CS5100_PretrainedModels.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"accelerator":"GPU","colab":{"name":"CS5100_PretrainedModels.ipynb","provenance":[],"machine_shape":"hm","authorship_tag":"ABX9TyPY2a7euDUX0xusCjd/C4k0"},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","metadata":{"id":"AkaXIYi7QEev","executionInfo":{"status":"ok","timestamp":1636916520477,"user_tz":300,"elapsed":13946,"user":{"displayName":"GPU DeDe","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09796458770182087498"}}},"source":["# Import Neccessary Libraries:\n","\n","import numpy as np\n","import pandas as pd\n","import torch\n","from torchvision import datasets, transforms, models\n","import matplotlib.pyplot as plt\n","from torch.utils.data import DataLoader, Dataset\n","from torch import nn\n","import torch.nn.functional as F\n","from torch import optim\n","from tqdm import tqdm\n","import pickle\n","from torch.optim.lr_scheduler import StepLR\n","import time\n","from sklearn.model_selection import train_test_split\n","from PIL import Image"],"execution_count":1,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MRwKN5ZvQMuz","executionInfo":{"status":"ok","timestamp":1636916611902,"user_tz":300,"elapsed":91432,"user":{"displayName":"GPU DeDe","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09796458770182087498"}},"outputId":"207ec00c-a593-4ffd-e50e-870725ce1377"},"source":["# To mount drive:\n","from google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"LpeYrzG3QMxT","executionInfo":{"status":"ok","timestamp":1636916611903,"user_tz":300,"elapsed":18,"user":{"displayName":"GPU DeDe","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09796458770182087498"}},"outputId":"b8e8b011-56d3-4d2e-b02c-a3819fb1d638"},"source":["# Checking GPU:\n","print(torch.cuda.get_device_name(0))"],"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["Tesla P100-PCIE-16GB\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QKnysO1GQMzk","executionInfo":{"status":"ok","timestamp":1636916611904,"user_tz":300,"elapsed":13,"user":{"displayName":"GPU DeDe","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09796458770182087498"}},"outputId":"63e436a6-6926-4814-e36e-23503c238c65"},"source":["device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n","device"],"execution_count":4,"outputs":[{"output_type":"execute_result","data":{"text/plain":["device(type='cuda', index=0)"]},"metadata":{},"execution_count":4}]},{"cell_type":"code","metadata":{"id":"oDX910kjQM5q","executionInfo":{"status":"ok","timestamp":1636916611904,"user_tz":300,"elapsed":10,"user":{"displayName":"GPU DeDe","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09796458770182087498"}}},"source":["# # To perform Transformations to the Data\n","# transform = transforms.ToTensor()"],"execution_count":5,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"G-JPlVQrQzt3","executionInfo":{"status":"ok","timestamp":1636916635782,"user_tz":300,"elapsed":23887,"user":{"displayName":"GPU DeDe","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09796458770182087498"}},"outputId":"b7bfc240-2f9f-4f79-b635-34cbea30b2d8"},"source":["!cp \"/content/drive/MyDrive/CS5100 Project/resized_images.zip\" -r \"/content/resized_images.zip\"\n","!unzip /content/resized_images.zip -d /content/resized_images"],"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","  inflating: /content/resized_images/00026538_006.png  \n","  inflating: /content/resized_images/00026538_007.png  \n","  inflating: /content/resized_images/00026538_008.png  \n","  inflating: /content/resized_images/00026538_009.png  \n","  inflating: /content/resized_images/00026538_010.png  \n","  inflating: /content/resized_images/00026538_011.png  \n","  inflating: /content/resized_images/00026538_012.png  \n","  inflating: /content/resized_images/00026538_013.png  \n","  inflating: /content/resized_images/00026538_014.png  \n","  inflating: /content/resized_images/00026538_015.png  \n","  inflating: /content/resized_images/00026538_016.png  \n","  inflating: /content/resized_images/00026538_017.png  \n","  inflating: /content/resized_images/00026538_018.png  \n","  inflating: 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/content/resized_images/00000038_003.png  \n","  inflating: /content/resized_images/00000038_006.png  \n","  inflating: /content/resized_images/00000039_000.png  \n","  inflating: /content/resized_images/00000039_004.png  \n"]}]},{"cell_type":"code","metadata":{"id":"NW33NbaUREb6","executionInfo":{"status":"ok","timestamp":1636916635782,"user_tz":300,"elapsed":19,"user":{"displayName":"GPU DeDe","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09796458770182087498"}}},"source":["def read_pickle_file(filepath):\n","  with open(filepath, \"rb\") as f:\n","    return pickle.load(f)"],"execution_count":7,"outputs":[]},{"cell_type":"code","metadata":{"id":"vHx_nsebjH7C","executionInfo":{"status":"ok","timestamp":1636916635783,"user_tz":300,"elapsed":18,"user":{"displayName":"GPU DeDe","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09796458770182087498"}}},"source":["# Function to Load Trained Models:\n","\n","def loadModel(model_name, model):\n","  state_dict = torch.load(\"/content/drive/MyDrive/CS5100 Project/Pretrained Models/\" + model_name + \".pt\")\n","  model.load_state_dict(state_dict)\n","  return model"],"execution_count":8,"outputs":[]},{"cell_type":"code","metadata":{"id":"hKGMN5HPUDcr","executionInfo":{"status":"ok","timestamp":1636916635783,"user_tz":300,"elapsed":18,"user":{"displayName":"GPU DeDe","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09796458770182087498"}}},"source":["class ChestXRay_ImageDataset(Dataset):\n","\n","    def __init__(self, metadata, transforms):\n","        self.metadata = metadata\n","        self.transforms = transforms\n","\n","    def __len__(self):\n","        return len(self.metadata)\n","\n","    def __getitem__(self, index):\n","        if torch.is_tensor(index):\n","            index = index.tolist()\n","\n","        image_path = self.metadata[\"image_path\"][index]\n","        # hurricane_image = io.imread(image_path)\n","        xray_image = Image.open(image_path).convert(\"RGB\")\n","        label = self.metadata[\"Disease_Labels\"][index]\n","\n","        if self.transforms:\n","            xray_image = self.transforms(xray_image)\n","\n","        return xray_image, label"],"execution_count":9,"outputs":[]},{"cell_type":"code","metadata":{"id":"Y3DAItD0XYnK","executionInfo":{"status":"ok","timestamp":1636916635784,"user_tz":300,"elapsed":18,"user":{"displayName":"GPU DeDe","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09796458770182087498"}}},"source":["def display_sample_images(dataloader, batch_size, sample_size, num_rows, class_dict):\n","  sample_size = min(sample_size, batch_size)\n","  images, labels = next(iter(dataloader))\n","  images = images.numpy()\n","\n","  fig = plt.figure(figsize = (30, 10))\n","  for i in np.arange(sample_size):\n","      ax = fig.add_subplot(num_rows, sample_size/num_rows, i+1, xticks = [], yticks = [])\n","      ax.imshow(np.squeeze(np.transpose(images[i], (1, 2 ,0))))\n","      ax.set_title(str(class_dict[labels[i].item()]))\n","  \n","  return"],"execution_count":10,"outputs":[]},{"cell_type":"code","metadata":{"id":"4NjH-xY7ZNx3","executionInfo":{"status":"ok","timestamp":1636916635989,"user_tz":300,"elapsed":6,"user":{"displayName":"GPU DeDe","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09796458770182087498"}}},"source":["# Write and Read Lists:\n","# Used to Save Train and Test Losses for all Epochs\n","\n","def write_lists(list_, fname):\n","  with open(\"/content/drive/MyDrive/CS5100 Project/Loss Files/\" + fname, 'w') as f:\n","      for s in list_:\n","          f.write(str(s) + '\\n')\n","\n","def read_lists(fname):\n","  with open(\"/content/drive/MyDrive/CS5100 Project/Loss Files/\" + fname, 'r') as f:\n","      list_ = [float(line.rstrip('\\n')) for line in f]\n","  return list_\n"],"execution_count":11,"outputs":[]},{"cell_type":"code","metadata":{"id":"T-4qBVukPmD8","executionInfo":{"status":"ok","timestamp":1636916635990,"user_tz":300,"elapsed":6,"user":{"displayName":"GPU DeDe","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09796458770182087498"}}},"source":["# Training and Validating Model on Data:\n","\n","def train(epoch, model, trainloader, validloader, criterion, optimizer, model_name, valid_loss_min = np.Inf):\n","  # Training the model:\n","  start = time.time()\n","  \n","  train_losses, validation_losses = [], []\n","  for e in range(epochs):\n","    model.train()\n","    train_loss, validation_loss = 0.0, 0.0\n","    for images, labels in trainloader:\n","        images, labels = images.to(device), labels.to(device)\n","        optimizer.zero_grad()\n","        output = model(images)\n","        loss = criterion(output, labels)\n","        loss.backward()\n","        optimizer.step()\n","        train_loss += loss.item()*images.size(0)\n","    \n","    # Validate the Model:\n","    model.eval()\n","    for images, labels in validloader:\n","      images, labels = images.to(device), labels.to(device)\n","      output = model(images)\n","      loss = criterion(output, labels)\n","      validation_loss += loss.item()*images.size(0)\n","\n","    train_loss = train_loss/len(trainloader.dataset)\n","    validation_loss = train_loss/len(validloader.dataset)\n","    train_losses.append(train_loss)\n","    validation_losses.append(validation_loss)\n","\n","    print(\"Epoch: {}/{}, Train Loss: {:.5f}, Validation Loss: {:.5f}\".format(e+1, epochs, train_loss, validation_loss))\n","    \n","    if validation_loss <= valid_loss_min:\n","      print(\"Decrease in Validation Loss: {:.5f} to {:.5f}. Saving Model...