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Pabla007/trasfer learning.ipynb

Created December 28, 2019 18:14
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trasfer learning.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Transfer Learning\n",
"\n",
"In this notebook, you'll learn how to use pre-trained networks to solved challenging problems in computer vision. Specifically, you'll use networks trained on [ImageNet](http://www.image-net.org/) [available from torchvision](http://pytorch.org/docs/0.3.0/torchvision/models.html). \n",
"\n",
"ImageNet is a massive dataset with over 1 million labeled images in 1000 categories. It's used to train deep neural networks using an architecture called convolutional layers. I'm not going to get into the details of convolutional networks here, but if you want to learn more about them, please [watch this](https://www.youtube.com/watch?v=2-Ol7ZB0MmU).\n",
"\n",
"Once trained, these models work astonishingly well as feature detectors for images they weren't trained on. Using a pre-trained network on images not in the training set is called transfer learning. Here we'll use transfer learning to train a network that can classify our cat and dog photos with near perfect accuracy.\n",
"\n",
"With `torchvision.models` you can download these pre-trained networks and use them in your applications. We'll include `models` in our imports now."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"%config InlineBackend.figure_format = 'retina'\n",
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import torch\n",
"from torch import nn\n",
"from torch import optim\n",
"import torch.nn.functional as F\n",
"from torchvision import datasets, transforms, models"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Most of the pretrained models require the input to be 224x224 images. Also, we'll need to match the normalization used when the models were trained. Each color channel was normalized separately, the means are `[0.485, 0.456, 0.406]` and the standard deviations are `[0.229, 0.224, 0.225]`."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"data_dir = 'C:\\\\Users\\\\pabla\\\\Downloads\\\\deep-learning-v2-pytorch-master\\\\deep-learning-v2-pytorch-master\\\\intro-to-pytorch\\\\Cat_Dog_data\\\\Cat_Dog_data'\n",
"\n",
"# TODO: Define transforms for the training data and testing data\n",
"train_transforms = transforms.Compose([transforms.RandomRotation(30),\n",
" transforms.RandomResizedCrop(224),\n",
" transforms.RandomHorizontalFlip(),\n",
" transforms.ToTensor(),\n",
" transforms.Normalize([0.485,0.456,0.406],\n",
" [0.229,0.224,0.225])])\n",
"\n",
"test_transforms = transforms.Compose([transforms.Resize(255),\n",
" transforms.CenterCrop(224),\n",
" transforms.ToTensor(),\n",
" transforms.Normalize([0.485,0.456,0.406],\n",
" [0.229,0.224,0.225])])\n",
"\n",
"# Pass transforms in here, then run the next cell to see how the transforms look\n",
"train_data = datasets.ImageFolder(data_dir + '/train', transform=train_transforms)\n",
"test_data = datasets.ImageFolder(data_dir + '/test', transform=test_transforms)\n",
"\n",
"trainloader = torch.utils.data.DataLoader(train_data, batch_size=64, shuffle=True)\n",
"testloader = torch.utils.data.DataLoader(test_data, batch_size=64)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can load in a model such as [DenseNet](http://pytorch.org/docs/0.3.0/torchvision/models.html#id5). Let's print out the model architecture so we can see what's going on."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Downloading: \"https://download.pytorch.org/models/densenet121-a639ec97.pth\" to C:\\Users\\pabla/.cache\\torch\\checkpoints\\densenet121-a639ec97.pth\n",
"100%|█████████████████████████████████████████████████████████████████████████████| 30.8M/30.8M [00:06<00:00, 4.87MB/s]\n"
]
},
{
"data": {
"text/plain": [
"DenseNet(\n",
" (features): Sequential(\n",
" (conv0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
" (norm0): BatchNorm2d(64, 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(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer2): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer3): _DenseLayer(\n",
" (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer4): _DenseLayer(\n",
" (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer5): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer6): _DenseLayer(\n",
