From c9d3858df8ab2681f041baf1a5c9d259d2867ce3 Mon Sep 17 00:00:00 2001
From: Lanka Naga Sai Deep <cb.en.p2aid19019@cb.students.amrita.edu>
Date: Tue, 21 Sep 2021 17:37:24 +0530
Subject: [PATCH] Update accuracy graph.png, AlexNet.ipynb, confusion
matrix.png, examples predicted and target.png, loss.png, helper_dataset.py,
helper_evaluation.py, helper_plotting.py, helper_train.py,
minibatch_loss_list.txt, train_acc_list.txt, valid_acc_list.txt files
---
AlexNet.ipynb | 2456 +++++++++++++++++++++++++++++
accuracy graph.png | Bin 0 -> 20585 bytes
confusion matrix.png | Bin 0 -> 8883 bytes
examples predicted and target.png | Bin 0 -> 64523 bytes
helper_dataset.py | 149 ++
helper_evaluation.py | 78 +
helper_plotting.py | 190 +++
helper_train.py | 76 +
loss.png | Bin 0 -> 21077 bytes
minibatch_loss_list.txt | 1 +
train_acc_list.txt | 1 +
valid_acc_list.txt | 1 +
12 files changed, 2952 insertions(+)
create mode 100644 AlexNet.ipynb
create mode 100644 accuracy graph.png
create mode 100644 confusion matrix.png
create mode 100644 examples predicted and target.png
create mode 100644 helper_dataset.py
create mode 100644 helper_evaluation.py
create mode 100644 helper_plotting.py
create mode 100644 helper_train.py
create mode 100644 loss.png
create mode 100644 minibatch_loss_list.txt
create mode 100644 train_acc_list.txt
create mode 100644 valid_acc_list.txt
diff --git a/AlexNet.ipynb b/AlexNet.ipynb
new file mode 100644
index 0000000..070f97e
--- /dev/null
+++ b/AlexNet.ipynb
@@ -0,0 +1,2456 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import torch\n",
+ "import torchvision\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# From local helper files\n",
+ "from helper_evaluation import set_all_seeds, set_deterministic, compute_confusion_matrix\n",
+ "from helper_train import train_model\n",
+ "from helper_plotting import plot_training_loss, plot_accuracy, show_examples, plot_confusion_matrix\n",
+ "from helper_dataset import get_dataloaders_cifar10, UnNormalize"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "RANDOM_SEED = 123\n",
+ "BATCH_SIZE = 32\n",
+ "NUM_EPOCHS = 100\n",
+ "DEVICE = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "set_all_seeds(RANDOM_SEED)\n",
+ "#set_deterministic()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from torchvision import datasets, transforms\n",
+ "from torch.utils.data import DataLoader, random_split"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def UploadData(path, train):\n",
+ " #set up transforms for train and test datasets\n",
+ " train_transforms = transforms.Compose([transforms.Grayscale(num_output_channels=1), transforms.Resize(512), transforms.CenterCrop(511), transforms.RandomRotation(30),transforms.RandomHorizontalFlip(), transforms.transforms.ToTensor()]) \n",
+ " valid_transforms = transforms.Compose([transforms.Grayscale(num_output_channels=1), transforms.Resize(512), transforms.CenterCrop(511), transforms.transforms.ToTensor()]) \n",
+ " #test_transforms = transforms.Compose([transforms.Grayscale(num_output_channels=1), transforms.Resize(512), transforms.CenterCrop(511), transforms.ToTensor()])\n",
+ " \n",
+ " #set up datasets from Image Folders\n",
+ " train_dataset = datasets.ImageFolder(path + '/train', transform=train_transforms)\n",
+ " valid_dataset = datasets.ImageFolder(path + '/validation', transform=valid_transforms)\n",
+ " #test_dataset = datasets.ImageFolder(path + '/test', transform=test_transforms)\n",
+ "\n",
+ " #set up dataloaders with batch size of 32\n",
+ " trainloader = torch.utils.data.DataLoader(train_dataset, batch_size=32, shuffle=True)\n",
+ " validloader = torch.utils.data.DataLoader(valid_dataset, batch_size=32, shuffle=True)\n",
+ " #testloader = torch.utils.data.DataLoader(test_dataset, batch_size=32, shuffle=True)\n",
+ " \n",
+ " return trainloader, validloader #, testloader"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "train_loader, valid_loader = UploadData(\"/home/user/research/CXR_Covid-19_Challenge\", True) #, testloader"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Image batch dimensions: torch.Size([32, 1, 511, 511])\n",
+ "Image label dimensions: torch.Size([32])\n",
+ "Class labels of 10 examples: tensor([2, 1, 2, 1, 2, 1, 1, 1, 2, 0])\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Checking the dataset\n",
+ "for images, labels in train_loader: \n",
+ " print('Image batch dimensions:', images.shape)\n",
+ " print('Image label dimensions:', labels.shape)\n",
+ " print('Class labels of 10 examples:', labels[:10])\n",
+ " break"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class AlexNet(torch.nn.Module):\n",
+ "\n",
+ " def __init__(self, num_classes):\n",
+ " super().__init__()\n",
+ " self.features = torch.nn.Sequential(\n",
+ " torch.nn.Conv2d(1, 64, kernel_size=11, stride=4, padding=2),\n",
+ " torch.nn.ReLU(inplace=True),\n",
+ " torch.nn.MaxPool2d(kernel_size=3, stride=2),\n",
+ " torch.nn.Conv2d(64, 192, kernel_size=5, padding=2),\n",
+ " torch.nn.ReLU(inplace=True),\n",
+ " torch.nn.MaxPool2d(kernel_size=3, stride=2),\n",
+ " torch.nn.Conv2d(192, 384, kernel_size=3, padding=1),\n",
+ " torch.nn.ReLU(inplace=True),\n",
+ " torch.nn.Conv2d(384, 256, kernel_size=3, padding=1),\n",
+ " torch.nn.ReLU(inplace=True),\n",
+ " torch.nn.Conv2d(256, 256, kernel_size=3, padding=1),\n",
+ " torch.nn.ReLU(inplace=True),\n",
+ " torch.nn.MaxPool2d(kernel_size=3, stride=2),\n",
+ " )\n",
+ " self.avgpool = torch.nn.AdaptiveAvgPool2d((6, 6))\n",
+ " self.classifier = torch.nn.Sequential(\n",
+ " torch.nn.Dropout(0.5),\n",
+ " torch.nn.Linear(256 * 6 * 6, 4096),\n",
+ " torch.nn.ReLU(inplace=True),\n",
+ " torch.nn.Dropout(0.5),\n",
+ " torch.nn.Linear(4096, 4096),\n",
+ " torch.nn.ReLU(inplace=True),\n",
+ " torch.nn.Linear(4096, num_classes)\n",
+ " )\n",
+ "\n",
+ " def forward(self, x):\n",
+ " x = self.features(x)\n",
+ " x = self.avgpool(x)\n",
+ " x = x.view(x.size(0), 256 * 6 * 6)\n",
+ " logits = self.classifier(x)\n",
+ " return logits"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model = AlexNet(num_classes=3)\n",
+ "\n",
+ "model = model.to(DEVICE)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "optimizer = torch.optim.SGD(model.parameters(), momentum=0.9, lr=0.01)\n",
+ "scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer,\n",
+ " factor=0.1,\n",
+ " mode='max',\n",
+ " verbose=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " 0%| | 0/562 [00:00<?, ?it/s]/home/user/anaconda3/lib/python3.8/site-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /pytorch/c10/core/TensorImpl.h:1156.)\n",
+ " return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)\n",
+ "100%|██████████| 562/562 [08:33<00:00, 1.09it/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 001/100 | Train: 52.89% | Validation: 52.56%\n",
+ "Validation acc Increased( 0 ---> 52.56410217285156 ) \t Saving The Model\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 0%| | 0/562 [00:00<?, ?it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Time elapsed: 13.22 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 562/562 [06:58<00:00, 1.34it/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 002/100 | Train: 64.48% | Validation: 58.51%\n",
+ "Validation acc Increased( 52.56410217285156 ---> 58.508155822753906 ) \t Saving The Model\n"
+ ]
+ },
+ {
+ "name": "stderr",
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+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Time elapsed: 11.72 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 562/562 [05:40<00:00, 1.65it/s]\n",
+ " 0%| | 0/562 [00:00<?, ?it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 003/100 | Train: 59.99% | Validation: 53.99%\n",
+ "Time elapsed: 10.43 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 562/562 [05:45<00:00, 1.62it/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 004/100 | Train: 67.96% | Validation: 69.46%\n",
+ "Validation acc Increased( 58.508155822753906 ---> 69.46387481689453 ) \t Saving The Model\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
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+ " 0%| | 0/562 [00:00<?, ?it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Time elapsed: 10.78 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 562/562 [05:51<00:00, 1.60it/s]\n",