\".format(valid_loss_min, validation_loss))\n","      # torch.save(model.state_dict(), \"/content/\" + model_name + \".pt\")\n","      torch.save(model.state_dict(), \"/content/drive/MyDrive/CS5100 Project/Pretrained Models/\" + model_name + \".pt\")\n","      valid_loss_min = validation_loss\n","\n","  write_lists(train_losses, \"train_losses_\" + model_name)\n","  write_lists(validation_losses, \"validation_losses_\" + model_name) \n","  end = time.time()\n","  hours, rem = divmod(end-start, 3600)\n","  minutes, seconds = divmod(rem, 60)\n","  print(\"\\nTime Taken for Training: {:0>2}:{:0>2}:{:05.2f}\\n\".format(int(hours),int(minutes),seconds))\n","  \n","  return train_losses, validation_losses\n"],"execution_count":12,"outputs":[]},{"cell_type":"code","metadata":{"id":"AE04YpwD_lOV","executionInfo":{"status":"ok","timestamp":1636916635991,"user_tz":300,"elapsed":7,"user":{"displayName":"GPU DeDe","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09796458770182087498"}}},"source":["# Testing Model on Data:\n","\n","def test(model, testloader, criterion, num_classes):\n","\n","  test_loss, accuracy = 0.0, 0.0\n","  class_correct = list(0. for i in range(num_classes))\n","  class_total = list(0. for i in range(num_classes))\n","  model.eval()\n","\n","  for images, labels in testloader:\n","      images, labels = images.to(device), labels.to(device)\n","      output = model(images)\n","      loss = criterion(output, labels)\n","      test_loss += loss.item()*images.size(0)\n","      _, pred = torch.max(output, 1)\n","      correct = np.squeeze(pred.eq(labels.data.view_as(pred)))\n","\n","      for i in range(len(labels)):\n","          label = labels.data[i]\n","          class_correct[label] += correct[i].item()\n","          class_total[label] += 1\n","\n","  test_loss = test_loss/len(testloader.sampler)\n","  print(\"Test Loss: {}\".format(test_loss))\n","\n","  for i in range(num_classes):\n","      if class_total[i] > 0:\n","          print(\"Test Accuracy of {}: {:.3f}%. Frac: {}/{}\".format(str(class_dict[i]), 100*(class_correct[i]/class_total[i]), \n","                                                        np.sum(class_correct[i]), np.sum(class_total[i])))\n","      else:\n","          print(\"Test Accuracy of {}: N/A (no training examples)\".format(classes[i]))\n","  print(\"\\n Test Accuracy (Overall): {:.3f}. Frac {}/{}\".format((100 * np.sum(class_correct)/np.sum(class_total)), \n","                                                            np.sum(class_correct), np.sum(class_total)))\n","  \n","  return\n","  "],"execution_count":13,"outputs":[]},{"cell_type":"code","metadata":{"id":"qFAGCFrkSZ4G","executionInfo":{"status":"ok","timestamp":1636916635991,"user_tz":300,"elapsed":6,"user":{"displayName":"GPU DeDe","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09796458770182087498"}}},"source":["filepath = \"/content/class_labels_dict.pkl\"\n","class_dict = read_pickle_file(filepath)"],"execution_count":14,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"BnTgYmo-Sis3","executionInfo":{"elapsed":207,"status":"ok","timestamp":1636863869795,"user":{"displayName":"GPU DeDe","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09796458770182087498"},"user_tz":300},"outputId":"cc19001e-574a-47a8-8964-a4cb49ae1626"},"source":["print(class_dict)"],"execution_count":null,"outputs":[{"name":"stdout","output_type":"stream","text":["{0: 'Atelectasis', 1: 'Cardiomegaly', 2: 'Consolidation', 3: 'Edema', 4: 'Effusion', 5: 'Emphysema', 6: 'Fibrosis', 7: 'Infiltration', 8: 'Mass', 9: 'No Finding', 10: 'Nodule', 11: 'Pleural_Thickening', 12: 'Pneumonia', 13: 'Pneumothorax'}\n"]}]},{"cell_type":"code","metadata":{"id":"BIvy-ZacRN_A","executionInfo":{"status":"ok","timestamp":1636916637481,"user_tz":300,"elapsed":1496,"user":{"displayName":"GPU DeDe","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09796458770182087498"}}},"source":["df = pd.read_csv(\"/content/df_labels_le.csv\")"],"execution_count":15,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"BNxnRHDaRyAJ","executionInfo":{"status":"ok","timestamp":1636916637482,"user_tz":300,"elapsed":20,"user":{"displayName":"GPU DeDe","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09796458770182087498"}},"outputId":"41ef6ca1-a42b-4fc5-9a3e-95ebb2080437"},"source":["df.head()"],"execution_count":16,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Image Index</th>\n","      <th>value</th>\n","      <th>Disease_Labels</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>00000001_000.png</td>\n","      <td>Cardiomegaly</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>00000001_002.png</td>\n","      <td>Cardiomegaly</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>00000004_000.png</td>\n","      <td>Mass</td>\n","      <td>8</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>00000005_007.png</td>\n","      <td>Effusion</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>00000008_000.png</td>\n","      <td>Cardiomegaly</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["        Image Index         value  Disease_Labels\n","0  00000001_000.png  Cardiomegaly               1\n","1  00000001_002.png  Cardiomegaly               1\n","2  00000004_000.png          Mass               8\n","3  00000005_007.png      Effusion               4\n","4  00000008_000.png  Cardiomegaly               1"]},"metadata":{},"execution_count":16}]},{"cell_type":"code","metadata":{"id":"e10Wo36hR3QZ","executionInfo":{"status":"ok","timestamp":1636916637482,"user_tz":300,"elapsed":17,"user":{"displayName":"GPU DeDe","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09796458770182087498"}}},"source":["df[\"image_path\"] = \"/content/resized_images/\" + df[\"Image Index\"]"],"execution_count":17,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"WRjfYmPJR77z","executionInfo":{"status":"ok","timestamp":1636916637483,"user_tz":300,"elapsed":17,"user":{"displayName":"GPU DeDe","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09796458770182087498"}},"outputId":"525515fa-b4d3-4a72-966c-d630c8787279"},"source":["df.head()"],"execution_count":18,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Image Index</th>\n","      <th>value</th>\n","      <th>Disease_Labels</th>\n","      <th>image_path</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>00000001_000.png</td>\n","      <td>Cardiomegaly</td>\n","      <td>1</td>\n","      <td>/content/resized_images/00000001_000.png</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>00000001_002.png</td>\n","      <td>Cardiomegaly</td>\n","      <td>1</td>\n","      <td>/content/resized_images/00000001_002.png</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>00000004_000.png</td>\n","      <td>Mass</td>\n","      <td>8</td>\n","      <td>/content/resized_images/00000004_000.png</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>00000005_007.png</td>\n","      <td>Effusion</td>\n","      <td>4</td>\n","      <td>/content/resized_images/00000005_007.png</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>00000008_000.png</td>\n","      <td>Cardiomegaly</td>\n","      <td>1</td>\n","      <td>/content/resized_images/00000008_000.png</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["        Image Index  ...                                image_path\n","0  00000001_000.png  ...  /content/resized_images/00000001_000.png\n","1  00000001_002.png  ...  /content/resized_images/00000001_002.png\n","2  00000004_000.png  ...  /content/resized_images/00000004_000.png\n","3  00000005_007.png  ...  /content/resized_images/00000005_007.png\n","4  00000008_000.png  ...  /content/resized_images/00000008_000.png\n","\n","[5 rows x 4 columns]"]},"metadata":{},"execution_count":18}]},{"cell_type":"code","metadata":{"id":"rKSf_kZISDCY","executionInfo":{"status":"ok","timestamp":1636916637483,"user_tz":300,"elapsed":15,"user":{"displayName":"GPU DeDe","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09796458770182087498"}}},"source":["X, y = df[\"image_path\"], df[\"Disease_Labels\"]"],"execution_count":19,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"NbeogQg_SFVQ","executionInfo":{"status":"ok","timestamp":1636916637483,"user_tz":300,"elapsed":15,"user":{"displayName":"GPU DeDe","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09796458770182087498"}},"outputId":"36ec07a1-79ea-4e77-e1f3-7000c9c6bc26"},"source":["print(X.shape, y.shape)"],"execution_count":20,"outputs":[{"output_type":"stream","name":"stdout","text":["(77637,) (77637,)\n"]}]},{"cell_type":"code","metadata":{"id":"V3WzHVkHSpJK","executionInfo":{"status":"ok","timestamp":1636916637676,"user_tz":300,"elapsed":23,"user":{"displayName":"GPU DeDe","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09796458770182087498"}}},"source":["X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=101, stratify=y, shuffle=True)\n","X_train, X_test = pd.DataFrame(X_train), pd.DataFrame(X_test)"],"execution_count":21,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"g6sdYtqnS3Bj","executionInfo":{"status":"ok","timestamp":1636916637677,"user_tz":300,"elapsed":23,"user":{"displayName":"GPU DeDe","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09796458770182087498"}},"outputId":"c93f7491-7e2f-4dd5-97f6-473f55f7670d"},"source":["print(X_train.shape, y_train.shape)\n","print(X_test.shape, y_test.shape)"],"execution_count":22,"outputs":[{"output_type":"stream","name":"stdout","text":["(62109, 