" (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" )\n",
" (transition1): _Transition(\n",
" (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" (conv): Conv2d(256, 128, 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(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer2): _DenseLayer(\n",
" (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, 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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer4): _DenseLayer(\n",
" (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer5): _DenseLayer(\n",
" (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer6): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer7): _DenseLayer(\n",
" (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer8): _DenseLayer(\n",
" (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer9): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer10): _DenseLayer(\n",
" (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer11): _DenseLayer(\n",
" (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer12): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" )\n",
" (transition2): _Transition(\n",
" (norm): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" (conv): Conv2d(512, 256, 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(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer2): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer3): _DenseLayer(\n",
" (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer4): _DenseLayer(\n",
" (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, 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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer6): _DenseLayer(\n",
" (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer7): _DenseLayer(\n",
" (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer8): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer9): _DenseLayer(\n",
" (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer10): _DenseLayer(\n",
" (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer11): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer12): _DenseLayer(\n",
" (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer13): _DenseLayer(\n",
" (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer14): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer15): _DenseLayer(\n",
" (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer16): _DenseLayer(\n",
" (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer17): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer18): _DenseLayer(\n",
" (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer19): _DenseLayer(\n",
" (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer20): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer21): _DenseLayer(\n",
" (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer22): _DenseLayer(\n",
" (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer23): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer24): _DenseLayer(\n",
" (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" )\n",
" (transition3): _Transition(\n",
" (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" (conv): Conv2d(1024, 512, 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(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer2): _DenseLayer(\n",
" (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer3): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer4): _DenseLayer(\n",
" (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer5): _DenseLayer(\n",
" (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer6): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer7): _DenseLayer(\n",
" (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer8): _DenseLayer(\n",
" (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, 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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer10): _DenseLayer(\n",
" (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer11): _DenseLayer(\n",
" (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer12): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer13): _DenseLayer(\n",
" (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer14): _DenseLayer(\n",