+ " 0%| | 0/562 [00:00<?, ?it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 005/100 | Train: 67.00% | Validation: 66.84%\n",
+ "Time elapsed: 10.86 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 562/562 [05:57<00:00, 1.57it/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 006/100 | Train: 72.56% | Validation: 70.43%\n",
+ "Validation acc Increased( 69.46387481689453 ---> 70.42540740966797 ) \t Saving The Model\n"
+ ]
+ },
+ {
+ "name": "stderr",
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+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Time elapsed: 11.22 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
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+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 007/100 | Train: 75.87% | Validation: 77.33%\n",
+ "Validation acc Increased( 70.42540740966797 ---> 77.33100891113281 ) \t Saving The Model\n"
+ ]
+ },
+ {
+ "name": "stderr",
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+ ]
+ },
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+ "output_type": "stream",
+ "text": [
+ "Time elapsed: 11.90 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 562/562 [06:05<00:00, 1.54it/s]\n",
+ " 0%| | 0/562 [00:00<?, ?it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 008/100 | Train: 74.03% | Validation: 72.67%\n",
+ "Time elapsed: 11.27 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
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+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 009/100 | Train: 77.20% | Validation: 78.55%\n",
+ "Validation acc Increased( 77.33100891113281 ---> 78.55477905273438 ) \t Saving The Model\n"
+ ]
+ },
+ {
+ "name": "stderr",
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+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Time elapsed: 10.97 min\n"
+ ]
+ },
+ {
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+ "output_type": "stream",
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+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 010/100 | Train: 76.80% | Validation: 79.02%\n",
+ "Validation acc Increased( 78.55477905273438 ---> 79.02098083496094 ) \t Saving The Model\n"
+ ]
+ },
+ {
+ "name": "stderr",
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+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Time elapsed: 10.94 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 562/562 [05:37<00:00, 1.66it/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 011/100 | Train: 82.84% | Validation: 83.80%\n",
+ "Validation acc Increased( 79.02098083496094 ---> 83.7995376586914 ) \t Saving The Model\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
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+ ]
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+ "text": [
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+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 562/562 [06:14<00:00, 1.50it/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 012/100 | Train: 84.63% | Validation: 84.38%\n",
+ "Validation acc Increased( 83.7995376586914 ---> 84.38228607177734 ) \t Saving The Model\n"
+ ]
+ },
+ {
+ "name": "stderr",
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+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Time elapsed: 11.06 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 562/562 [06:10<00:00, 1.52it/s]\n",
+ " 0%| | 0/562 [00:00<?, ?it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 013/100 | Train: 82.84% | Validation: 82.93%\n",
+ "Time elapsed: 11.07 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 562/562 [05:30<00:00, 1.70it/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 014/100 | Train: 83.25% | Validation: 85.23%\n",
+ "Validation acc Increased( 84.38228607177734 ---> 85.2272720336914 ) \t Saving The Model\n"
+ ]
+ },
+ {
+ "name": "stderr",
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+ ]
+ },
+ {
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+ "output_type": "stream",
+ "text": [
+ "Time elapsed: 10.28 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 562/562 [05:55<00:00, 1.58it/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 015/100 | Train: 86.21% | Validation: 85.93%\n",
+ "Validation acc Increased( 85.2272720336914 ---> 85.92657470703125 ) \t Saving The Model\n"
+ ]
+ },
+ {
+ "name": "stderr",
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+ ]
+ },
+ {
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+ "output_type": "stream",
+ "text": [
+ "Time elapsed: 10.76 min\n"
+ ]
+ },
+ {
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+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 016/100 | Train: 86.74% | Validation: 87.94%\n",
+ "Validation acc Increased( 85.92657470703125 ---> 87.93706512451172 ) \t Saving The Model\n"
+ ]
+ },
+ {
+ "name": "stderr",
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+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Time elapsed: 11.21 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
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+ " 0%| | 0/562 [00:00<?, ?it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 017/100 | Train: 86.86% | Validation: 86.83%\n",
+ "Time elapsed: 10.71 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 562/562 [05:59<00:00, 1.56it/s]\n",
+ " 0%| | 0/562 [00:00<?, ?it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 018/100 | Train: 87.60% | Validation: 87.03%\n",
+ "Time elapsed: 10.97 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
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+ " 0%| | 0/562 [00:00<?, ?it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 019/100 | Train: 88.81% | Validation: 86.31%\n",
+ "Time elapsed: 11.46 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 562/562 [06:02<00:00, 1.55it/s]\n",
+ " 0%| | 0/562 [00:00<?, ?it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 020/100 | Train: 87.78% | Validation: 86.98%\n",
+ "Time elapsed: 11.49 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
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+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 021/100 | Train: 88.50% | Validation: 86.68%\n",
+ "Time elapsed: 10.87 min\n"
+ ]
+ },
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+ "name": "stderr",
+ "output_type": "stream",
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+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 022/100 | Train: 89.27% | Validation: 88.75%\n",
+ "Validation acc Increased( 87.93706512451172 ---> 88.75291442871094 ) \t Saving The Model\n"
+ ]
+ },
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+ "name": "stderr",
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+ "output_type": "stream",
+ "text": [
+ "Time elapsed: 10.80 min\n"
+ ]
+ },
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+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 023/100 | Train: 88.86% | Validation: 87.41%\n",
+ "Time elapsed: 11.36 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
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+ },
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+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 024/100 | Train: 86.40% | Validation: 86.16%\n",
+ "Time elapsed: 10.17 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
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+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 025/100 | Train: 90.08% | Validation: 90.21%\n",
+ "Validation acc Increased( 88.75291442871094 ---> 90.20979309082031 ) \t Saving The Model\n"
+ ]
+ },
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+ "name": "stderr",
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+ },
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+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Time elapsed: 10.50 min\n"
+ ]
+ },
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+ "name": "stderr",
+ "output_type": "stream",
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+ },
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+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 026/100 | Train: 90.12% | Validation: 87.59%\n",