1) (62109,)\n","(15528, 1) (15528,)\n"]}]},{"cell_type":"code","metadata":{"id":"Z9j5-aMsS58G","executionInfo":{"status":"ok","timestamp":1636916637677,"user_tz":300,"elapsed":18,"user":{"displayName":"GPU DeDe","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09796458770182087498"}}},"source":["train_df = pd.concat([X_train, y_train], axis=1)\n","test_df = pd.concat([X_test, y_test], axis=1).reset_index().drop(\"index\", axis=1)\n","\n","rs = np.random.RandomState(101)\n","msk = rs.rand(len(train_df)) < 0.85\n","valid_df = train_df[~msk].reset_index().drop(\"index\", axis=1)\n","train_df = train_df[msk].reset_index().drop(\"index\", axis=1)\n"],"execution_count":23,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UO2d4FqOTCnV","executionInfo":{"status":"ok","timestamp":1636916637678,"user_tz":300,"elapsed":19,"user":{"displayName":"GPU DeDe","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09796458770182087498"}},"outputId":"d1d98fe1-78a3-4d88-9a6b-b80637e705f3"},"source":["print(train_df.shape, valid_df.shape, test_df.shape)"],"execution_count":24,"outputs":[{"output_type":"stream","name":"stdout","text":["(52696, 2) (9413, 2) (15528, 2)\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Nx-b3UX9Wa6H","executionInfo":{"status":"ok","timestamp":1636916637678,"user_tz":300,"elapsed":16,"user":{"displayName":"GPU DeDe","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09796458770182087498"}},"outputId":"0382c672-f401-4e33-b924-6a274c3e8176"},"source":["train_df[\"Disease_Labels\"].value_counts()"],"execution_count":25,"outputs":[{"output_type":"execute_result","data":{"text/plain":["7     10632\n","4      9410\n","0      6352\n","13     4179\n","9      4115\n","8      3942\n","10     3692\n","2      3296\n","1      1729\n","11     1694\n","5      1292\n","3      1227\n","12      639\n","6       497\n","Name: Disease_Labels, dtype: int64"]},"metadata":{},"execution_count":25}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2WBp6QNIWsyy","executionInfo":{"status":"ok","timestamp":1636916637679,"user_tz":300,"elapsed":14,"user":{"displayName":"GPU DeDe","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09796458770182087498"}},"outputId":"49a94aa6-7298-4187-b3d3-b6872359ee33"},"source":["test_df[\"Disease_Labels\"].value_counts()"],"execution_count":26,"outputs":[{"output_type":"execute_result","data":{"text/plain":["7     3134\n","4     2768\n","0     1876\n","13    1234\n","9     1207\n","8     1157\n","10    1093\n","2      968\n","1      509\n","11     502\n","5      381\n","3      361\n","12     193\n","6      145\n","Name: Disease_Labels, dtype: int64"]},"metadata":{},"execution_count":26}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"zuQ_KFvQWtF6","executionInfo":{"status":"ok","timestamp":1636916637680,"user_tz":300,"elapsed":12,"user":{"displayName":"GPU DeDe","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09796458770182087498"}},"outputId":"85e926f2-a8f7-4c44-e934-f76f6d6feeec"},"source":["valid_df[\"Disease_Labels\"].value_counts()"],"execution_count":27,"outputs":[{"output_type":"execute_result","data":{"text/plain":["7     1905\n","4     1660\n","0     1153\n","13     756\n","9      714\n","8      688\n","10     678\n","2      574\n","11     313\n","1      307\n","5      230\n","3      216\n","12     134\n","6       85\n","Name: Disease_Labels, dtype: int64"]},"metadata":{},"execution_count":27}]},{"cell_type":"code","metadata":{"id":"Fs8HK_OHWLX_","executionInfo":{"status":"ok","timestamp":1636916637681,"user_tz":300,"elapsed":11,"user":{"displayName":"GPU DeDe","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09796458770182087498"}}},"source":["transform = transforms.Compose([\n","        # transforms.RandomResizedCrop(224),\n","        transforms.Resize((224, 224)),\n","        # transforms.Grayscale(num_output_channels=1),\n","        # transforms.RandomHorizontalFlip(),\n","        transforms.ToTensor(),\n","        # transforms.Normalize([0.5], [0.5])\n","])"],"execution_count":28,"outputs":[]},{"cell_type":"code","metadata":{"id":"Cbj-wrwrV7au","executionInfo":{"status":"ok","timestamp":1636916638189,"user_tz":300,"elapsed":6,"user":{"displayName":"GPU DeDe","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09796458770182087498"}}},"source":["trainset = ChestXRay_ImageDataset(train_df, transform)\n","testset = ChestXRay_ImageDataset(test_df, transform)\n","validset = ChestXRay_ImageDataset(valid_df, transform)\n","\n","trainloader = DataLoader(trainset, batch_size=128, shuffle=True)\n","testloader = DataLoader(testset, batch_size=128, shuffle=True)\n","validloader = DataLoader(validset, batch_size=128, shuffle=True)"],"execution_count":29,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":357},"id":"9CoqTaHLXRDM","executionInfo":{"status":"ok","timestamp":1636916640476,"user_tz":300,"elapsed":2292,"user":{"displayName":"GPU DeDe","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09796458770182087498"}},"outputId":"b5064aec-357e-4cf9-fb61-fc3d56cb06f8"},"source":["batch_size = 64\n","sample_size = 30\n","num_rows = 5\n","display_sample_images(testloader, batch_size, sample_size, num_rows, class_dict)"],"execution_count":30,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 2160x720 with 30 Axes>"]},"metadata":{}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wr0yrIs9ZYCl","executionInfo":{"elapsed":1728,"status":"ok","timestamp":1636751952445,"user":{"displayName":"GPU DeDe","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09796458770182087498"},"user_tz":300},"outputId":"7285c4b8-db5a-439f-dcb7-4bb9655de056"},"source":["# VGG16 Classifier:\n","\n","vgg16 = models.vgg16(pretrained = True)\n","# vgg16\n","\n","# Freeze Parameters so we dont backprop through them:\n","for param in vgg16.parameters():\n","    param.requires_grad = False\n","\n","\"\"\"\n","classifier = nn.Sequential(nn.Linear(25088, 4096, bias = True),\n","                           nn.ReLU(inplace = True),\n","                           nn.Dropout(0.2),\n","                           nn.Linear(4096, 4096, bias = True),\n","                           nn.ReLU(inplace = True),\n","                           nn.Dropout(0.2),\n","                           nn.Linear(4096, 225, bias = True),\n","                           nn.LogSoftmax(dim = 1))\n","\n","vgg16.classifier = classifier\n","vgg16.classifier\n","\n","\"\"\"\n","\n","vgg16.classifier[6] = nn.Linear(4096, len(class_dict), bias = True)\n","# vgg16.classifier\n","vgg16.to(device)\n","\n","# vgg16.avgpool = nn.AdaptiveAvgPool2d(output_size = (2, 2))\n","# vgg16"],"execution_count":null,"outputs":[{"data":{"text/plain":["VGG(\n","  (features): Sequential(\n","    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (1): ReLU(inplace=True)\n","    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (3): ReLU(inplace=True)\n","    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n","    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (6): ReLU(inplace=True)\n","    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (8): ReLU(inplace=True)\n","    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n","    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (11): ReLU(inplace=True)\n","    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (13): ReLU(inplace=True)\n","    (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (15): ReLU(inplace=True)\n","    (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n","    (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (18): ReLU(inplace=True)\n","    (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (20): ReLU(inplace=True)\n","    (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (22): ReLU(inplace=True)\n","    (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n","    (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (25): ReLU(inplace=True)\n","    (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (27): ReLU(inplace=True)\n","    (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (29): ReLU(inplace=True)\n","    (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n","  )\n","  (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))\n","  (classifier): Sequential(\n","    (0): Linear(in_features=25088, out_features=4096, bias=True)\n","    (1): ReLU(inplace=True)\n","    (2): Dropout(p=0.5, inplace=False)\n","    (3): Linear(in_features=4096, out_features=4096, bias=True)\n","    (4): ReLU(inplace=True)\n","    (5): Dropout(p=0.5, inplace=False)\n","    (6): Linear(in_features=4096, out_features=14, bias=True)\n","  )\n",")"]},"execution_count":71,"metadata":{},"output_type":"execute_result"}]},{"cell_type":"code","metadata":{"id":"3AILhID4ZjU9"},"source":["criterion = nn.CrossEntropyLoss()\n","optimizer = optim.Adam(vgg16.classifier.parameters(), lr = 0.0001)\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"WA2-si_9aB_E","executionInfo":{"elapsed":9895863,"status":"ok","timestamp":1636765224130,"user":{"displayName":"GPU DeDe","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09796458770182087498"},"user_tz":300},"outputId":"02076084-b193-4900-8917-265efc96b29e"},"source":["epochs = 50\n","model_name = \"vgg16_adam_e50_bs128_lr00001\"\n","train_losses, validation_losses = train(epochs, vgg16, trainloader, validloader, criterion, optimizer, model_name, valid_loss_min = np.Inf)"],"execution_count":null,"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch: 1/50, Train Loss: 2.31776, Validation Loss: 0.00025\n","Decrease in Validation Loss: inf to 0.00025. Saving Model...