" (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer15): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer16): _DenseLayer(\n",
" (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" )\n",
" (norm5): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (classifier): Linear(in_features=1024, out_features=1000, bias=True)\n",
")"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = models.densenet121(pretrained=True)\n",
"model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This model is built out of two main parts, the features and the classifier. The features part is a stack of convolutional layers and overall works as a feature detector that can be fed into a classifier. The classifier part is a single fully-connected layer `(classifier): Linear(in_features=1024, out_features=1000)`. This layer was trained on the ImageNet dataset, so it won't work for our specific problem. That means we need to replace the classifier, but the features will work perfectly on their own. In general, I think about pre-trained networks as amazingly good feature detectors that can be used as the input for simple feed-forward classifiers."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# Freeze parameters so we don't backprop through them\n",
"for param in model.parameters():\n",
" param.requires_grad = False\n",
"\n",
"from collections import OrderedDict\n",
"classifier = nn.Sequential(OrderedDict([\n",
" ('fc1', nn.Linear(1024, 500)),\n",
" ('relu', nn.ReLU()),\n",
" ('fc2', nn.Linear(500, 2)),\n",
" ('output', nn.LogSoftmax(dim=1))\n",
" ]))\n",
" \n",
"model.classifier = classifier"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With our model built, we need to train the classifier. However, now we're using a **really deep** neural network. If you try to train this on a CPU like normal, it will take a long, long time. Instead, we're going to use the GPU to do the calculations. The linear algebra computations are done in parallel on the GPU leading to 100x increased training speeds. It's also possible to train on multiple GPUs, further decreasing training time.\n",
"\n",
"PyTorch, along with pretty much every other deep learning framework, uses [CUDA](https://developer.nvidia.com/cuda-zone) to efficiently compute the forward and backwards passes on the GPU. In PyTorch, you move your model parameters and other tensors to the GPU memory using `model.to('cuda')`. You can move them back from the GPU with `model.to('cpu')` which you'll commonly do when you need to operate on the network output outside of PyTorch. As a demonstration of the increased speed, I'll compare how long it takes to perform a forward and backward pass with and without a GPU."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n"
]
}
],
"source": [
"import time\n",
"cuda = torch.cuda.is_available()\n",
"print(cuda)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Device = cpu; Time per batch: 5.349 seconds\n",
"Device = cuda; Time per batch: 0.046 seconds\n"
]
}
],
"source": [
"for device in ['cpu', 'cuda']:\n",
"\n",
" criterion = nn.NLLLoss()\n",
" # Only train the classifier parameters, feature parameters are frozen\n",
" optimizer = optim.Adam(model.classifier.parameters(), lr=0.001)\n",
"\n",
" model.to(device)\n",
"\n",
" for ii, (inputs, labels) in enumerate(trainloader):\n",
"\n",
" # Move input and label tensors to the GPU\n",
" inputs, labels = inputs.to(device), labels.to(device)\n",
"\n",
" start = time.time()\n",
"\n",
" outputs = model.forward(inputs)\n",
" loss = criterion(outputs, labels)\n",
" loss.backward()\n",
" optimizer.step()\n",
"\n",
" if ii==3:\n",
" break\n",
" \n",
" print(f\"Device = {device}; Time per batch: {(time.time() - start)/3:.3f} seconds\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can write device agnostic code which will automatically use CUDA if it's enabled like so:\n",
"```python\n",
"# at beginning of the script\n",
"device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
"\n",
"...\n",
"\n",
"# then whenever you get a new Tensor or Module\n",
"# this won't copy if they are already on the desired device\n",
"input = data.to(device)\n",
"model = MyModule(...).to(device)\n",
"```\n",
"\n",
"From here, I'll let you finish training the model. The process is the same as before except now your model is much more powerful. You should get better than 95% accuracy easily.\n",
"\n",
