSTNrEo+zqW+DSmXhgVqWve1atWF6/mX7nZezco2zd9vM2vA96DMzKySXKDMynVh2Qn0UKV8nEt9G0wuiohWnt/MzKwpHkGZmVkluUCZtYGkQyQ9JmmepC/ViW8s6eo8PkPS+BJz+Z+SHpb0gKTfSqrbAtyOXGr2+5ikkNTS7rXe5CPpqPzr85CkK8rKRdI4SXdIuj//Xk1qUR6XSHpe0txEXJJ+lOf5gKTiFtmMCP/zP/9r4T9gIDAfeCewEfBHYMce+3wWuCD/+Gjg6hJz+SAwNP/4n8rMJd9vBHAXMB3oKvn7tD1wP7BZ/vk7SszlQuCf8o93BBa0KJf9gN2BuYn4JOA3gICJwIyiru0RlFnr7QXMi4gnI2IVcBVweI99Dgcuyz++DjhQUive5rHOXCLijohYmX86HRjTgjx6lUvuLOA7wF9alEdf8jkB+HFEvAgQEc+XmEsAb88/3gR4phWJRMRdwLIGuxwO/J/ITAc2lbRlEdd2gTJrvdHAoprPF+fb6u4TEauBl4GRJeVS63iyv45bYZ255NNFYyPiphbl0Kd8gHcB75J0j6Tpkg4pMZczgWMlLQZuBk5pUS7r0tefqV7zShJmVpekY4Eu4AMlXX8A8ANgShnXTxhENs23P9nI8i5JEyLipRJyOQa4NCK+L2lv4OeSdoqIfvMWeI+gzFpvCTC25vMx+ba6+0gaRDZl80JJuSDpIOCrwGER8XoL8uhNLiOAnYA7JS0gu78xtYWNEr352iwGpkbEGxHxFPA4WcEqI5fjgWsAIuJeYAjZ2njt1qufqWa4QJm13n3A9pK2kbQRWRPE1B77TAU+k3/8ceD2yO9AtzsXSbsBPyUrTq26x7LOXCLi5YgYFRHjI2I82f2wwyKiVY+L7s336Qay0ROSRpFN+T1ZUi5PAwfmuexAVqCWtiCXdZkKfDrv5psIvBwRfyrixJ7iM2uxiFgt6WTgVrLurEsi4iFJ3wBmRsRU4GKyKZp5ZDekjy4xl38DhgPX5n0aT0fEYSXl0ja9zOdW4EOSHgbWAF+MiMJHur3M5QvAzySdRtYwMaUVf9RIupKsKI/K73d9HRic53kB2f2vScA8YCVwXGHXbs0faWZmZuvHU3xmZlZJLlBmZlZJLlBmZlZJLlBmZlZJLlBmZlZJLlBmZlZJLlBmZlZJLlBmZlZJ/w+onoCETR3qwAAAAABJRU5ErkJggg==\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure 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DQvNRsYJyqx8x0TEYcCbgPdLOq6x0TcsNBuZ2lyD2m/Px6sOoRLNSslTml38xv7h45w1tbhMvtM6VUoeEf35v09I+jZwBHB7Rw5uNoq4B2VWIkk7SJq46TnwRmBxtVGZ1VNtelBmNfFK4NuSIPv/9Y2I+H61IZnVkxOUWYki4kHgkKrjMBsNPMRnZmZJcoIyM7MkOUGZmVmS2nINqqjkGGCQwcK2pqXRM1dtTUijUkqzi6dUTm5mo4N7UGZmliQnKDMzS5ITlJmZJckJyszMkuQEZWZmSXKCMmsDSV2S7pF0Y9WxmNVV0zLzZrNlL1g/trBt1i9PK96py8WTVvYM6c2+cpBSaXqzOFt0PrAU2KnsHZttK9yDMiuZpGnAm4EvVx2LWZ05QZmV73PAR2D4b6X7jrpmI+MEZVYiSbOAJyKicMzQd9Q1GxknKLNyvR6YLWkFcDXwBklfrzYks3pygjIrUUT8fURMi4hu4AzgRxFxVsVhmdWSE5SZmSWpaZl567Nl17uU/MTFzxa2XbDr/YVtKZVNt6rsGdJTek+6Xr13YdusqcXbzSuegL+piLgNuK21rc3MPSgzM0uSE5SZmSXJCcrMzJLkBGVmZklygjKr0L3966oOwSxZTlBmZpakpmXm26ofHDSxuI10yqbrbvCYGYVtY+5YVNj2jqWPFbZ984BXFbYNLH9oJGGZWSLcgzIzsyQ5QZmVSNJ4ST+T9HNJSyT9U9UxmdWVh/jMyrUeeENEPCdpLHCHpO9FxPyqAzOrGycosxJFRADP5S/H5o+oLiKz+vIQn1nJJHVJWgQ8AcyLiAUVh2RWS05QZiWLiIGImAFMA46QdFBje+MddQde8PegzIqMiiG+uf13D7u87Jm5rVzNSsmbaVZKnpKIWCvpVuAUYHHD8jnAHIBxe+zr4T+zAu5BmZVI0mRJu+TPXwGcBPyy0qDMampU9KDMErIHcIWkLrIPgN+MiBsrjsmslpygzEoUEb8ADq06DrPRwEN8ZmaWJCcoMzNLkhOUWYUOnrpz1SGYJavla1BFpd3Q+fLuTh6v0+f90NWvLWzb+4xftLTPlH52RW7s7ytsmzXVM8qbbQvcgzIzsyQ5QZlVyHfUNSvmBGVmZklygjIzsyQ5QZmZWZKcoMxKJGm6pFsl3ZffUff8qmMyq6umZebNS33TKEfutE6XYbdaSm6V2Qh8KCIWSpoI9EmaFxH3VR2YWd24B2VWooh4NCIW5s+fBZYCU6uNyqyenKDM2kRSN9nEsQuGLPcNC81GwAnKrA0k7Qh8C7ggIp5pbIuIORHRExE9XRM81ZFZEScos5JJGkuWnK6MiOurjsesrpygzEokScBXgKUR8dmq4zGrs6ZVfJ6Uszydnvz05tWLCttOnpJ+BWY73pNmk+SW6PXAnwH3SlqUL/uHiLipEwc3G018R12zEkXEHYCqjsNsNPAQn5mZJckJyqxCvmGhWTEnKDMzS5ITlJmZJckJyszMkjQqqviKSrhbLVVuVo7c6mSxb33d7MK2V971bGHbV/a8tbCt2fmdPGXGiOLaEp0ulS/S7OczpslnrlYnOJ432NJmI3Jv/zq6L/xu+w5g1kYrLnlzW/fvHpSZmSXJCcrMzJLkBGVWIkmXSXpC0uKqYzGrOycos3JdDpxSdRBmo4ETlFmJIuJ24Kmq4zAbDZygzMwsSaOizLzsEudWS8mb2biqv7Dt8aOKtxvsL7/GuVm5eDODtLHeeoh2lPo3s/7Uzs3wLulc4FyArp0md+y4ZnXjHpRZh/mOumYj4wRlZmZJcoIyK5Gkq4C7gP0lrZL0rqpjMqurUXENyiwVEXFm1TGYjRbuQZmZWZKcoMzMLEmKiMLGk8a8vbixRe2YEbuoJLkd5ci2ZT7+4MLCtk/uc1gHI2ndvMFr1a599/T0RG9vb7t2b1YLkvoiomfocvegzMwsSU5QZmaWJCcoMzNLkhOUmZklyQnKzMyS5ARlZmZJ6vhMEmPVVfo+XU6erpRKydvxFYfhSDoFuBToAr4cEZeUtnOzbYh7UGYlktQFfAF4E3AgcKakA6uNyqyenKDMynUEsDwiHoyIl4CrgdMqjsmslpygzMo1FVjZ8HpVvuxlks6V1Cupd82aNR0NzqxOnKDMOqzxhoWTJ/uOumZFnKDMytUPTG94PS1fZmZbyAnKrFx3A/tK2lvS9sAZwNyKYzKrpY6XmZ88ZUanD7lNmnznLoVtT62fUNg2cMLqNkSThjJLyYtExEZJ5wE3k5WZXxYRS9p+YLNRyHfUNStZRNwE3FR1HGZ15yE+MzNLkhOUmZklyQnKzMyS5ARlZmZJcoIyM7MktaWKb27/3YVtp/zlewvbXnFhcYnzaC5/boc1R69t0tqsrVyPfujowrY9PnNnx+Iws/pxD8rMzJLkBGVmZklygjIzsyQ5QZmZWZI81ZFZhfr6+p6TtKzqOBpMAp6sOoicYxneaIxlr+EWOkGZVWtZRPRUHcQmknpTicexDG9biqVpgpo3eK3KPuCPv9fihoOlhmEp+LcPVh2BmSXM16DMzCxJTlBm1ZpTdQBDpBSPYxneNhOLIqKd+zczM2uJe1BmZpYkJyizDpB0iqRlkpZLunCY9nGSrsnbF0jqrjCWv5V0n6RfSPqhpGFLgDsRS8N6fywpJLW1em0k8Uh6R/7+LJH0japikbSnpFsl3ZP/rE5tUxyXSXpC0uKCdkn69zzOX0g6rLSDR4QffvjRxgfQBfwfsA+wPfBz4MAh67wP+GL+/AzgmgpjOQGYkD9/b5Wx5OtNBG4H5gM9Ff+c9gXuAXbNX+9eYSxzgPfmzw8EVrQpluOAw4DFBe2nAt8DBBwJLCjr2O5BmbXfEcDyiHgwIl4CrgZOG7LOacAV+fPrgJmSSv+ax0hiiYhbI+KF/OV8YFob4hhRLLlPAf8K/LpNcWxJPO8GvhARTwNExBMVxhLATvnznYG23PIhIm4HnmqyymnA/0RmPrCLpD3KOLYTlFn7TQVWNrxelS8bdp2I2AisA3arKJZG7yL7dNwOm40lHy6aHhHfbVMMWxQPsB+wn6SfSpov6ZQKY/kEcJakVcBNwAfaFMvmbOnv1Ih5JgkzG5aks4Ae4A8rOv4Y4LPA2VUcv8B2ZMN8x5P1LG+XdHBErK0gljOByyPiM5KOAr4m6aCIGDXTGrgHZdZ+/cD0htfT8mXDriNpO7Ihm19VFAuSTgQ+CsyOiPVtiGMksUwEDgJuk7SC7PrG3DYWSozkvVkFzI2IDRHxEHA/WcKqIpZ3Ad8EiIi7gPFkc+N12oh+p1rhBGXWfncD+0raW9L2ZEUQc4esMxf4i/z524AfRX4FutOxSDoU+BJZcmrXNZbNxhIR6yJiUkR0R0Q32fWw2RHRW0U8uRvIek9ImkQ25PdgRbE8AszMYzmALEGtaUMsmzMX+PO8mu9IYF1EPFrGjj3EZ9ZmEbFR0nnAzWTVWZdFxBJJnwR6I2Iu8BWyIZrlZBekz6gwln8DdgSuzes0HomI2RXF0jEjjOdm4I2S7gMGgA9HROk93RHG8iHgvyV9kKxg4ux2fKiRdBVZUp6UX++6CBibx/lFsutfpwLLgReAc0o7dns+pJmZmW0dD/GZmVmSnKDMzCxJTlBmZpYkJygzM0uSE5SZmSXJCcrMzJLkBGVmZklygjIzsyT9P9rhLg1Qji4ZAAAAAElFTkSuQmCC\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{"needs_background":"light"}}]}]}