\n","Epoch: 2/50, Train Loss: 2.26063, Validation Loss: 0.00024\n","Decrease in Validation Loss: 0.00025 to 0.00024. Saving Model...\n","Epoch: 3/50, Train Loss: 2.24028, Validation Loss: 0.00024\n","Decrease in Validation Loss: 0.00024 to 0.00024. Saving Model...\n","Epoch: 4/50, Train Loss: 2.22810, Validation Loss: 0.00024\n","Decrease in Validation Loss: 0.00024 to 0.00024. Saving Model...\n","Epoch: 5/50, Train Loss: 2.21873, Validation Loss: 0.00024\n","Decrease in Validation Loss: 0.00024 to 0.00024. Saving Model...\n","Epoch: 6/50, Train Loss: 2.21200, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00024 to 0.00023. Saving Model...\n","Epoch: 7/50, Train Loss: 2.20643, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 8/50, Train Loss: 2.20332, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 9/50, Train Loss: 2.19777, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 10/50, Train Loss: 2.19632, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 11/50, Train Loss: 2.19417, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 12/50, Train Loss: 2.19199, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 13/50, Train Loss: 2.18784, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 14/50, Train Loss: 2.18813, Validation Loss: 0.00023\n","Epoch: 15/50, Train Loss: 2.18770, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 16/50, Train Loss: 2.18575, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 17/50, Train Loss: 2.18166, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 18/50, Train Loss: 2.17963, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 19/50, Train Loss: 2.17880, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 20/50, Train Loss: 2.17804, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 21/50, Train Loss: 2.17797, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 22/50, Train Loss: 2.17536, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 23/50, Train Loss: 2.17855, Validation Loss: 0.00023\n","Epoch: 24/50, Train Loss: 2.17196, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 25/50, Train Loss: 2.17515, Validation Loss: 0.00023\n","Epoch: 26/50, Train Loss: 2.17315, Validation Loss: 0.00023\n","Epoch: 27/50, Train Loss: 2.17335, Validation Loss: 0.00023\n","Epoch: 28/50, Train Loss: 2.17104, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 29/50, Train Loss: 2.17430, Validation Loss: 0.00023\n","Epoch: 30/50, Train Loss: 2.17071, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 31/50, Train Loss: 2.17265, Validation Loss: 0.00023\n","Epoch: 32/50, Train Loss: 2.16748, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 33/50, Train Loss: 2.16996, Validation Loss: 0.00023\n","Epoch: 34/50, Train Loss: 2.17044, Validation Loss: 0.00023\n","Epoch: 35/50, Train Loss: 2.16834, Validation Loss: 0.00023\n","Epoch: 36/50, Train Loss: 2.16944, Validation Loss: 0.00023\n","Epoch: 37/50, Train Loss: 2.16621, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 38/50, Train Loss: 2.16651, Validation Loss: 0.00023\n","Epoch: 39/50, Train Loss: 2.16858, Validation Loss: 0.00023\n","Epoch: 40/50, Train Loss: 2.16827, Validation Loss: 0.00023\n","Epoch: 41/50, Train Loss: 2.16563, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 42/50, Train Loss: 2.16488, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 43/50, Train Loss: 2.16827, Validation Loss: 0.00023\n","Epoch: 44/50, Train Loss: 2.16521, Validation Loss: 0.00023\n","Epoch: 45/50, Train Loss: 2.16334, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 46/50, Train Loss: 2.16426, Validation Loss: 0.00023\n","Epoch: 47/50, Train Loss: 2.16048, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 48/50, Train Loss: 2.16431, Validation Loss: 0.00023\n","Epoch: 49/50, Train Loss: 2.16409, Validation Loss: 0.00023\n","Epoch: 50/50, Train Loss: 2.16320, Validation Loss: 0.00023\n","\n","Time Taken for Training: 03:40:44.61\n","\n"]}]},{"cell_type":"code","metadata":{"id":"xBjV_4XNaRgV"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"O099CikpAc9P"},"source":["For VGG16 epochs: 50, bs: 64, lr:0.0003, Adam, CEL ==>\n","\n","Epoch: 1/50, Train Loss: 2.28554, Validation Loss: 0.00024\n","Decrease in Validation Loss: inf to 0.00024. Saving Model...\n","Epoch: 2/50, Train Loss: 2.24239, Validation Loss: 0.00024\n","Decrease in Validation Loss: 0.00024 to 0.00024. Saving Model...\n","Epoch: 3/50, Train Loss: 2.22811, Validation Loss: 0.00024\n","Decrease in Validation Loss: 0.00024 to 0.00024. Saving Model...\n","Epoch: 4/50, Train Loss: 2.22237, Validation Loss: 0.00024\n","Decrease in Validation Loss: 0.00024 to 0.00024. Saving Model...\n","Epoch: 5/50, Train Loss: 2.21775, Validation Loss: 0.00024\n","Decrease in Validation Loss: 0.00024 to 0.00024. Saving Model...\n","Epoch: 6/50, Train Loss: 2.21646, Validation Loss: 0.00024\n","Decrease in Validation Loss: 0.00024 to 0.00024. Saving Model...\n","Epoch: 7/50, Train Loss: 2.21535, Validation Loss: 0.00024\n","Decrease in Validation Loss: 0.00024 to 0.00024. Saving Model...\n","Epoch: 8/50, Train Loss: 2.21356, Validation Loss: 0.00024\n","Decrease in Validation Loss: 0.00024 to 0.00024. Saving Model...\n","Epoch: 9/50, Train Loss: 2.20949, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00024 to 0.00023. Saving Model...\n","Epoch: 10/50, Train Loss: 2.20985, Validation Loss: 0.00023\n","Epoch: 11/50, Train Loss: 2.20690, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 12/50, Train Loss: 2.20458, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 13/50, Train Loss: 2.20518, Validation Loss: 0.00023\n","Epoch: 14/50, Train Loss: 2.20700, Validation Loss: 0.00023\n","Epoch: 15/50, Train Loss: 2.20448, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 16/50, Train Loss: 2.20534, Validation Loss: 0.00023\n","Epoch: 17/50, Train Loss: 2.20095, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 18/50, Train Loss: 2.20319, Validation Loss: 0.00023\n","Epoch: 19/50, Train Loss: 2.20380, Validation Loss: 0.00023\n","Epoch: 20/50, Train Loss: 2.20010, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 21/50, Train Loss: 2.20219, Validation Loss: 0.00023\n","Epoch: 22/50, Train Loss: 2.20274, Validation Loss: 0.00023\n","Epoch: 23/50, Train Loss: 2.20128, Validation Loss: 0.00023\n","Epoch: 24/50, Train Loss: 2.20163, Validation Loss: 0.00023\n","Epoch: 25/50, Train Loss: 2.19891, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 26/50, Train Loss: 2.19744, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 27/50, Train Loss: 2.20165, Validation Loss: 0.00023\n","Epoch: 28/50, Train Loss: 2.19838, Validation Loss: 0.00023\n","Epoch: 29/50, Train Loss: 2.19874, Validation Loss: 0.00023\n","Epoch: 30/50, Train Loss: 2.20038, Validation Loss: 0.00023\n","Epoch: 31/50, Train Loss: 2.19978, Validation Loss: 0.00023\n","Epoch: 32/50, Train Loss: 2.19975, Validation Loss: 0.00023\n","Epoch: 33/50, Train Loss: 2.19574, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 34/50, Train Loss: 2.19687, Validation Loss: 0.00023\n","Epoch: 35/50, Train Loss: 2.19827, Validation Loss: 0.00023\n","Epoch: 36/50, Train Loss: 2.19958, Validation Loss: 0.00023\n","Epoch: 37/50, Train Loss: 2.19816, Validation Loss: 0.00023\n","Epoch: 38/50, Train Loss: 2.19544, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 39/50, Train Loss: 2.19912, Validation Loss: 0.00023\n","Epoch: 40/50, Train Loss: 2.19746, Validation Loss: 0.00023\n","Epoch: 41/50, Train Loss: 2.19474, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 42/50, Train Loss: 2.19657, Validation Loss: 0.00023\n","Epoch: 43/50, Train Loss: 2.19800, Validation Loss: 0.00023\n","Epoch: 44/50, Train Loss: 2.19834, Validation Loss: 0.00023\n","Epoch: 45/50, Train Loss: 2.19350, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 46/50, Train Loss: 2.19782, Validation Loss: 0.00023\n","Epoch: 47/50, Train Loss: 2.19661, Validation Loss: 0.00023\n","Epoch: 48/50, Train Loss: 2.19700, Validation Loss: 0.00023\n","Epoch: 49/50, Train Loss: 2.19594, Validation Loss: 0.00023\n","Epoch: 50/50, Train Loss: 2.19601, Validation Loss: 0.00023\n","\n","Time Taken for Training: 03:43:49.93"]},{"cell_type":"markdown","metadata":{"id":"NAVkPke1PvTd"},"source":["For VGG16 epochs: 50, bs: 128, lr:0.0001, Adam, CEL ==>\n","\n","Epoch: 1/50, Train Loss: 2.31776, Validation Loss: 0.00025\n","Decrease in Validation Loss: inf to 0.00025. Saving Model...\n","Epoch: 2/50, Train Loss: 2.26063, Validation Loss: 0.00024\n","Decrease in Validation Loss: 0.00025 to 0.00024. Saving Model...\n","Epoch: 3/50, Train Loss: 2.24028, Validation Loss: 0.00024\n","Decrease in Validation Loss: 0.00024 to 0.00024. Saving Model...\n","Epoch: 4/50, Train Loss: 2.22810, Validation Loss: 0.00024\n","Decrease in Validation Loss: 0.00024 to 0.00024. Saving Model...\n","Epoch: 5/50, Train Loss: 2.21873, Validation Loss: 0.00024\n","Decrease in Validation Loss: 0.00024 to 0.00024. Saving Model...\n","Epoch: 6/50, Train Loss: 2.21200, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00024 to 0.00023. Saving Model...