">**Exercise:** Train a pretrained models to classify the cat and dog images. Continue with the DenseNet model, or try ResNet, it's also a good model to try out first. Make sure you are only training the classifier and the parameters for the features part are frozen."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"DenseNet(\n",
" (features): Sequential(\n",
" (conv0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
" (norm0): BatchNorm2d(64, 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(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer2): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer3): _DenseLayer(\n",
" (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer4): _DenseLayer(\n",
" (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer5): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer6): _DenseLayer(\n",
" (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" )\n",
" (transition1): _Transition(\n",
" (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" (conv): Conv2d(256, 128, 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(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer2): _DenseLayer(\n",
" (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, 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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer4): _DenseLayer(\n",
" (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer5): _DenseLayer(\n",
" (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer6): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer7): _DenseLayer(\n",
" (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer8): _DenseLayer(\n",
" (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer9): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer10): _DenseLayer(\n",
" (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer11): _DenseLayer(\n",
" (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer12): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" )\n",
" (transition2): _Transition(\n",
" (norm): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" (conv): Conv2d(512, 256, 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(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer2): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer3): _DenseLayer(\n",
" (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer4): _DenseLayer(\n",
" (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, 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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer6): _DenseLayer(\n",
" (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer7): _DenseLayer(\n",
" (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer8): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer9): _DenseLayer(\n",
" (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer10): _DenseLayer(\n",
" (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer11): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer12): _DenseLayer(\n",
" (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer13): _DenseLayer(\n",
" (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer14): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer15): _DenseLayer(\n",
" (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer16): _DenseLayer(\n",
" (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer17): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer18): _DenseLayer(\n",
" (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer19): _DenseLayer(\n",
" (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer20): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer21): _DenseLayer(\n",
" (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer22): _DenseLayer(\n",
" (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer23): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer24): _DenseLayer(\n",
" (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" )\n",
" (transition3): _Transition(\n",
" (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" (conv): Conv2d(1024, 512, 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(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer2): _DenseLayer(\n",
" (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer3): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer4): _DenseLayer(\n",
" (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer5): _DenseLayer(\n",