+ "Time elapsed: 11.44 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
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+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 027/100 | Train: 90.77% | Validation: 88.96%\n",
+ "Time elapsed: 11.23 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
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+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 028/100 | Train: 89.91% | Validation: 89.10%\n",
+ "Time elapsed: 11.07 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
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+ ]
+ },
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+ "output_type": "stream",
+ "text": [
+ "Epoch: 029/100 | Train: 89.95% | Validation: 89.69%\n",
+ "Time elapsed: 10.93 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
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+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 030/100 | Train: 90.49% | Validation: 88.87%\n",
+ "Time elapsed: 11.41 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
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+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 031/100 | Train: 90.66% | Validation: 90.41%\n",
+ "Validation acc Increased( 90.20979309082031 ---> 90.41375732421875 ) \t Saving The Model\n"
+ ]
+ },
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+ "name": "stderr",
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+ ]
+ },
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+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Time elapsed: 10.97 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
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+ "output_type": "stream",
+ "text": [
+ "Epoch: 032/100 | Train: 91.31% | Validation: 90.18%\n",
+ "Time elapsed: 10.80 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
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+ "output_type": "stream",
+ "text": [
+ "Epoch: 033/100 | Train: 92.04% | Validation: 90.01%\n",
+ "Time elapsed: 10.45 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
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+ },
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+ "output_type": "stream",
+ "text": [
+ "Epoch: 034/100 | Train: 91.87% | Validation: 90.18%\n",
+ "Time elapsed: 10.53 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
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+ "100%|██████████| 562/562 [05:32<00:00, 1.69it/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 035/100 | Train: 91.53% | Validation: 90.82%\n",
+ "Validation acc Increased( 90.41375732421875 ---> 90.8216781616211 ) \t Saving The Model\n"
+ ]
+ },
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+ "name": "stderr",
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+ "output_type": "stream",
+ "text": [
+ "Time elapsed: 10.62 min\n"
+ ]
+ },
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+ "name": "stderr",
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+ "output_type": "stream",
+ "text": [
+ "Epoch: 036/100 | Train: 90.43% | Validation: 87.00%\n",
+ "Time elapsed: 11.04 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
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+ },
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+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 037/100 | Train: 91.29% | Validation: 88.90%\n",
+ "Time elapsed: 10.18 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
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+ "100%|██████████| 562/562 [06:01<00:00, 1.56it/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 038/100 | Train: 92.06% | Validation: 91.72%\n",
+ "Validation acc Increased( 90.8216781616211 ---> 91.72494506835938 ) \t Saving The Model\n"
+ ]
+ },
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+ "name": "stderr",
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+ "output_type": "stream",
+ "text": [
+ "Time elapsed: 11.29 min\n"
+ ]
+ },
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+ "name": "stderr",
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+ },
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+ "output_type": "stream",
+ "text": [
+ "Epoch: 039/100 | Train: 92.38% | Validation: 91.11%\n",
+ "Time elapsed: 10.93 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
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+ "output_type": "stream",
+ "text": [
+ "Epoch: 040/100 | Train: 91.98% | Validation: 90.15%\n",
+ "Time elapsed: 10.86 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
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+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 041/100 | Train: 91.96% | Validation: 89.51%\n",
+ "Time elapsed: 10.44 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
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+ },
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+ "output_type": "stream",
+ "text": [
+ "Epoch: 042/100 | Train: 93.29% | Validation: 90.65%\n",
+ "Time elapsed: 10.76 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
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+ },
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+ "output_type": "stream",
+ "text": [
+ "Epoch: 043/100 | Train: 93.55% | Validation: 90.88%\n",
+ "Time elapsed: 10.94 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
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+ },
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+ "output_type": "stream",
+ "text": [
+ "Epoch: 044/100 | Train: 92.82% | Validation: 91.14%\n",
+ "Time elapsed: 11.67 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
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+ },
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+ "output_type": "stream",
+ "text": [
+ "Epoch: 045/100 | Train: 93.66% | Validation: 91.43%\n",
+ "Time elapsed: 10.35 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
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+ ]
+ },
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+ "output_type": "stream",
+ "text": [
+ "Epoch: 046/100 | Train: 93.46% | Validation: 91.03%\n",
+ "Time elapsed: 10.68 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
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+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 047/100 | Train: 91.95% | Validation: 90.82%\n",
+ "Time elapsed: 10.30 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
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+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 048/100 | Train: 93.08% | Validation: 91.03%\n",
+ "Time elapsed: 10.60 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
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+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 049/100 | Train: 94.14% | Validation: 92.34%\n",
+ "Validation acc Increased( 91.72494506835938 ---> 92.33683013916016 ) \t Saving The Model\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
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+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Time elapsed: 11.04 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
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+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 050/100 | Train: 93.26% | Validation: 92.19%\n",
+ "Time elapsed: 10.74 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
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+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 051/100 | Train: 94.21% | Validation: 92.25%\n",
+ "Time elapsed: 11.13 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
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+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 052/100 | Train: 93.08% | Validation: 90.71%\n",
+ "Time elapsed: 10.91 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
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+ "100%|██████████| 562/562 [06:00<00:00, 1.56it/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 053/100 | Train: 92.92% | Validation: 92.40%\n",
+ "Validation acc Increased( 92.33683013916016 ---> 92.39511108398438 ) \t Saving The Model\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