\n","Epoch: 7/50, Train Loss: 2.20643, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 8/50, Train Loss: 2.20332, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 9/50, Train Loss: 2.19777, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 10/50, Train Loss: 2.19632, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 11/50, Train Loss: 2.19417, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 12/50, Train Loss: 2.19199, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 13/50, Train Loss: 2.18784, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 14/50, Train Loss: 2.18813, Validation Loss: 0.00023\n","Epoch: 15/50, Train Loss: 2.18770, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 16/50, Train Loss: 2.18575, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 17/50, Train Loss: 2.18166, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 18/50, Train Loss: 2.17963, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 19/50, Train Loss: 2.17880, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 20/50, Train Loss: 2.17804, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 21/50, Train Loss: 2.17797, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 22/50, Train Loss: 2.17536, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 23/50, Train Loss: 2.17855, Validation Loss: 0.00023\n","Epoch: 24/50, Train Loss: 2.17196, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 25/50, Train Loss: 2.17515, Validation Loss: 0.00023\n","Epoch: 26/50, Train Loss: 2.17315, Validation Loss: 0.00023\n","Epoch: 27/50, Train Loss: 2.17335, Validation Loss: 0.00023\n","Epoch: 28/50, Train Loss: 2.17104, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 29/50, Train Loss: 2.17430, Validation Loss: 0.00023\n","Epoch: 30/50, Train Loss: 2.17071, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 31/50, Train Loss: 2.17265, Validation Loss: 0.00023\n","Epoch: 32/50, Train Loss: 2.16748, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 33/50, Train Loss: 2.16996, Validation Loss: 0.00023\n","Epoch: 34/50, Train Loss: 2.17044, Validation Loss: 0.00023\n","Epoch: 35/50, Train Loss: 2.16834, Validation Loss: 0.00023\n","Epoch: 36/50, Train Loss: 2.16944, Validation Loss: 0.00023\n","Epoch: 37/50, Train Loss: 2.16621, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 38/50, Train Loss: 2.16651, Validation Loss: 0.00023\n","Epoch: 39/50, Train Loss: 2.16858, Validation Loss: 0.00023\n","Epoch: 40/50, Train Loss: 2.16827, Validation Loss: 0.00023\n","Epoch: 41/50, Train Loss: 2.16563, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 42/50, Train Loss: 2.16488, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 43/50, Train Loss: 2.16827, Validation Loss: 0.00023\n","Epoch: 44/50, Train Loss: 2.16521, Validation Loss: 0.00023\n","Epoch: 45/50, Train Loss: 2.16334, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 46/50, Train Loss: 2.16426, Validation Loss: 0.00023\n","Epoch: 47/50, Train Loss: 2.16048, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00023 to 0.00023. Saving Model...\n","Epoch: 48/50, Train Loss: 2.16431, Validation Loss: 0.00023\n","Epoch: 49/50, Train Loss: 2.16409, Validation Loss: 0.00023\n","Epoch: 50/50, Train Loss: 2.16320, Validation Loss: 0.00023\n","\n","Time Taken for Training: 03:40:44.61"]},{"cell_type":"markdown","metadata":{"id":"UgjlS_hDDqIx"},"source":["For VGG16 epochs: 50, bs: 64, lr:0.003, Adam, CEL ==>\n","\n","\n","Epoch: 1/50, Train Loss: 2.67199, Validation Loss: 0.00028\n","Decrease in Validation Loss: inf to 0.00028. Saving Model...\n","Epoch: 2/50, Train Loss: 2.72251, Validation Loss: 0.00029\n","Epoch: 3/50, Train Loss: 2.73586, Validation Loss: 0.00029\n","Epoch: 4/50, Train Loss: 2.74349, Validation Loss: 0.00029\n","Epoch: 5/50, Train Loss: 2.74190, Validation Loss: 0.00029\n","Epoch: 6/50, Train Loss: 2.73055, Validation Loss: 0.00029\n","Epoch: 7/50, Train Loss: 2.74791, Validation Loss: 0.00029\n","Epoch: 8/50, Train Loss: 2.75149, Validation Loss: 0.00029\n","Epoch: 9/50, Train Loss: 2.74317, Validation Loss: 0.00029\n","Epoch: 10/50, Train Loss: 2.74896, Validation Loss: 0.00029\n","Epoch: 11/50, Train Loss: 2.72996, Validation Loss: 0.00029\n","Epoch: 12/50, Train Loss: 2.75307, Validation Loss: 0.00029\n","Epoch: 13/50, Train Loss: 2.73515, Validation Loss: 0.00029\n","Epoch: 14/50, Train Loss: 2.74478, Validation Loss: 0.00029\n","Epoch: 15/50, Train Loss: 2.75202, Validation Loss: 0.00029\n","Epoch: 16/50, Train Loss: 2.73667, Validation Loss: 0.00029\n","Epoch: 17/50, Train Loss: 2.74505, Validation Loss: 0.00029\n","Epoch: 18/50, Train Loss: 2.73358, Validation Loss: 0.00029\n","Epoch: 19/50, Train Loss: 2.74158, Validation Loss: 0.00029\n","Epoch: 20/50, Train Loss: 2.75046, Validation Loss: 0.00029\n","Epoch: 21/50, Train Loss: 2.72986, Validation Loss: 0.00029\n","Epoch: 22/50, Train Loss: 2.74996, Validation Loss: 0.00029\n","Epoch: 23/50, Train Loss: 2.73268, Validation Loss: 0.00029\n","Epoch: 24/50, Train Loss: 2.73653, Validation Loss: 0.00029\n","Epoch: 25/50, Train Loss: 2.74742, Validation Loss: 0.00029\n","Epoch: 26/50, Train Loss: 2.73605, Validation Loss: 0.00029\n","Epoch: 27/50, Train Loss: 2.73706, Validation Loss: 0.00029\n","Epoch: 28/50, Train Loss: 2.74785, Validation Loss: 0.00029\n","Epoch: 29/50, Train Loss: 2.74744, Validation Loss: 0.00029\n","Epoch: 30/50, Train Loss: 2.75083, Validation Loss: 0.00029\n","Epoch: 31/50, Train Loss: 2.74357, Validation Loss: 0.00029\n","Epoch: 32/50, Train Loss: 2.73047, Validation Loss: 0.00029\n","Epoch: 33/50, Train Loss: 2.75133, Validation Loss: 0.00029\n","Epoch: 34/50, Train Loss: 2.74364, Validation Loss: 0.00029\n","Epoch: 35/50, Train Loss: 2.75224, Validation Loss: 0.00029\n","Epoch: 36/50, Train Loss: 2.74243, Validation Loss: 0.00029\n","Epoch: 37/50, Train Loss: 2.73548, Validation Loss: 0.00029\n","Epoch: 38/50, Train Loss: 2.75010, Validation Loss: 0.00029\n","Epoch: 39/50, Train Loss: 2.73646, Validation Loss: 0.00029\n","Epoch: 40/50, Train Loss: 2.74458, Validation Loss: 0.00029\n","Epoch: 41/50, Train Loss: 2.74269, Validation Loss: 0.00029\n","Epoch: 42/50, Train Loss: 2.75820, Validation Loss: 0.00029\n","Epoch: 43/50, Train Loss: 2.73327, Validation Loss: 0.00029\n","Epoch: 44/50, Train Loss: 2.75337, Validation Loss: 0.00029\n","Epoch: 45/50, Train Loss: 2.74269, Validation Loss: 0.00029\n","Epoch: 46/50, Train Loss: 2.73587, Validation Loss: 0.00029\n","Epoch: 47/50, Train Loss: 2.73948, Validation Loss: 0.00029\n","Epoch: 48/50, Train Loss: 2.75167, Validation Loss: 0.00029\n","Epoch: 49/50, Train Loss: 2.74497, Validation Loss: 0.00029\n","Epoch: 50/50, Train Loss: 2.74715, Validation Loss: 0.00029\n","\n","Time Taken for Training: 03:30:30.93"]},{"cell_type":"code","metadata":{"id":"f3DhtwU2Dxnk"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"EVvCWCUb_lQu","executionInfo":{"elapsed":68232,"status":"ok","timestamp":1636751649044,"user":{"displayName":"GPU DeDe","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09796458770182087498"},"user_tz":300},"outputId":"54a3147f-0a3f-4ca2-d602-20cb5501a63b"},"source":["test(vgg16, testloader, criterion, 14)"],"execution_count":null,"outputs":[{"name":"stdout","output_type":"stream","text":["Test Loss: 2.1552134275067902\n","Test Accuracy of Atelectasis: 15.672%. Frac: 294.0/1876.0\n","Test Accuracy of Cardiomegaly: 1.965%. Frac: 10.0/509.0\n","Test Accuracy of Consolidation: 2.169%. Frac: 21.0/968.0\n","Test Accuracy of Edema: 0.554%. Frac: 2.0/361.0\n","Test Accuracy of Effusion: 49.675%. Frac: 1375.0/2768.0\n","Test Accuracy of Emphysema: 1.050%. Frac: 4.0/381.0\n","Test Accuracy of Fibrosis: 0.000%. Frac: 0.0/145.0\n","Test Accuracy of Infiltration: 48.309%. Frac: 1514.0/3134.0\n","Test Accuracy of Mass: 1.296%. Frac: 15.0/1157.0\n","Test Accuracy of No Finding: 34.963%. Frac: 422.0/1207.0\n","Test Accuracy of Nodule: 7.411%. Frac: 81.0/1093.0\n","Test Accuracy of Pleural_Thickening: 0.000%. Frac: 0.0/502.0\n","Test Accuracy of Pneumonia: 0.518%. Frac: 1.0/193.0\n","Test Accuracy of Pneumothorax: 24.068%. Frac: 297.0/1234.0\n","\n"," Test Accuracy (Overall): 25.992. Frac 4036.0/15528.0\n"]}]},{"cell_type":"code","metadata":{"id":"y66nFp24jshy","executionInfo":{"status":"ok","timestamp":1636917336280,"user_tz":300,"elapsed":761,"user":{"displayName":"GPU DeDe","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09796458770182087498"}}},"source":["# Densenet161 Classifier\n","densenet161 = models.densenet161(pretrained = True)\n","# densenet161\n","\n","\n","# Freeze Parameters so we dont backprop through them:\n","for param in densenet161.parameters():\n","    param.requires_grad = False\n","\n","\n","# Create Custom Classifier:\n","classifier = nn.Sequential(nn.Linear(2208, 512),\n","                           nn.ReLU(),\n","                           nn.Dropout(0.20),\n","                           nn.Linear(512, 14))\n","                          #  nn.LogSoftmax(dim = 1))\n","\n","# Assign Classifier\n","densenet161.classifier = classifier\n","# densenet161.classifier"],"execution_count":35,"outputs":[]},{"cell_type":"code","metadata":{"id":"rhOGwJq01rOT","executionInfo":{"status":"ok","timestamp":1636917337679,"user_tz":300,"elapsed":593,"user":{"displayName":"GPU DeDe","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09796458770182087498"}}},"source":["# loading previously trained model to train for more epochs:\n","model_name = \"densenet161_adam_e50_bs128_lr00001\"\n","densenet161 = loadModel(model_name, densenet161)"],"execution_count":36,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qIw2hlxo_x2N","outputId":"77d751c7-c1f1-4032-f3a0-daa9d2f8fd26"},"source":["densenet161.to(device)\n","criterion = nn.CrossEntropyLoss()\n","optimizer = optim.Adam(densenet161.classifier.parameters(), lr = 0.0001)\n","epochs = 70\n","model_name = \"densenet161_adam_e120_bs128_lr00001\"\n","train_losses, validation_losses = train(epochs, densenet161, trainloader, validloader, criterion, optimizer, model_name, valid_loss_min = np.Inf)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch: 1/70, Train Loss: 1.51704, Validation Loss: 0.00016\n","Decrease in Validation Loss: inf to 0.00016. Saving Model...