" (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer6): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer7): _DenseLayer(\n",
" (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer8): _DenseLayer(\n",
" (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, 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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer10): _DenseLayer(\n",
" (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer11): _DenseLayer(\n",
" (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer12): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer13): _DenseLayer(\n",
" (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer14): _DenseLayer(\n",
" (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer15): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer16): _DenseLayer(\n",
" (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" )\n",
" (norm5): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (classifier): Sequential(\n",
" (0): Linear(in_features=1024, out_features=256, bias=True)\n",
" (1): ReLU()\n",
" (2): Dropout(p=0.2, inplace=False)\n",
" (3): Linear(in_features=256, out_features=2, bias=True)\n",
" (4): LogSoftmax()\n",
" )\n",
")"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Use GPU if it's available\n",
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
"\n",
"model = models.densenet121(pretrained=True)\n",
"\n",
"# Freeze parameters so we don't backprop through them\n",
"for param in model.parameters():\n",
" param.requires_grad = False\n",
" \n",
"model.classifier = nn.Sequential(nn.Linear(1024, 256),\n",
" nn.ReLU(),\n",
" nn.Dropout(0.2),\n",
" nn.Linear(256, 2),\n",
" nn.LogSoftmax(dim=1))\n",
"\n",
"criterion = nn.NLLLoss()\n",
"\n",
"# Only train the classifier parameters, feature parameters are frozen\n",
"optimizer = optim.Adam(model.classifier.parameters(), lr=0.003)\n",
"\n",
"model.to(device)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/1.. Train loss: 0.767.. Test loss: 0.300.. Test accuracy: 0.907\n",
"Epoch 1/1.. Train loss: 0.356.. Test loss: 0.131.. Test accuracy: 0.972\n",
"Epoch 1/1.. Train loss: 0.246.. Test loss: 0.087.. Test accuracy: 0.975\n",
"Epoch 1/1.. Train loss: 0.199.. Test loss: 0.068.. Test accuracy: 0.977\n",
"Epoch 1/1.. Train loss: 0.173.. Test loss: 0.058.. Test accuracy: 0.980\n",
"Epoch 1/1.. Train loss: 0.179.. Test loss: 0.052.. Test accuracy: 0.981\n",
"Epoch 1/1.. Train loss: 0.156.. Test loss: 0.065.. Test accuracy: 0.978\n",
"Epoch 1/1.. Train loss: 0.187.. Test loss: 0.053.. Test accuracy: 0.980\n",
"Epoch 1/1.. Train loss: 0.139.. Test loss: 0.055.. Test accuracy: 0.977\n",
"Epoch 1/1.. Train loss: 0.201.. Test loss: 0.046.. Test accuracy: 0.982\n",
"Epoch 1/1.. Train loss: 0.180.. Test loss: 0.081.. Test accuracy: 0.971\n",
"Epoch 1/1.. Train loss: 0.157.. Test loss: 0.050.. Test accuracy: 0.980\n",
"Epoch 1/1.. Train loss: 0.221.. Test loss: 0.049.. Test accuracy: 0.982\n",
"Epoch 1/1.. Train loss: 0.187.. Test loss: 0.048.. Test accuracy: 0.983\n",
"Epoch 1/1.. Train loss: 0.194.. Test loss: 0.046.. Test accuracy: 0.983\n",
"Epoch 1/1.. Train loss: 0.221.. Test loss: 0.090.. Test accuracy: 0.965\n",
"Epoch 1/1.. Train loss: 0.244.. Test loss: 0.120.. Test accuracy: 0.950\n",
"Epoch 1/1.. Train loss: 0.288.. Test loss: 0.117.. Test accuracy: 0.950\n",
"Epoch 1/1.. Train loss: 0.338.. Test loss: 0.060.. Test accuracy: 0.983\n",
"Epoch 1/1.. Train loss: 0.239.. Test loss: 0.094.. Test accuracy: 0.970\n",
"Epoch 1/1.. Train loss: 0.182.. Test loss: 0.098.. Test accuracy: 0.960\n",
"Epoch 1/1.. Train loss: 0.283.. Test loss: 0.153.. Test accuracy: 0.936\n",
"Epoch 1/1.. Train loss: 0.161.. Test loss: 0.055.. Test accuracy: 0.979\n",
"Epoch 1/1.. Train loss: 0.179.. Test loss: 0.081.. Test accuracy: 0.970\n",
"Epoch 1/1.. Train loss: 0.151.. Test loss: 0.043.. Test accuracy: 0.984\n",
"Epoch 1/1.. Train loss: 0.145.. Test loss: 0.043.. Test accuracy: 0.983\n",
"Epoch 1/1.. Train loss: 0.203.. Test loss: 0.045.. Test accuracy: 0.983\n",
"Epoch 1/1.. Train loss: 0.244.. Test loss: 0.061.. Test accuracy: 0.979\n",
"Epoch 1/1.. Train loss: 0.190.. Test loss: 0.048.. Test accuracy: 0.983\n",
"Epoch 1/1.. Train loss: 0.132.. Test loss: 0.056.. Test accuracy: 0.982\n",
"Epoch 1/1.. Train loss: 0.213.. Test loss: 0.067.. Test accuracy: 0.977\n",