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+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Time elapsed: 11.21 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
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+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 054/100 | Train: 94.38% | Validation: 92.60%\n",
+ "Validation acc Increased( 92.39511108398438 ---> 92.59906768798828 ) \t Saving The Model\n"
+ ]
+ },
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+ "name": "stderr",
+ "output_type": "stream",
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+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Time elapsed: 11.16 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
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+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 055/100 | Train: 93.57% | Validation: 90.15%\n",
+ "Time elapsed: 10.86 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
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+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 056/100 | Train: 94.23% | Validation: 91.93%\n",
+ "Time elapsed: 10.83 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
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+ "100%|██████████| 562/562 [05:36<00:00, 1.67it/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 057/100 | Train: 94.58% | Validation: 92.95%\n",
+ "Validation acc Increased( 92.59906768798828 ---> 92.94872283935547 ) \t Saving The Model\n"
+ ]
+ },
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+ "name": "stderr",
+ "output_type": "stream",
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+ ]
+ },
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+ "output_type": "stream",
+ "text": [
+ "Time elapsed: 10.41 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
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+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 058/100 | Train: 94.03% | Validation: 92.74%\n",
+ "Time elapsed: 10.40 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
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+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 059/100 | Train: 94.10% | Validation: 92.19%\n",
+ "Time elapsed: 10.86 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
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+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 060/100 | Train: 94.48% | Validation: 92.40%\n",
+ "Time elapsed: 10.60 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
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+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 061/100 | Train: 94.88% | Validation: 93.01%\n",
+ "Validation acc Increased( 92.94872283935547 ---> 93.00698852539062 ) \t Saving The Model\n"
+ ]
+ },
+ {
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+ "output_type": "stream",
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+ },
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+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Time elapsed: 11.21 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
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+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 062/100 | Train: 93.69% | Validation: 91.67%\n",
+ "Time elapsed: 11.04 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
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+ ]
+ },
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+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 063/100 | Train: 95.10% | Validation: 92.77%\n",
+ "Time elapsed: 11.34 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
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+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 064/100 | Train: 94.44% | Validation: 92.02%\n",
+ "Time elapsed: 11.32 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
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+ " 0%| | 0/562 [00:00<?, ?it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 065/100 | Train: 95.47% | Validation: 92.02%\n",
+ "Time elapsed: 11.30 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
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+ "100%|██████████| 562/562 [05:50<00:00, 1.61it/s]\n",
+ " 0%| | 0/562 [00:00<?, ?it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 066/100 | Train: 94.69% | Validation: 92.37%\n",
+ "Time elapsed: 10.92 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
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+ "100%|██████████| 562/562 [06:02<00:00, 1.55it/s]\n",
+ " 0%| | 0/562 [00:00<?, ?it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 067/100 | Train: 94.42% | Validation: 91.40%\n",
+ "Time elapsed: 11.09 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 562/562 [05:43<00:00, 1.64it/s]\n",
+ " 0%| | 0/562 [00:00<?, ?it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 068/100 | Train: 95.12% | Validation: 92.19%\n",
+ "Time elapsed: 10.97 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 562/562 [05:55<00:00, 1.58it/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 069/100 | Train: 95.87% | Validation: 93.04%\n",
+ "Validation acc Increased( 93.00698852539062 ---> 93.0361328125 ) \t Saving The Model\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
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+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Time elapsed: 11.08 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 562/562 [05:59<00:00, 1.56it/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 070/100 | Train: 95.15% | Validation: 93.85%\n",
+ "Validation acc Increased( 93.0361328125 ---> 93.85198211669922 ) \t Saving The Model\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
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+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Time elapsed: 11.11 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
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+ "100%|██████████| 562/562 [05:43<00:00, 1.63it/s]\n",
+ " 0%| | 0/562 [00:00<?, ?it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 071/100 | Train: 94.97% | Validation: 92.72%\n",
+ "Time elapsed: 10.56 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
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+ "100%|██████████| 562/562 [05:55<00:00, 1.58it/s]\n",
+ " 0%| | 0/562 [00:00<?, ?it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 072/100 | Train: 95.46% | Validation: 92.02%\n",
+ "Time elapsed: 10.95 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
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+ "100%|██████████| 562/562 [06:01<00:00, 1.56it/s]\n",
+ " 0%| | 0/562 [00:00<?, ?it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 073/100 | Train: 94.70% | Validation: 91.55%\n",
+ "Time elapsed: 11.04 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
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+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 074/100 | Train: 94.65% | Validation: 92.63%\n",
+ "Time elapsed: 11.07 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
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+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 075/100 | Train: 95.36% | Validation: 92.83%\n",
+ "Time elapsed: 11.42 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
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+ " 0%| | 0/562 [00:00<?, ?it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 076/100 | Train: 95.57% | Validation: 93.44%\n",
+ "Time elapsed: 11.03 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 562/562 [06:08<00:00, 1.53it/s]\n",
+ " 0%| | 0/562 [00:00<?, ?it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 077/100 | Train: 95.55% | Validation: 93.04%\n",
+ "Time elapsed: 11.67 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 562/562 [05:47<00:00, 1.62it/s]\n",
+ " 0%| | 0/562 [00:00<?, ?it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 078/100 | Train: 95.67% | Validation: 93.68%\n",
+ "Time elapsed: 11.26 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 562/562 [06:02<00:00, 1.55it/s]\n",
+ " 0%| | 0/562 [00:00<?, ?it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 079/100 | Train: 95.76% | Validation: 92.95%\n",