\n","Epoch: 2/70, Train Loss: 1.50281, Validation Loss: 0.00016\n","Decrease in Validation Loss: 0.00016 to 0.00016. Saving Model...\n","Epoch: 3/70, Train Loss: 1.49328, Validation Loss: 0.00016\n","Decrease in Validation Loss: 0.00016 to 0.00016. Saving Model...\n","Epoch: 4/70, Train Loss: 1.48225, Validation Loss: 0.00016\n","Decrease in Validation Loss: 0.00016 to 0.00016. Saving Model...\n","Epoch: 5/70, Train Loss: 1.47052, Validation Loss: 0.00016\n","Decrease in Validation Loss: 0.00016 to 0.00016. Saving Model...\n","Epoch: 6/70, Train Loss: 1.46192, Validation Loss: 0.00016\n","Decrease in Validation Loss: 0.00016 to 0.00016. Saving Model...\n","Epoch: 7/70, Train Loss: 1.45276, Validation Loss: 0.00015\n","Decrease in Validation Loss: 0.00016 to 0.00015. Saving Model...\n","Epoch: 8/70, Train Loss: 1.43841, Validation Loss: 0.00015\n","Decrease in Validation Loss: 0.00015 to 0.00015. Saving Model...\n","Epoch: 9/70, Train Loss: 1.43181, Validation Loss: 0.00015\n","Decrease in Validation Loss: 0.00015 to 0.00015. Saving Model...\n","Epoch: 10/70, Train Loss: 1.41935, Validation Loss: 0.00015\n","Decrease in Validation Loss: 0.00015 to 0.00015. Saving Model...\n","Epoch: 11/70, Train Loss: 1.40914, Validation Loss: 0.00015\n","Decrease in Validation Loss: 0.00015 to 0.00015. Saving Model...\n","Epoch: 12/70, Train Loss: 1.39794, Validation Loss: 0.00015\n","Decrease in Validation Loss: 0.00015 to 0.00015. Saving Model...\n","Epoch: 13/70, Train Loss: 1.38857, Validation Loss: 0.00015\n","Decrease in Validation Loss: 0.00015 to 0.00015. Saving Model...\n","Epoch: 14/70, Train Loss: 1.37820, Validation Loss: 0.00015\n","Decrease in Validation Loss: 0.00015 to 0.00015. Saving Model...\n","Epoch: 15/70, Train Loss: 1.36761, Validation Loss: 0.00015\n","Decrease in Validation Loss: 0.00015 to 0.00015. Saving Model...\n","Epoch: 16/70, Train Loss: 1.36051, Validation Loss: 0.00014\n","Decrease in Validation Loss: 0.00015 to 0.00014. Saving Model...\n","Epoch: 17/70, Train Loss: 1.34704, Validation Loss: 0.00014\n","Decrease in Validation Loss: 0.00014 to 0.00014. Saving Model...\n","Epoch: 18/70, Train Loss: 1.34006, Validation Loss: 0.00014\n","Decrease in Validation Loss: 0.00014 to 0.00014. Saving Model...\n","Epoch: 19/70, Train Loss: 1.33472, Validation Loss: 0.00014\n","Decrease in Validation Loss: 0.00014 to 0.00014. Saving Model...\n","Epoch: 20/70, Train Loss: 1.32032, Validation Loss: 0.00014\n","Decrease in Validation Loss: 0.00014 to 0.00014. Saving Model...\n","Epoch: 21/70, Train Loss: 1.31681, Validation Loss: 0.00014\n","Decrease in Validation Loss: 0.00014 to 0.00014. Saving Model...\n","Epoch: 22/70, Train Loss: 1.30657, Validation Loss: 0.00014\n","Decrease in Validation Loss: 0.00014 to 0.00014. Saving Model...\n","Epoch: 23/70, Train Loss: 1.29749, Validation Loss: 0.00014\n","Decrease in Validation Loss: 0.00014 to 0.00014. Saving Model...\n","Epoch: 24/70, Train Loss: 1.28641, Validation Loss: 0.00014\n","Decrease in Validation Loss: 0.00014 to 0.00014. Saving Model...\n","Epoch: 25/70, Train Loss: 1.28009, Validation Loss: 0.00014\n","Decrease in Validation Loss: 0.00014 to 0.00014. Saving Model...\n","Epoch: 26/70, Train Loss: 1.27298, Validation Loss: 0.00014\n","Decrease in Validation Loss: 0.00014 to 0.00014. Saving Model...\n","Epoch: 27/70, Train Loss: 1.25867, Validation Loss: 0.00013\n","Decrease in Validation Loss: 0.00014 to 0.00013. Saving Model...\n","Epoch: 28/70, Train Loss: 1.25446, Validation Loss: 0.00013\n","Decrease in Validation Loss: 0.00013 to 0.00013. Saving Model...\n","Epoch: 29/70, Train Loss: 1.24415, Validation Loss: 0.00013\n","Decrease in Validation Loss: 0.00013 to 0.00013. Saving Model...\n","Epoch: 30/70, Train Loss: 1.24185, Validation Loss: 0.00013\n","Decrease in Validation Loss: 0.00013 to 0.00013. Saving Model...\n","Epoch: 31/70, Train Loss: 1.22885, Validation Loss: 0.00013\n","Decrease in Validation Loss: 0.00013 to 0.00013. Saving Model...\n","Epoch: 32/70, Train Loss: 1.22525, Validation Loss: 0.00013\n","Decrease in Validation Loss: 0.00013 to 0.00013. Saving Model...\n","Epoch: 33/70, Train Loss: 1.21160, Validation Loss: 0.00013\n","Decrease in Validation Loss: 0.00013 to 0.00013. Saving Model...\n","Epoch: 34/70, Train Loss: 1.20634, Validation Loss: 0.00013\n","Decrease in Validation Loss: 0.00013 to 0.00013. Saving Model...\n","Epoch: 35/70, Train Loss: 1.19906, Validation Loss: 0.00013\n","Decrease in Validation Loss: 0.00013 to 0.00013. Saving Model...\n","Epoch: 36/70, Train Loss: 1.19369, Validation Loss: 0.00013\n","Decrease in Validation Loss: 0.00013 to 0.00013. Saving Model...\n","Epoch: 37/70, Train Loss: 1.18525, Validation Loss: 0.00013\n","Decrease in Validation Loss: 0.00013 to 0.00013. Saving Model...\n","Epoch: 38/70, Train Loss: 1.17366, Validation Loss: 0.00012\n","Decrease in Validation Loss: 0.00013 to 0.00012. Saving Model...\n","Epoch: 39/70, Train Loss: 1.16713, Validation Loss: 0.00012\n","Decrease in Validation Loss: 0.00012 to 0.00012. Saving Model...\n","Epoch: 40/70, Train Loss: 1.16143, Validation Loss: 0.00012\n","Decrease in Validation Loss: 0.00012 to 0.00012. Saving Model...\n","Epoch: 41/70, Train Loss: 1.15369, Validation Loss: 0.00012\n","Decrease in Validation Loss: 0.00012 to 0.00012. Saving Model...\n","Epoch: 42/70, Train Loss: 1.15158, Validation Loss: 0.00012\n","Decrease in Validation Loss: 0.00012 to 0.00012. Saving Model...\n","Epoch: 43/70, Train Loss: 1.14190, Validation Loss: 0.00012\n","Decrease in Validation Loss: 0.00012 to 0.00012. Saving Model...\n","Epoch: 44/70, Train Loss: 1.13406, Validation Loss: 0.00012\n","Decrease in Validation Loss: 0.00012 to 0.00012. Saving Model...\n","Epoch: 45/70, Train Loss: 1.12791, Validation Loss: 0.00012\n","Decrease in Validation Loss: 0.00012 to 0.00012. Saving Model...\n","Epoch: 46/70, Train Loss: 1.12071, Validation Loss: 0.00012\n","Decrease in Validation Loss: 0.00012 to 0.00012. Saving Model...\n","Epoch: 47/70, Train Loss: 1.11825, Validation Loss: 0.00012\n","Decrease in Validation Loss: 0.00012 to 0.00012. Saving Model...\n","Epoch: 48/70, Train Loss: 1.10779, Validation Loss: 0.00012\n","Decrease in Validation Loss: 0.00012 to 0.00012. Saving Model...\n","Epoch: 49/70, Train Loss: 1.10561, Validation Loss: 0.00012\n","Decrease in Validation Loss: 0.00012 to 0.00012. Saving Model...\n","Epoch: 50/70, Train Loss: 1.09983, Validation Loss: 0.00012\n","Decrease in Validation Loss: 0.00012 to 0.00012. Saving Model...\n","Epoch: 51/70, Train Loss: 1.08751, Validation Loss: 0.00012\n","Decrease in Validation Loss: 0.00012 to 0.00012. Saving Model...\n","Epoch: 52/70, Train Loss: 1.08894, Validation Loss: 0.00012\n","Epoch: 53/70, Train Loss: 1.07990, Validation Loss: 0.00011\n","Decrease in Validation Loss: 0.00012 to 0.00011. Saving Model...\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"TO8qtD7RjvOI","executionInfo":{"elapsed":3379,"status":"ok","timestamp":1636861848321,"user":{"displayName":"GPU DeDe","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09796458770182087498"},"user_tz":300},"outputId":"b23028f6-bcff-42bc-f8ac-739240c5b1e5"},"source":["model_name = \"densenet161_adam_e50_bs128_lr00001\"\n","\n","densenet161 = loadModel(model_name, densenet161)\n","densenet161.to(device)"],"execution_count":null,"outputs":[{"data":{"text/plain":["DenseNet(\n","  (features): Sequential(\n","    (conv0): Conv2d(3, 96, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n","    (norm0): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","    (relu0): ReLU(inplace=True)\n","    (pool0): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n","    (denseblock1): _DenseBlock(\n","      (denselayer1): _DenseLayer(\n","        (norm1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(96, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer2): _DenseLayer(\n","        (norm1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(144, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer3): _DenseLayer(\n","        (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer4): _DenseLayer(\n","        (norm1): BatchNorm2d(240, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(240, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer5): _DenseLayer(\n","        (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(288, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer6): _DenseLayer(\n","        (norm1): BatchNorm2d(336, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(336, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","    )\n","    (transition1): _Transition(\n","      (norm): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","      (conv): Conv2d(384, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n","    )\n","    (denseblock2): _DenseBlock(\n","      (denselayer1): _DenseLayer(\n","        (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer2): _DenseLayer(\n","        (norm1): BatchNorm2d(240, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(240, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer3): _DenseLayer(\n","        (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(288, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer4): _DenseLayer(\n","        (norm1): BatchNorm2d(336, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(336, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer5): _DenseLayer(\n","        (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(384, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer6): _DenseLayer(\n","        (norm1): BatchNorm2d(432, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(432, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer7): _DenseLayer(\n","        (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(480, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer8): _DenseLayer(\n","        (norm1): BatchNorm2d(528, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(528, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer9): _DenseLayer(\n","        (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(576, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer10): _DenseLayer(\n","        (norm1): BatchNorm2d(624, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(624, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer11): _DenseLayer(\n","        (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(672, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer12): _DenseLayer(\n","        (norm1): BatchNorm2d(720, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(720, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","    )\n","    (transition2): _Transition(\n","      (norm): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","      (conv): Conv2d(768, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n","    )\n","    (denseblock3): _DenseBlock(\n","      (denselayer1): _DenseLayer(\n","        (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(384, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer2): _DenseLayer(\n","        (norm1): BatchNorm2d(432, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(432, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer3): _DenseLayer(\n","        (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(480, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer4): _DenseLayer(\n","        (norm1): BatchNorm2d(528, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(528, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer5): _DenseLayer(\n","        (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(576, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer6): _DenseLayer(\n","        (norm1): BatchNorm2d(624, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(624, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer7): _DenseLayer(\n","        (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(672, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer8): _DenseLayer(\n","        (norm1): BatchNorm2d(720, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(720, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer9): _DenseLayer(\n","        (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer10): _DenseLayer(\n","        (norm1): BatchNorm2d(816, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(816, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer11): _DenseLayer(\n","        (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(864, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer12): _DenseLayer(\n","        (norm1): BatchNorm2d(912, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(912, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer13): _DenseLayer(\n","        (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(960, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer14): _DenseLayer(\n","        (norm1): BatchNorm2d(1008, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(1008, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer15): _DenseLayer(\n","        (norm1): BatchNorm2d(1056, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(1056, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer16): _DenseLayer(\n","        (norm1): BatchNorm2d(1104, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(1104, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer17): _DenseLayer(\n","        (norm1): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(1152, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer18): _DenseLayer(\n","        (norm1): BatchNorm2d(1200, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(1200, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer19): _DenseLayer(\n","        (norm1): BatchNorm2d(1248, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(1248, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer20): _DenseLayer(\n","        (norm1): BatchNorm2d(1296, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(1296, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer21): _DenseLayer(\n","        (norm1): BatchNorm2d(1344, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(1344, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer22): _DenseLayer(\n","        (norm1): BatchNorm2d(1392, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(1392, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer23): _DenseLayer(\n","        (norm1): BatchNorm2d(1440, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(1440, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer24): _DenseLayer(\n","        (norm1): BatchNorm2d(1488, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(1488, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer25): _DenseLayer(\n","        (norm1): BatchNorm2d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(1536, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer26): _DenseLayer(\n","        (norm1): BatchNorm2d(1584, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(1584, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer27): _DenseLayer(\n","        (norm1): BatchNorm2d(1632, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(1632, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer28): _DenseLayer(\n","        (norm1): BatchNorm2d(1680, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(1680, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer29): _DenseLayer(\n","        (norm1): BatchNorm2d(1728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(1728, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer30): _DenseLayer(\n","        (norm1): BatchNorm2d(1776, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(1776, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer31): _DenseLayer(\n","        (norm1): BatchNorm2d(1824, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(1824, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer32): _DenseLayer(\n","        (norm1): BatchNorm2d(1872, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(1872, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer33): _DenseLayer(\n","        (norm1): BatchNorm2d(1920, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(1920, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer34): _DenseLayer(\n","        (norm1): BatchNorm2d(1968, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(1968, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer35): _DenseLayer(\n","        (norm1): BatchNorm2d(2016, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(2016, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer36): _DenseLayer(\n","        (norm1): BatchNorm2d(2064, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(2064, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","    )\n","    (transition3): _Transition(\n","      (norm): BatchNorm2d(2112, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","      (conv): Conv2d(2112, 1056, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n","    )\n","    (denseblock4): _DenseBlock(\n","      (denselayer1): _DenseLayer(\n","        (norm1): BatchNorm2d(1056, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(1056, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer2): _DenseLayer(\n","        (norm1): BatchNorm2d(1104, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(1104, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer3): _DenseLayer(\n","        (norm1): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(1152, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer4): _DenseLayer(\n","        (norm1): BatchNorm2d(1200, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(1200, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer5): _DenseLayer(\n","        (norm1): BatchNorm2d(1248, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(1248, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer6): _DenseLayer(\n","        (norm1): BatchNorm2d(1296, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(1296, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer7): _DenseLayer(\n","        (norm1): BatchNorm2d(1344, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(1344, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer8): _DenseLayer(\n","        (norm1): BatchNorm2d(1392, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(1392, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer9): _DenseLayer(\n","        (norm1): BatchNorm2d(1440, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(1440, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer10): _DenseLayer(\n","        (norm1): BatchNorm2d(1488, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(1488, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer11): _DenseLayer(\n","        (norm1): BatchNorm2d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(1536, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer12): _DenseLayer(\n","        (norm1): BatchNorm2d(1584, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(1584, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer13): _DenseLayer(\n","        (norm1): BatchNorm2d(1632, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(1632, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer14): _DenseLayer(\n","        (norm1): BatchNorm2d(1680, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(1680, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer15): _DenseLayer(\n","        (norm1): BatchNorm2d(1728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(1728, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer16): _DenseLayer(\n","        (norm1): BatchNorm2d(1776, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(1776, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer17): _DenseLayer(\n","        (norm1): BatchNorm2d(1824, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(1824, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer18): _DenseLayer(\n","        (norm1): BatchNorm2d(1872, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(1872, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer19): _DenseLayer(\n","        (norm1): BatchNorm2d(1920, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(1920, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer20): _DenseLayer(\n","        (norm1): BatchNorm2d(1968, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(1968, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer21): _DenseLayer(\n","        (norm1): BatchNorm2d(2016, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(2016, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer22): _DenseLayer(\n","        (norm1): BatchNorm2d(2064, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(2064, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer23): _DenseLayer(\n","        (norm1): BatchNorm2d(2112, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(2112, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (denselayer24): _DenseLayer(\n","        (norm1): BatchNorm2d(2160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu1): ReLU(inplace=True)\n","        (conv1): Conv2d(2160, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu2): ReLU(inplace=True)\n","        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","    )\n","    (norm5): BatchNorm2d(2208, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  )\n","  (classifier): Sequential(\n","    (0): Linear(in_features=2208, out_features=512, bias=True)\n","    (1): ReLU()\n","    (2): Dropout(p=0.2, inplace=False)\n","    (3): Linear(in_features=512, out_features=14, bias=True)\n","  )\n",")"]},"execution_count":32,"metadata":{},"output_type":"execute_result"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xpI4Rzk7ivkK","executionInfo":{"status":"ok","timestamp":1636917129723,"user_tz":300,"elapsed":289302,"user":{"displayName":"GPU DeDe","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09796458770182087498"}},"outputId":"d234b0d6-af4a-49c3-efe5-7c03ed6682fd"},"source":["criterion = nn.CrossEntropyLoss()\n","densenet161.to(device)\n","test(densenet161, testloader, criterion, 14)"],"execution_count":34,"outputs":[{"output_type":"stream","name":"stdout","text":["Test Loss: 1.4303724972452871\n","Test Accuracy of Atelectasis: 43.010%. Frac: 2732.0/6352.0\n","Test Accuracy of Cardiomegaly: 57.837%. Frac: 1000.0/1729.0\n","Test Accuracy of Consolidation: 29.915%. Frac: 986.0/3296.0\n","Test Accuracy of Edema: 40.994%. Frac: 503.0/1227.0\n","Test Accuracy of Effusion: 67.800%. Frac: 6380.0/9410.0\n","Test Accuracy of Emphysema: 45.666%. Frac: 590.0/1292.0\n","Test Accuracy of Fibrosis: 8.652%. Frac: 43.0/497.0\n","Test Accuracy of Infiltration: 62.378%. Frac: 6632.0/10632.0\n","Test Accuracy of Mass: 37.012%. Frac: 1459.0/3942.0\n","Test Accuracy of No Finding: 43.426%. Frac: 1787.0/4115.0\n","Test Accuracy of Nodule: 34.561%. Frac: 1276.0/3692.0\n","Test Accuracy of Pleural_Thickening: 19.658%. Frac: 333.0/1694.0\n","Test Accuracy of Pneumonia: 67.293%. Frac: 430.0/639.0\n","Test Accuracy of Pneumothorax: 43.479%. Frac: 1817.0/4179.0\n","\n"," Test Accuracy (Overall): 49.279. Frac 25968.0/52696.0\n"]}]},{"cell_type":"code","metadata":{"id":"jDAeXxPbPQms"},"source":["# Densenet161 Classifier\n","\n","\n","densenet161_rg = models.densenet161(pretrained = True)\n","# densenet161\n","\n","# Freeze Parameters so we dont backprop through them:\n","# for param in densenet161.parameters():\n","#     param.requires_grad = False\n","\n","\n","# Create Custom Classifier:\n","classifier = nn.Sequential(nn.Linear(2208, 512),\n","                           nn.ReLU(),\n","                           nn.Dropout(0.20),\n","                           nn.Linear(512, 14))\n","                          #  nn.LogSoftmax(dim = 1))\n","\n","# Assign Classifier\n","densenet161_rg.classifier = classifier\n","# densenet161_rg.classifier\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"background_save":true,"base_uri":"https://localhost:8080/"},"id":"pPP5n1iPlA_i","outputId":"f311ae8a-7620-41b0-c4b3-81ff3af404ee"},"source":["densenet161_rg.to(device)\n","criterion = nn.CrossEntropyLoss()\n","optimizer = optim.Adam(densenet161_rg.classifier.parameters(), lr = 0.0001)\n","epochs = 120\n","model_name = \"densenet161_adam_e50_bs128_lr00001_reqgrad\"\n","train_losses, validation_losses = train(epochs, densenet161_rg, trainloader, validloader, criterion, optimizer, model_name, valid_loss_min = np.Inf)"],"execution_count":null,"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch: 1/120, Train Loss: 2.23179, Validation Loss: 0.00024\n","Decrease in Validation Loss: inf to 0.00024. Saving Model...\n","Epoch: 2/120, Train Loss: 2.14438, Validation Loss: 0.00023\n","Decrease in Validation Loss: 0.00024 to 0.00023. Saving Model...\n","Epoch: 3/120, Train Loss: 2.11189, Validation Loss: 0.00022\n","Decrease in Validation Loss: 0.00023 to 0.00022. Saving Model...\n","Epoch: 4/120, Train Loss: 2.08822, Validation Loss: 0.00022\n","Decrease in Validation Loss: 0.00022 to 0.00022. Saving Model...\n","Epoch: 5/120, Train Loss: 2.06960, Validation Loss: 0.00022\n","Decrease in Validation Loss: 0.00022 to 0.00022. Saving Model...\n","Epoch: 6/120, Train Loss: 2.05216, Validation Loss: 0.00022\n","Decrease in Validation Loss: 0.00022 to 0.00022. Saving Model...\n","Epoch: 7/120, Train Loss: 2.04023, Validation Loss: 0.00022\n","Decrease in Validation Loss: 0.00022 to 0.00022. Saving Model...\n","Epoch: 8/120, Train Loss: 2.02667, Validation Loss: 0.00022\n","Decrease in Validation Loss: 0.00022 to 0.00022. Saving Model...\n","Epoch: 9/120, Train Loss: 2.01381, Validation Loss: 0.00021\n","Decrease in Validation Loss: 0.00022 to 0.00021. Saving Model...\n","Epoch: 10/120, Train Loss: 2.00398, Validation Loss: 0.00021\n","Decrease in Validation Loss: 0.00021 to 0.00021. Saving Model...\n","Epoch: 11/120, Train Loss: 1.98794, Validation Loss: 0.00021\n","Decrease in Validation Loss: 0.00021 to 0.00021. Saving Model...\n","Epoch: 12/120, Train Loss: 1.97803, Validation Loss: 0.00021\n","Decrease in Validation Loss: 0.00021 to 0.00021. Saving Model...\n","Epoch: 13/120, Train Loss: 1.96462, Validation Loss: 0.00021\n","Decrease in Validation Loss: 0.00021 to 0.00021. Saving Model...\n","Epoch: 14/120, Train Loss: 1.95362, Validation Loss: 0.00021\n","Decrease in Validation Loss: 0.00021 to 0.00021. Saving Model...\n","Epoch: 15/120, Train Loss: 1.94193, Validation Loss: 0.00021\n","Decrease in Validation Loss: 0.00021 to 0.00021. Saving Model...\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QsnTyuBymVfJ","executionInfo":{"elapsed":316,"status":"ok","timestamp":1636862480529,"user":{"displayName":"GPU DeDe","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09796458770182087498"},"user_tz":300},"outputId":"b467f520-d984-4fd3-d625-40557477ea20"},"source":["!nvidia-smi\n"],"execution_count":null,"outputs":[{"name":"stdout","output_type":"stream","text":["Sun Nov 14 04:01:20 2021       \n","+-----------------------------------------------------------------------------+\n","| NVIDIA-SMI 495.44       Driver Version: 460.32.03    CUDA Version: 11.2     |\n","|-------------------------------+----------------------+----------------------+\n","| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n","| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n","|                               |                      |               MIG M. |\n","|===============================+======================+======================|\n","|   0  Tesla P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\n","| N/A   44C    P0    34W / 250W |  16159MiB / 16280MiB |      0%      Default |\n","|                               |                      |                  N/A |\n","+-------------------------------+----------------------+----------------------+\n","                                                                               \n","+-----------------------------------------------------------------------------+\n","| Processes:                                                                  |\n","|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n","|        ID   ID                                                   Usage      |\n","|=============================================================================|\n","|  No running processes found                                                 |\n","+-----------------------------------------------------------------------------+\n"]}]},{"cell_type":"code","metadata":{"id":"eo_hY478nE09"},"source":[""],"execution_count":null,"outputs":[]}]}