"Epoch 1/1.. Train loss: 0.164.. Test loss: 0.043.. Test accuracy: 0.984\n",
"Epoch 1/1.. Train loss: 0.238.. Test loss: 0.051.. Test accuracy: 0.981\n",
"Epoch 1/1.. Train loss: 0.148.. Test loss: 0.067.. Test accuracy: 0.976\n",
"Epoch 1/1.. Train loss: 0.196.. Test loss: 0.050.. Test accuracy: 0.984\n",
"Epoch 1/1.. Train loss: 0.164.. Test loss: 0.049.. Test accuracy: 0.982\n",
"Epoch 1/1.. Train loss: 0.142.. Test loss: 0.044.. Test accuracy: 0.983\n",
"Epoch 1/1.. Train loss: 0.113.. Test loss: 0.044.. Test accuracy: 0.984\n",
"Epoch 1/1.. Train loss: 0.165.. Test loss: 0.040.. Test accuracy: 0.983\n",
"Epoch 1/1.. Train loss: 0.133.. Test loss: 0.045.. Test accuracy: 0.982\n",
"Epoch 1/1.. Train loss: 0.182.. Test loss: 0.041.. Test accuracy: 0.983\n",
"Epoch 1/1.. Train loss: 0.224.. Test loss: 0.042.. Test accuracy: 0.982\n",
"Epoch 1/1.. Train loss: 0.207.. Test loss: 0.045.. Test accuracy: 0.983\n",
"Epoch 1/1.. Train loss: 0.212.. Test loss: 0.058.. Test accuracy: 0.980\n",
"Epoch 1/1.. Train loss: 0.192.. Test loss: 0.063.. Test accuracy: 0.978\n",
"Epoch 1/1.. Train loss: 0.126.. Test loss: 0.055.. Test accuracy: 0.979\n",
"Epoch 1/1.. Train loss: 0.076.. Test loss: 0.049.. Test accuracy: 0.983\n",
"Epoch 1/1.. Train loss: 0.213.. Test loss: 0.065.. Test accuracy: 0.978\n",
"Epoch 1/1.. Train loss: 0.186.. Test loss: 0.041.. Test accuracy: 0.985\n",
"Epoch 1/1.. Train loss: 0.139.. Test loss: 0.054.. Test accuracy: 0.982\n",
"Epoch 1/1.. Train loss: 0.173.. Test loss: 0.055.. Test accuracy: 0.979\n",
"Epoch 1/1.. Train loss: 0.150.. Test loss: 0.044.. Test accuracy: 0.982\n",
"Epoch 1/1.. Train loss: 0.164.. Test loss: 0.041.. Test accuracy: 0.983\n",
"Epoch 1/1.. Train loss: 0.107.. Test loss: 0.047.. Test accuracy: 0.982\n",
"Epoch 1/1.. Train loss: 0.158.. Test loss: 0.045.. Test accuracy: 0.983\n",
"Epoch 1/1.. Train loss: 0.119.. Test loss: 0.058.. Test accuracy: 0.979\n",
"Epoch 1/1.. Train loss: 0.113.. Test loss: 0.039.. Test accuracy: 0.984\n",
"Epoch 1/1.. Train loss: 0.182.. Test loss: 0.047.. Test accuracy: 0.982\n",
"Epoch 1/1.. Train loss: 0.153.. Test loss: 0.040.. Test accuracy: 0.984\n",
"Epoch 1/1.. Train loss: 0.207.. Test loss: 0.039.. Test accuracy: 0.984\n",
"Epoch 1/1.. Train loss: 0.158.. Test loss: 0.042.. Test accuracy: 0.982\n",
"Epoch 1/1.. Train loss: 0.162.. Test loss: 0.040.. Test accuracy: 0.983\n",
"Epoch 1/1.. Train loss: 0.145.. Test loss: 0.043.. Test accuracy: 0.983\n",
"Epoch 1/1.. Train loss: 0.173.. Test loss: 0.049.. Test accuracy: 0.980\n",
"Epoch 1/1.. Train loss: 0.158.. Test loss: 0.043.. Test accuracy: 0.983\n",
"Epoch 1/1.. Train loss: 0.156.. Test loss: 0.041.. Test accuracy: 0.983\n",
"Epoch 1/1.. Train loss: 0.158.. Test loss: 0.095.. Test accuracy: 0.963\n",
"Epoch 1/1.. Train loss: 0.178.. Test loss: 0.041.. Test accuracy: 0.984\n",
"Epoch 1/1.. Train loss: 0.165.. Test loss: 0.041.. Test accuracy: 0.984\n"
]
}
],
"source": [
"epochs = 1\n",
"steps = 0\n",
"running_loss = 0\n",
"print_every = 5\n",
"for epoch in range(epochs):\n",
" for inputs, labels in trainloader:\n",
" steps += 1\n",
" # Move input and label tensors to the default device\n",
" inputs, labels = inputs.to(device), labels.to(device)\n",
" \n",
" optimizer.zero_grad()\n",
" \n",
" logps = model.forward(inputs)\n",
" loss = criterion(logps, labels)\n",
" loss.backward()\n",
" optimizer.step()\n",
"\n",
" running_loss += loss.item()\n",
" \n",
" if steps % print_every == 0:\n",
" test_loss = 0\n",
" accuracy = 0\n",
" model.eval()\n",
" with torch.no_grad():\n",
" for inputs, labels in testloader:\n",
" inputs, labels = inputs.to(device), labels.to(device)\n",
" logps = model.forward(inputs)\n",
" batch_loss = criterion(logps, labels)\n",
" \n",
" test_loss += batch_loss.item()\n",
" \n",
" # Calculate accuracy\n",
" ps = torch.exp(logps)\n",
" top_p, top_class = ps.topk(1, dim=1)\n",
" equals = top_class == labels.view(*top_class.shape)\n",
" accuracy += torch.mean(equals.type(torch.FloatTensor)).item()\n",
" \n",
" print(f\"Epoch {epoch+1}/{epochs}.. \"\n",
" f\"Train loss: {running_loss/print_every:.3f}.. \"\n",
" f\"Test loss: {test_loss/len(testloader):.3f}.. \"\n",
" f\"Test accuracy: {accuracy/len(testloader):.3f}\")\n",
" running_loss = 0\n",
" model.train()"
]
}
],
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