+ "Time elapsed: 11.37 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 562/562 [06:06<00:00, 1.53it/s]\n",
+ " 0%| | 0/562 [00:00<?, ?it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 080/100 | Train: 96.27% | Validation: 92.77%\n",
+ "Time elapsed: 11.37 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 562/562 [06:08<00:00, 1.52it/s]\n",
+ " 0%| | 0/562 [00:00<?, ?it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 081/100 | Train: 96.20% | Validation: 92.66%\n",
+ "Time elapsed: 11.56 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 562/562 [06:04<00:00, 1.54it/s]\n",
+ " 0%| | 0/562 [00:00<?, ?it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 082/100 | Train: 95.89% | Validation: 93.07%\n",
+ "Time elapsed: 11.14 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
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+ " 0%| | 0/562 [00:00<?, ?it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 083/100 | Train: 96.10% | Validation: 92.95%\n",
+ "Time elapsed: 11.54 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
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+ " 0%| | 0/562 [00:00<?, ?it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 084/100 | Train: 95.82% | Validation: 92.54%\n",
+ "Time elapsed: 11.13 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 562/562 [06:09<00:00, 1.52it/s]\n",
+ " 0%| | 0/562 [00:00<?, ?it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 085/100 | Train: 96.21% | Validation: 93.56%\n",
+ "Time elapsed: 11.21 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 562/562 [06:03<00:00, 1.54it/s]\n",
+ " 0%| | 0/562 [00:00<?, ?it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 086/100 | Train: 95.93% | Validation: 92.83%\n",
+ "Time elapsed: 11.20 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 562/562 [05:59<00:00, 1.56it/s]\n",
+ " 0%| | 0/562 [00:00<?, ?it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 087/100 | Train: 95.66% | Validation: 93.44%\n",
+ "Time elapsed: 11.28 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 562/562 [06:22<00:00, 1.47it/s]\n",
+ " 0%| | 0/562 [00:00<?, ?it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 088/100 | Train: 95.96% | Validation: 92.95%\n",
+ "Time elapsed: 11.51 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 562/562 [05:59<00:00, 1.56it/s]\n",
+ " 0%| | 0/562 [00:00<?, ?it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 089/100 | Train: 95.85% | Validation: 92.57%\n",
+ "Time elapsed: 11.71 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 562/562 [06:02<00:00, 1.55it/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 090/100 | Train: 96.20% | Validation: 94.06%\n",
+ "Validation acc Increased( 93.85198211669922 ---> 94.05594635009766 ) \t Saving The Model\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 0%| | 0/562 [00:00<?, ?it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Time elapsed: 11.43 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 562/562 [06:16<00:00, 1.49it/s]\n",
+ " 0%| | 0/562 [00:00<?, ?it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 091/100 | Train: 96.31% | Validation: 93.39%\n",
+ "Time elapsed: 11.64 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 562/562 [06:29<00:00, 1.44it/s]\n",
+ " 0%| | 0/562 [00:00<?, ?it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 092/100 | Train: 95.92% | Validation: 92.72%\n",
+ "Time elapsed: 11.99 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 562/562 [06:28<00:00, 1.45it/s]\n",
+ " 0%| | 0/562 [00:00<?, ?it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 093/100 | Train: 96.39% | Validation: 93.30%\n",
+ "Time elapsed: 11.54 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 562/562 [06:06<00:00, 1.54it/s]\n",
+ " 0%| | 0/562 [00:00<?, ?it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 094/100 | Train: 95.77% | Validation: 91.43%\n",
+ "Time elapsed: 11.63 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 562/562 [06:24<00:00, 1.46it/s]\n",
+ " 0%| | 0/562 [00:00<?, ?it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 095/100 | Train: 96.72% | Validation: 92.83%\n",
+ "Time elapsed: 12.04 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 562/562 [06:19<00:00, 1.48it/s]\n",
+ " 0%| | 0/562 [00:00<?, ?it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 096/100 | Train: 96.89% | Validation: 93.97%\n",
+ "Time elapsed: 12.29 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 562/562 [06:19<00:00, 1.48it/s]\n",
+ " 0%| | 0/562 [00:00<?, ?it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 097/100 | Train: 96.40% | Validation: 93.07%\n",
+ "Time elapsed: 11.86 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 562/562 [06:19<00:00, 1.48it/s]\n",
+ " 0%| | 0/562 [00:00<?, ?it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 098/100 | Train: 96.44% | Validation: 93.33%\n",
+ "Time elapsed: 12.22 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 562/562 [06:04<00:00, 1.54it/s]\n",
+ " 0%| | 0/562 [00:00<?, ?it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 099/100 | Train: 96.48% | Validation: 92.63%\n",
+ "Time elapsed: 11.29 min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 562/562 [06:00<00:00, 1.56it/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 100/100 | Train: 96.48% | Validation: 93.21%\n",
+ "Time elapsed: 11.71 min\n",
+ "Total Training Time: 1111.15 min\n"
+ ]
+ }
+ ],
+ "source": [
+ "minibatch_loss_list, train_acc_list, valid_acc_list = train_model(\n",
+ " model=model,\n",
+ " num_epochs=NUM_EPOCHS,\n",
+ " train_loader=train_loader,\n",
+ " valid_loader=valid_loader,\n",
+ " optimizer=optimizer,\n",
+ " device=DEVICE,\n",
+ " scheduler=scheduler,\n",
+ " scheduler_on='valid_acc',\n",
+ " logging_interval=300)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_training_loss(minibatch_loss_list=minibatch_loss_list,\n",
+ " num_epochs=NUM_EPOCHS,\n",
+ " iter_per_epoch=len(train_loader),\n",
+ " results_dir=None,\n",
+ " averaging_iterations=200)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_accuracy(train_acc_list=train_acc_list,\n",
+ " valid_acc_list=valid_acc_list,\n",
+ " results_dir=None)\n",
+ "plt.annotate(\"* Heighest valid accuracy\",(90,max(valid_acc_list)))\n",
+ "#plt.ylim([60, 100])\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "f1 = open(\"/home/user/research/AlexNet/minibatch_loss_list.txt\",\"w\")\n",
+ "f2 = open(\"/home/user/research/AlexNet/train_acc_list.txt\",\"w\")\n",
+ "f3 = open(\"/home/user/research/AlexNet/valid_acc_list.txt\",\"w\")\n",
+ "for i in range(len(train_acc_list)):\n",
+ " f2.write(str(train_acc_list[i]))\n",
+ " f2.write(\",\")\n",
+ " f3.write(str(valid_acc_list[i]))\n",
+ " f3.write(\",\")\n",
+ "for i in range(len(minibatch_loss_list)):\n",
+ " f1.write(str(minibatch_loss_list[i]))\n",
+ " f1.write(\",\")\n",
+ "f1.close()\n",
+ "f2.close()\n",
+ "f3.close()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "using GPU\n"
+ ]
+ }
+ ],
+ "source": [
+ "net = AlexNet(3)\n",
+ "if torch.cuda.is_available():\n",
+ " print(\"using GPU\")\n",
+ " net = net.cuda()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<All keys matched successfully>"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "net.load_state_dict(torch.load(\"/home/user/research/AlexNet/Alexnet_model.pth\"))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from tqdm import tqdm"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 108/108 [00:34<00:00, 3.15it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "93.79370880126953\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "right = 0\n",
+ "total = 0\n",
+ "for data, labels in tqdm(valid_loader):\n",
+ " if torch.cuda.is_available():\n",
+ " data, labels = data.cuda(), labels.cuda()\n",
+ " outputs = net(data)\n",
+ " _, predicted = torch.max(outputs, 1)\n",
+ " correct = (predicted == labels).float().sum()\n",
+ " right+=correct.float()\n",
+ " total = total+len(predicted)\n",
+ " #print(correct*100/len(predicted))\n",
+ " #pred = predicted.tolist()\n",
+ " #correct = labels.tolist()\n",
+ "# for i in range(len(pred)):\n",
+ "# if(pred[i]==correct[i]):\n",
+ "# right+=1\n",
+ "# else:\n",
+ "# wrong+=1\n",
+ "print(float(right*100/total))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from helper_evaluation import compute_accuracy"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "valid_acc = compute_accuracy(model, valid_loader, device=DEVICE)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "93.2109603881836"
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "valid_acc.item()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 15 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "net.cpu()\n",
+ "unnormalizer = UnNormalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
+ "class_dict = {0: 'covid',\n",
+ " 1: 'normal',\n",
+ " 2: 'pneumonia',\n",
+ " }\n",
+ "\n",
+ "show_examples(model=net, data_loader=valid_loader, unnormalizer=unnormalizer, class_dict=class_dict)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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wzXMf88jYL7lg347cd2I3BDw58Ws+mblhHXu8etD1DDjx2NzfTdu23HTrHdSqXZtfn3oyvbp3oXr16twy+E4klb+yKpaFQAM4DrhVUi1yXccTfsK6ngFOlfQuMIlct3O91KVrV157a1TaZaTu9AfGr9Z22G2lH9T+fukK/jz0g8ouKa917tKVl199a7X2wXfek0I1a2e9CrSImEbuAH7R9KBis1fbk4qIPmuajojhwPA1LLvvGrbfZi3KNbMqVpE71naQ9KKkicn0tpL+XPmlmZmtnYoMrL2N3EOGlwJExLvAEZVZlJnZuqhIoNWKiJI3dNxwRhqa2XqjIoE2S9Lm/Pig4UOAGZValZnZOqjISYEzgMFAJ0nTganAMZValZnZOqjIg4anAHtKqg0URMS35b3HzCwNFblj7YUlpgGIiEsrqSYzs3VSkS5n8WHSNYH9gQ175KGZ5aWKdDn/Vnxa0iB+vMDbzCxvVOQsZ0m1gHY/dyFmZj9VRY6hTSAZsgFUAxoBPn5mZnmnIsfQ9i/2ehnwdUR4YK2Z5Z0yA01SAfBkRGxT1nJmZvmgzGNoEbECGC+pVRXVY2a2zirS5WwGvCfpbYoN4YiIfpVWlZnZOqhIoF1S6VWYmf0MKhJo/xMRfyzeIOkqftptrs3MfnYVGYe2Vyltpd7R1cwsTWvcQ5N0GnA60C65v36RusBrlV2YmdnaKqvLeT/wNHAFcF6x9m8jYk6lVmVmtg7Kei7nfHKPiDuy6soxM1t363Itp5lZXnKgmVlmONDMLDMcaGaWGQ40M8sMB5qZZYYDzcwyw4FmZpnhQDOzzKjI3TZsLQQQEeUut6F67LReaZeQ9w6+/a20S1hveQ/NzDLDgWZmmeFAM7PMcKCZWWY40MwsMxxoZpYZDjQzywwHmpllhgPNzDLDgWZmmeFAM7PMcKCZWWY40MwsMxxoZpYZDjQzywwHmpllhgPNzDLDgWZmmeFAM7PMcKCZWWY40MwsMxxoZpYZDjQzywwHmpllhgPNzDLDgWZmmeFAM7PMcKCZWWY40MwsMxxoZpYZDjQzywwHWoYMPOVEWjdvQveunVe2/em8c+m6zZbsuH0XDj/kYObNm5degSk56/RT2Kpdc3bp0XVl29w5czj0wH3p2XUrDj1wX+bNnbvKe774/DPaNtuEm/9xbRVXm45DujbjzqO7csfRXfnz3u3ZqJrYvGEtbjysM4OP7MIth29LpyZ1VnlP4zrVefLUHhy23WYpVb06B1opJD0lqX7adayt/scez2PDnl6lbfc99mLUuAm8PWY87du3Z9BVV6RUXXqOOPpY/v3osFXabvj71ey86268Oe59dt51N274+9WrzL/w/HPYY6+9q7LM1DSsXZ2DujTj1H+/y0n/GkdBgdi9Q0MG9m7DPW99zoAHxnP3m58xYKfWq7zv9F3a8vanc9ew1nQ40EoREf8TEfPSrmNt9d55Fxps0mCVtj336kthYSEAO/ToyfTp09MoLVW9dtqZ+ptsskrbM08+weFH9Qfg8KP68/SwoSvnPTXscVq3aUfHTltVaZ1pqlYgahQWUCCoUVjA7IVLiAhqVa8GQO0ahcxeuGTl8ju1a8CM+T8wbc73aZVcqkoLNEltJH0oaYikdyU9LKmWpGmSLpE0RtIESZ2S5WtLulPSO5LGSjowaT9e0o3F1jtMUp/k9XeSrpI0WtILknaUNFzSFEn9kmVqSror2dZYSbsVW++jkp6RNFnS1cW2MU1Sw+T1Y8n635M0oLK+r6pwz9130XfvfdIuIy/MnPkNTZo2A6BJ02bMmjUTgIULF3Lj3wdxznl/TrO8KjVr4RIeGvMl/z6hGw+fvAMLFy9n1GfzuWnkNAb2bsO/T+jGqb1bc/vrnwFQs7CAI7o1Z8jbn6dc+eoqew+tIzA4IrYFFgCnJ+2zImJ74BbgnKTtAuCliNgB2A24RlLtctZfGxgeEd2Ab4G/AnsBBwGXJsucARARnYEjgSGSaibzugKHA52BwyW1LGUbJybr7w6cKWnTkgtIGiBplKRRRf8w8s1VV1xGYWEhRxx1dNql5LVrLr+UgWecSe06dcpfOCPq1KjGTu0acNSQ0Rx6xyhqblTAnh0b0q9zU24eOZUj7hrNTa9M45w9Ngfg+J4teXjcl/ywdEXKla+usJLX/3lEvJa8vg84M3n9aPJ7NHBw8rov0E9SUcDVBFqVs/4lwDPJ6wnA4ohYKmkC0CZp7w3cABARH0r6FOiQzHsxIuYDSHofaA2U/N/OmZIOSl63BNoDs4svEBGDgcEA23frHuXUXOXuu2cITz/1JE89+wKS0i4nLzRq1Jivv5pBk6bN+PqrGTRs2AiAMaPeZtjjj/KXC//E/PnzKFABNWrU5KSBp5ezxvVXt5b1mbHgB+Z/vwyAVz6Zw9bNfsEeHRty48ipAIyYPHtloHVqUpddttiUgTu1pk6NQlZEsGT5Ch5796vUPkORyg60kv+4i6YXJ7+XF6tBwK8iYlLxN0jqxqp7kjWLvV4aEUXrXFG03ohYIan4etdkcbHXxWsp2nYfYE+gV0QskjS8xPbz3nPPPsO1g67m2ReHU6tWrbTLyRt7/88BPHj/vZz5uz/w4P33ss9+BwAw9NmXVy5zzeWXUrtOnUyHGcDX3y5mq6Z1qVFYwOJlK9i+ZT0++vo7Zi9cQpfmv2D89AVs16Ie0+f9AMDZj0xc+d7jerTk+yXL8yLMoPIDrZWkXhHxBrnu3qvAdmtY9lngN5J+ExEhabuIGAtMA06XVAA0B3ZcyxpGAkcDL0nqQG6vbxKwfQXeWw+Ym4RZJ6DnWm67Sh13zFGMHDmc2bNmsUXblvz5wosZdPWVLF68mP337QvAjj16cMNNt6ZcadUaeMIxvP7qSObMnkXXTm05908X8pvfnsspxx/F/ffcTfOWLbl9yANpl5maD7/+jhEfz+afR2zL8oCPZ37HsPe+ZvLMhfx617ZUk1iyfAV/e/GTtEstl37cwfmZVyy1AZ4iFyi/BCYD/YH3ge4RMUtSd2BQRPSRtDFwXbKsgGkRsb9yfaT7yB3vmgg0AS6OiOGSvouIOsn2Lga+i4hByfR3EVEnOV52K9ANWAb8LiJelnR8Usevk+WHJbUMlzSN3DGzb4HHyAXpJKBR0bbX9Lm379Y9XnvznZ/47WXXtz8sS7uEvHfEXf77Kc/LZ+00OiK6l2yv7EAbFhHbVMoG8pQDrWwOtPI50Mq3pkDzODQzy4xKO4YWEdOADWrvzMzS5T00M8sMB5qZZYYDzcwyw4FmZpnhQDOzzHCgmVlmONDMLDMcaGaWGQ40M8sMB5qZZYYDzcwyw4FmZpnhQDOzzHCgmVlmONDMLDMcaGaWGQ40M8sMB5qZZYYDzcwyw4FmZpnhQDOzzHCgmVlmONDMLDMcaGaWGQ40M8sMB5qZZYYDzcwyw4FmZpnhQDOzzFBEpF1DpkiaCXyadh0lNARmpV1EHvP3U758+45aR0Sjko0OtA2ApFER0T3tOvKVv5/yrS/fkbucZpYZDjQzywwH2oZhcNoF5Dl/P+VbL74jH0Mzs8zwHpqZZYYDzcwyw4FmZpnhQDOzzChMuwD7+Uh6AljjWZ6I6FeF5eQlSQ3Kmh8Rc6qqlnwnaROgPVCzqC0iRqZXUfkcaNkyKPl9MNAUuC+ZPhKYlkZBeWg0udBXKfMCaFe15eQnSScDZwEtgHFAT+ANYPcUyyqXh21kkKSREbFLeW1mayJpArAD8GZEdJXUCbgkIg5PubQyeQ8tmxpJahcRUwAktQVWu5B3Q7c+dqmq0A8R8YMkJNWIiA8ldUy7qPI40LLpt8BwSVOS6TbAwPTKyT/ra5eqCn0hqT7wGPC8pLnAl6lWVAHucmaUpBpAp2Tyw4hYnGY9+WZ97VKlQdKuQD3gmYhYknY9ZfEeWoZI2j0iXpJ0cIlZm0siIh5NpbD8tF52qSqbpF9ExIISZ4MnJL/rAHl9FtiBli27Ai8BB5QyLwAH2o/Wyy5VFbgf2J/Szwbn/VlgdzkzSFK1iFiedh3ri/WpS2Vlc6BlkKTPgGeAB4GXwv+RS5Wc5WxJsZ5KRIxJr6L8Iqk50JpVv5+8PgvsQMsgSRuT63YeAWwPDAP+HRGvplpYHpH0F+B4YAqwImmOiPBZTkDSVcDhwPtA0d5+5PvVJg60jEv2Qq4Hjo6IamnXky8kTQI6u4tZuuT72XZ9Ozvui9MzStKukm4GxpAbOHpYyiXlm4lA/bSLyGNTgI3SLmJteQ8tgyRNJTdY9CFgaEQsTLei/COpO/A4uWBbuReS712qqiLpEaAL8CKrfj9nplZUBXjYRjZ1iYgFaReR54YAV5EbY7WinGU3REOTn/WK99AySFIL4AZgJ3Jjh14FzoqIL1ItLI9IGhERu6ZdRz6TVB3okExOioiladZTEQ60DJL0PLkBkvcmTceQOymwV3pV5RdJ15LrSg1l1S6Vh20AkvqQ24udRm5wbUvgOA/bsConaVxEdC2vbUMm6eVSmj1sIyFpNHBURExKpjsAD0REt3QrK5uPoWXTLEnHAA8k00cCs1OsJ69IqkbuZMnf064lj21UFGYAEfGRpLw/6+k9tAyS1Aq4EehF7hja68CZEfFZqoXlEUkvR8RuadeRryTdSe5vp+iwxdFAYUSckF5V5XOgZZCkIcDZETE3mW4ADIqIE9OtLH9Iuozc9ZsPAiuHtfgYWk5y+6kzgN7kjqGNBG7O94G2DrQMkjQ2IrYrr21D5mNo2eRjaNlUIGmTEnto/m9djLubZZO0P/AXfrw4XeQC/xepFlYO/5Fn09+A1yU9TO44yGHAZemWlF8k1QMuAooeHDMCuDQi5qdXVV65jtzTwyasT3drcZczoyRtRe7++AJejIj3Uy4prySX9kwkN9YKoD+5KyxK3u13g5R0yfeIiPXqKgoHmm2QPFavbJJ2INflHMGqA4+vTa2oCvDdNmxD9b2k3kUTknYCvk+xnnxzGbCI3J1a6hb7yWs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+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "mat = compute_confusion_matrix(model=net, data_loader=valid_loader, device=torch.device('cpu'))\n",
+ "plot_confusion_matrix(mat, class_names=class_dict.values())\n",
+ "plt.show()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.8"
+ },
+ "toc": {
+ "nav_menu": {},
+ "number_sections": true,
+ "sideBar": true,
+ "skip_h1_title": false,
+ "title_cell": "Table of Contents",
+ "title_sidebar": "Contents",
+ "toc_cell": false,
+ "toc_position": {},
+ "toc_section_display": true,
+ "toc_window_display": false
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/accuracy graph.png b/accuracy graph.png
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diff --git a/helper_dataset.py b/helper_dataset.py
new file mode 100644
index 0000000..3ff6bfa
--- /dev/null
+++ b/helper_dataset.py
@@ -0,0 +1,149 @@
+import torch
+from torch.utils.data import sampler
+from torchvision import datasets
+from torch.utils.data import DataLoader
+from torch.utils.data import SubsetRandomSampler
+from torchvision import transforms
+
+
+class UnNormalize(object):
+ def __init__(self, mean, std):
+ self.mean = mean
+ self.std = std
+
+ def __call__(self, tensor):
+ """
+ Parameters:
+ ------------
+ tensor (Tensor): Tensor image of size (C, H, W) to be normalized.
+
+ Returns:
+ ------------
+ Tensor: Normalized image.
+ """
+ for t, m, s in zip(tensor, self.mean, self.std):
+ t.mul_(s).add_(m)
+ return tensor
+
+
+def get_dataloaders_mnist(batch_size, num_workers=0,
+ validation_fraction=None,
+ train_transforms=None,
+ test_transforms=None):
+
+ if train_transforms is None:
+ train_transforms = transforms.ToTensor()
+
+ if test_transforms is None:
+ test_transforms = transforms.ToTensor()
+
+ train_dataset = datasets.MNIST(root='data',
+ train=True,
+ transform=train_transforms,
+ download=True)
+
+ valid_dataset = datasets.MNIST(root='data',
+ train=True,
+ transform=test_transforms)
+
+ test_dataset = datasets.MNIST(root='data',
+ train=False,
+ transform=test_transforms)
+
+ if validation_fraction is not None:
+ num = int(validation_fraction * 60000)
+ train_indices = torch.arange(0, 60000 - num)
+ valid_indices = torch.arange(60000 - num, 60000)
+
+ train_sampler = SubsetRandomSampler(train_indices)
+ valid_sampler = SubsetRandomSampler(valid_indices)
+
+ valid_loader = DataLoader(dataset=valid_dataset,
+ batch_size=batch_size,
+ num_workers=num_workers,
+ sampler=valid_sampler)
+
+ train_loader = DataLoader(dataset=train_dataset,
+ batch_size=batch_size,
+ num_workers=num_workers,
+ drop_last=True,
+ sampler=train_sampler)
+
+ else:
+ train_loader = DataLoader(dataset=train_dataset,
+ batch_size=batch_size,
+ num_workers=num_workers,
+ drop_last=True,
+ shuffle=True)
+
+ test_loader = DataLoader(dataset=test_dataset,
+ batch_size=batch_size,
+ num_workers=num_workers,
+ shuffle=False)
+
+ if validation_fraction is None:
+ return train_loader, test_loader
+ else:
+ return train_loader, valid_loader, test_loader
+
+
+def get_dataloaders_cifar10(batch_size, num_workers=0,
+ validation_fraction=None,
+ train_transforms=None,
+ test_transforms=None):
+
+ if train_transforms is None:
+ train_transforms = transforms.ToTensor()
+
+ if test_transforms is None:
+ test_transforms = transforms.ToTensor()
+
+ train_dataset = datasets.CIFAR10(root='data',
+ train=True,
+ transform=train_transforms,
+ download=True)
+
+ valid_dataset = datasets.CIFAR10(root='data',
+ train=True,
+ transform=test_transforms)
+
+ test_dataset = datasets.CIFAR10(root='data',
+ train=False,
+ transform=test_transforms)
+
+ if validation_fraction is not None:
+ num = int(validation_fraction * 50000)
+ train_indices = torch.arange(0, 50000 - num)
+ valid_indices = torch.arange(50000 - num, 50000)
+
+ train_sampler = SubsetRandomSampler(train_indices)
+ valid_sampler = SubsetRandomSampler(valid_indices)
+
+ valid_loader = DataLoader(dataset=valid_dataset,
+ batch_size=batch_size,
+ num_workers=num_workers,
+ sampler=valid_sampler)
+
+ train_loader = DataLoader(dataset=train_dataset,
+ batch_size=batch_size,
+ num_workers=num_workers,
+ drop_last=True,
+ sampler=train_sampler)
+
+ else:
+ train_loader = DataLoader(dataset=train_dataset,
+ batch_size=batch_size,
+ num_workers=num_workers,
+ drop_last=True,
+ shuffle=True)
+
+ test_loader = DataLoader(dataset=test_dataset,
+ batch_size=batch_size,
+ num_workers=num_workers,
+ shuffle=False)
+
+ if validation_fraction is None:
+ return train_loader, test_loader
+ else:
+ return train_loader, valid_loader, test_loader
+
diff --git a/helper_evaluation.py b/helper_evaluation.py
new file mode 100644
index 0000000..3162444
--- /dev/null
+++ b/helper_evaluation.py
@@ -0,0 +1,78 @@
+# imports from installed libraries
+import os
+import numpy as np
+import random
+import torch
+from distutils.version import LooseVersion as Version
+from itertools import product
+
+
+def set_all_seeds(seed):
+ os.environ["PL_GLOBAL_SEED"] = str(seed)
+ random.seed(seed)
+ np.random.seed(seed)
+ torch.manual_seed(seed)
+ torch.cuda.manual_seed_all(seed)
+
+
+def set_deterministic():
+ if torch.cuda.is_available():
+ torch.backends.cudnn.benchmark = False
+ torch.backends.cudnn.deterministic = True
+
+ if torch.__version__ <= Version("1.7"):
+ torch.set_deterministic(True)
+ else:
+ torch.use_deterministic_algorithms(True)
+
+
+def compute_accuracy(model, data_loader, device):
+
+ with torch.no_grad():
+
+ correct_pred, num_examples = 0, 0
+
+ for i, (features, targets) in enumerate(data_loader):
+
+ features = features.to(device)
+ targets = targets.float().to(device)
+
+ logits = model(features)
+ _, predicted_labels = torch.max(logits, 1)
+
+ num_examples += targets.size(0)
+ correct_pred += (predicted_labels == targets).sum()
+ return correct_pred.float()/num_examples * 100
+
+
+def compute_confusion_matrix(model, data_loader, device):
+
+ all_targets, all_predictions = [], []
+ with torch.no_grad():
+
+ for i, (features, targets) in enumerate(data_loader):
+
+ features = features.to(device)
+ targets = targets
+ logits = model(features)
+ _, predicted_labels = torch.max(logits, 1)
+ all_targets.extend(targets.to('cpu'))
+ all_predictions.extend(predicted_labels.to('cpu'))
+
+ all_predictions = all_predictions
+ all_predictions = np.array(all_predictions)
+ all_targets = np.array(all_targets)
+
+ class_labels = np.unique(np.concatenate((all_targets, all_predictions)))
+ if class_labels.shape[0] == 1:
+ if class_labels[0] != 0:
+ class_labels = np.array([0, class_labels[0]])
+ else:
+ class_labels = np.array([class_labels[0], 1])
+ n_labels = class_labels.shape[0]
+ lst = []
+ z = list(zip(all_targets, all_predictions))
+ for combi in product(class_labels, repeat=2):
+ lst.append(z.count(combi))
+ mat = np.asarray(lst)[:, None].reshape(n_labels, n_labels)
+ return mat
\ No newline at end of file
diff --git a/helper_plotting.py b/helper_plotting.py
new file mode 100644
index 0000000..5498f50
--- /dev/null
+++ b/helper_plotting.py
@@ -0,0 +1,190 @@
+# imports from installed libraries
+import os
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+
+
+def plot_training_loss(minibatch_loss_list, num_epochs, iter_per_epoch,
+ results_dir=None, averaging_iterations=100):
+
+ plt.figure()
+ ax1 = plt.subplot(1, 1, 1)
+ ax1.plot(range(len(minibatch_loss_list)),
+ (minibatch_loss_list), label='Minibatch Loss')
+
+ if len(minibatch_loss_list) > 1000:
+ ax1.set_ylim([
+ 0, np.max(minibatch_loss_list[1000:])*1.5
+ ])
+ ax1.set_xlabel('Iterations')
+ ax1.set_ylabel('Loss')
+
+ ax1.plot(np.convolve(minibatch_loss_list,
+ np.ones(averaging_iterations,)/averaging_iterations,
+ mode='valid'),
+ label='Running Average')
+ ax1.legend()
+
+ ###################
+ # Set scond x-axis
+ ax2 = ax1.twiny()
+ newlabel = list(range(num_epochs+1))
+
+ newpos = [e*iter_per_epoch for e in newlabel]
+
+ ax2.set_xticks(newpos[::10])
+ ax2.set_xticklabels(newlabel[::10])
+
+ ax2.xaxis.set_ticks_position('bottom')
+ ax2.xaxis.set_label_position('bottom')
+ ax2.spines['bottom'].set_position(('outward', 45))
+ ax2.set_xlabel('Epochs')
+ ax2.set_xlim(ax1.get_xlim())
+ ###################
+
+ plt.tight_layout()
+
+ if results_dir is not None:
+ image_path = os.path.join(results_dir, 'plot_training_loss.pdf')
+ plt.savefig(image_path)
+
+
+def plot_accuracy(train_acc_list, valid_acc_list, results_dir):
+
+ num_epochs = len(train_acc_list)
+
+ plt.plot(np.arange(1, num_epochs+1),
+ train_acc_list, label='Training')
+ plt.plot(np.arange(1, num_epochs+1),
+ valid_acc_list, label='Validation')
+
+ plt.xlabel('Epoch')
+ plt.ylabel('Accuracy')
+ plt.legend()
+
+ plt.tight_layout()
+
+ if results_dir is not None:
+ image_path = os.path.join(
+ results_dir, 'plot_acc_training_validation.pdf')
+ plt.savefig(image_path)
+
+
+def show_examples(model, data_loader, unnormalizer=None, class_dict=None):
+
+
+ for batch_idx, (features, targets) in enumerate(data_loader):
+
+ with torch.no_grad():
+ features = features
+ targets = targets
+ logits = model(features)
+ predictions = torch.argmax(logits, dim=1)
+ break
+
+ fig, axes = plt.subplots(nrows=3, ncols=5,
+ sharex=True, sharey=True)
+
+ if unnormalizer is not None:
+ for idx in range(features.shape[0]):
+ features[idx] = unnormalizer(features[idx])
+ nhwc_img = np.transpose(features, axes=(0, 2, 3, 1))
+
+ if nhwc_img.shape[-1] == 1:
+ nhw_img = np.squeeze(nhwc_img.numpy(), axis=3)
+
+ for idx, ax in enumerate(axes.ravel()):
+ ax.imshow(nhw_img[idx], cmap='binary')
+ if class_dict is not None:
+ ax.title.set_text(f'P: {class_dict[predictions[idx].item()]}'
+ f'\nT: {class_dict[targets[idx].item()]}')
+ else:
+ ax.title.set_text(f'P: {predictions[idx]} | T: {targets[idx]}')
+ ax.axison = False
+
+ else:
+
+ for idx, ax in enumerate(axes.ravel()):
+ ax.imshow(nhwc_img[idx])
+ if class_dict is not None:
+ ax.title.set_text(f'P: {class_dict[predictions[idx].item()]}'
+ f'\nT: {class_dict[targets[idx].item()]}')
+ else:
+ ax.title.set_text(f'P: {predictions[idx]} | T: {targets[idx]}')
+ ax.axison = False
+ plt.tight_layout()
+ plt.show()
+
+
+def plot_confusion_matrix(conf_mat,
+ hide_spines=False,
+ hide_ticks=False,
+ figsize=None,
+ cmap=None,
+ colorbar=False,
+ show_absolute=True,
+ show_normed=False,
+ class_names=None):
+
+ if not (show_absolute or show_normed):
+ raise AssertionError('Both show_absolute and show_normed are False')
+ if class_names is not None and len(class_names) != len(conf_mat):
+ raise AssertionError('len(class_names) should be equal to number of'
+ 'classes in the dataset')
+
+ total_samples = conf_mat.sum(axis=1)[:, np.newaxis]
+ normed_conf_mat = conf_mat.astype('float') / total_samples
+
+ fig, ax = plt.subplots(figsize=figsize)
+ ax.grid(False)
+ if cmap is None:
+ cmap = plt.cm.Blues
+
+ if figsize is None:
+ figsize = (len(conf_mat)*1.25, len(conf_mat)*1.25)
+
+ if show_normed:
+ matshow = ax.matshow(normed_conf_mat, cmap=cmap)
+ else:
+ matshow = ax.matshow(conf_mat, cmap=cmap)
+
+ if colorbar:
+ fig.colorbar(matshow)
+
+ for i in range(conf_mat.shape[0]):
+ for j in range(conf_mat.shape[1]):
+ cell_text = ""
+ if show_absolute:
+ cell_text += format(conf_mat[i, j], 'd')
+ if show_normed:
+ cell_text += "\n" + '('
+ cell_text += format(normed_conf_mat[i, j], '.2f') + ')'
+ else:
+ cell_text += format(normed_conf_mat[i, j], '.2f')
+ ax.text(x=j,
+ y=i,
+ s=cell_text,
+ va='center',
+ ha='center',
+ color="white" if normed_conf_mat[i, j] > 0.5 else "black")
+
+ if class_names is not None:
+ tick_marks = np.arange(len(class_names))
+ plt.xticks(tick_marks, class_names, rotation=90)
+ plt.yticks(tick_marks, class_names)
+
+ if hide_spines:
+ ax.spines['right'].set_visible(False)
+ ax.spines['top'].set_visible(False)
+ ax.spines['left'].set_visible(False)
+ ax.spines['bottom'].set_visible(False)
+ ax.yaxis.set_ticks_position('left')
+ ax.xaxis.set_ticks_position('bottom')
+ if hide_ticks:
+ ax.axes.get_yaxis().set_ticks([])
+ ax.axes.get_xaxis().set_ticks([])
+
+ plt.xlabel('predicted label')
+ plt.ylabel('true label')
+ return fig, ax
\ No newline at end of file
diff --git a/helper_train.py b/helper_train.py
new file mode 100644
index 0000000..ed551ef
--- /dev/null
+++ b/helper_train.py
@@ -0,0 +1,76 @@
+import time
+import torch
+import numpy as np
+from tqdm import tqdm
+from helper_evaluation import compute_accuracy
+
+
+def train_model(model, num_epochs, train_loader,
+ valid_loader, optimizer,
+ device, logging_interval=50,
+ scheduler=None,
+ scheduler_on='valid_acc'):
+
+ start_time1 = time.time()
+ min_valid_loss = 0
+ minibatch_loss_list, train_acc_list, valid_acc_list = [], [], []
+
+ for epoch in range(num_epochs):
+ start_time = time.time()
+ model.train()
+ for batch_idx, (features, targets) in enumerate(tqdm(train_loader)):
+
+ features = features.to(device)
+ targets = targets.to(device)
+
+ # ## FORWARD AND BACK PROP
+ logits = model(features)
+ loss = torch.nn.functional.cross_entropy(logits, targets)
+ optimizer.zero_grad()
+
+ loss.backward()
+
+ # ## UPDATE MODEL PARAMETERS
+ optimizer.step()
+
+ # ## LOGGING
+ minibatch_loss_list.append(loss.item())
+ '''if not batch_idx % logging_interval:
+ print(f'Epoch: {epoch+1:03d}/{num_epochs:03d} '
+ f'| Batch {batch_idx:04d}/{len(train_loader):04d} '
+ f'| Loss: {loss:.4f}')'''
+
+ model.eval()
+ with torch.no_grad(): # save memory during inference
+ train_acc = compute_accuracy(model, train_loader, device=device)
+ valid_acc = compute_accuracy(model, valid_loader, device=device)
+ print(f'Epoch: {epoch+1:03d}/{num_epochs:03d} '
+ f'| Train: {train_acc :.2f}% '
+ f'| Validation: {valid_acc :.2f}%')
+ train_acc_list.append(train_acc.item())
+ valid_acc_list.append(valid_acc.item())
+ if min_valid_loss < valid_acc.item():
+ print("Validation acc Increased(",min_valid_loss,"--->",valid_acc.item(),") \t Saving The Model")
+ min_valid_loss = valid_acc.item()
+ torch.save(model.state_dict(), '/home/user/research/AlexNet/Alexnet_model.pth')
+
+ elapsed = (time.time() - start_time)/60
+ print(f'Time elapsed: {elapsed:.2f} min')
+
+ '''if scheduler is not None:
+
+ if scheduler_on == 'valid_acc':
+ scheduler.step(valid_acc_list[-1])
+ elif scheduler_on == 'minibatch_loss':
+ scheduler.step(minibatch_loss_list[-1])
+ else:
+ raise ValueError(f'Invalid `scheduler_on` choice.')'''
+
+
+ elapsed = (time.time() - start_time1)/60
+ print(f'Total Training Time: {elapsed:.2f} min')
+
+ #test_acc = compute_accuracy(model, test_loader, device=device)
+ #print(f'Test accuracy {test_acc :.2f}%')
+
+ return minibatch_loss_list, train_acc_list, valid_acc_list
diff --git a/loss.png b/loss.png
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diff --git a/minibatch_loss_list.txt b/minibatch_loss_list.txt
new file mode 100644
index 0000000..8c84b71
--- /dev/null
+++ b/minibatch_loss_list.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/train_acc_list.txt b/train_acc_list.txt
new file mode 100644
index 0000000..893c8ea
--- /dev/null
+++ b/train_acc_list.txt
@@ -0,0 +1 @@
+52.89007568359375,64.47822570800781,59.98997497558594,67.95857238769531,66.99520874023438,72.55818939208984,75.86590576171875,74.03385925292969,77.20235443115234,76.79585266113281,82.84329986572266,84.62522888183594,82.83773040771484,83.24980163574219,86.21227264404297,86.74127960205078,86.8637924194336,87.59883880615234,88.80721282958984,87.78260040283203,88.49537658691406,89.27497100830078,88.85733032226562,86.39603424072266,90.07684326171875,90.12139129638672,90.76734161376953,89.90978240966797,89.94876861572266,90.4944839477539,90.65596771240234,91.31305694580078,92.04254150390625,91.86991119384766,91.53023529052734,90.42765808105469,91.29078674316406,92.05924987792969,92.37665557861328,91.98128509521484,91.9645767211914,93.2898941040039,93.54605102539062,92.8221435546875,93.6629867553711,93.45695495605469,91.9478759765625,93.07829284667969,94.14188385009766,93.25648498535156,94.21427154541016,93.0838623046875,92.91680145263672,94.37576293945312,93.56832885742188,94.2309799194336,94.58180236816406,94.03050994873047,94.10289764404297,94.4815673828125,94.8769302368164,93.68526458740234,95.09967803955078,94.44258880615234,95.47276306152344,94.69316864013672,94.4203109741211,95.11637878417969,95.86813354492188,95.14978790283203,94.96603393554688,95.46163177490234,94.69873809814453,94.65419006347656,95.36139678955078,95.56743621826172,95.55072784423828,95.66766357421875,95.75675964355469,96.2746353149414,96.2022476196289,95.89041137695312,96.10201263427734,95.8235855102539,96.21337890625,95.9349594116211,95.66210174560547,95.96279907226562,95.84586334228516,96.2022476196289,96.30804443359375,95.91825103759766,96.3860092163086,95.76789855957031,96.72012329101562,96.89274597167969,96.40271759033203,96.43612670898438,96.47510528564453,96.48067474365234,
\ No newline at end of file
diff --git a/valid_acc_list.txt b/valid_acc_list.txt
new file mode 100644
index 0000000..4e3a3b8
--- /dev/null
+++ b/valid_acc_list.txt
@@ -0,0 +1 @@
+52.56410217285156,58.508155822753906,53.991844177246094,69.46387481689453,66.84149169921875,70.42540740966797,77.33100891113281,72.66899871826172,78.55477905273438,79.02098083496094,83.7995376586914,84.38228607177734,82.92540740966797,85.2272720336914,85.92657470703125,87.93706512451172,86.82984161376953,87.03380584716797,86.30535888671875,86.97552490234375,86.68415069580078,88.75291442871094,87.41259002685547,86.15967559814453,90.20979309082031,87.58741760253906,88.95687866210938,89.10256958007812,89.68531799316406,88.86946105957031,90.41375732421875,90.18065643310547,90.00582885742188,90.18065643310547,90.8216781616211,87.0046615600586,88.89860534667969,91.72494506835938,91.11305236816406,90.15151977539062,89.51049041748047,90.64685821533203,90.87995147705078,91.14219665527344,91.43356323242188,91.02564239501953,90.8216781616211,91.02564239501953,92.33683013916016,92.1911392211914,92.24942016601562,90.70513153076172,92.39511108398438,92.59906768798828,90.15151977539062,91.92890167236328,92.94872283935547,92.74475860595703,92.1911392211914,92.39511108398438,93.00698852539062,91.66667175292969,92.77389526367188,92.01631927490234,92.01631927490234,92.365966796875,91.40443420410156,92.1911392211914,93.0361328125,93.85198211669922,92.71562194824219,92.01631927490234,91.55011749267578,92.62820434570312,92.83216857910156,93.44405364990234,93.0361328125,93.67715454101562,92.94872283935547,92.77389526367188,92.6573486328125,93.06526947021484,92.94872283935547,92.5407943725586,93.56060791015625,92.83216857910156,93.44405364990234,92.94872283935547,92.56993103027344,94.05594635009766,93.38578796386719,92.71562194824219,93.29837036132812,91.43356323242188,92.83216857910156,93.96853637695312,93.06526947021484,93.32750701904297,92.62820434570312,93.2109603881836,
--
GitLab