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Anthony Wang 78a654084f Initial commit
2021-08-16 21:13:19 -05:00

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{
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"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"`Learn the Basics <intro.html>`_ ||\n",
"**Quickstart** ||\n",
"`Tensors <tensorqs_tutorial.html>`_ ||\n",
"`Datasets & DataLoaders <data_tutorial.html>`_ ||\n",
"`Transforms <transforms_tutorial.html>`_ ||\n",
"`Build Model <buildmodel_tutorial.html>`_ ||\n",
"`Autograd <autogradqs_tutorial.html>`_ ||\n",
"`Optimization <optimization_tutorial.html>`_ ||\n",
"`Save & Load Model <saveloadrun_tutorial.html>`_\n",
"\n",
"Quickstart\n",
"===================\n",
"This section runs through the API for common tasks in machine learning. Refer to the links in each section to dive deeper.\n",
"\n",
"Working with data\n",
"-----------------\n",
"PyTorch has two `primitives to work with data <https://pytorch.org/docs/stable/data.html>`_:\n",
"``torch.utils.data.DataLoader`` and ``torch.utils.data.Dataset``.\n",
"``Dataset`` stores the samples and their corresponding labels, and ``DataLoader`` wraps an iterable around\n",
"the ``Dataset``.\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"jupyter": {
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},
"outputs": [],
"source": [
"import torch\n",
"from torch import nn\n",
"from torch.utils.data import DataLoader\n",
"from torchvision import datasets\n",
"from torchvision.transforms import ToTensor, Lambda, Compose\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"PyTorch offers domain-specific libraries such as `TorchText <https://pytorch.org/text/stable/index.html>`_,\n",
"`TorchVision <https://pytorch.org/vision/stable/index.html>`_, and `TorchAudio <https://pytorch.org/audio/stable/index.html>`_,\n",
"all of which include datasets. For this tutorial, we will be using a TorchVision dataset.\n",
"\n",
"The ``torchvision.datasets`` module contains ``Dataset`` objects for many real-world vision data like\n",
"CIFAR, COCO (`full list here <https://pytorch.org/vision/stable/datasets.html>`_). In this tutorial, we\n",
"use the FashionMNIST dataset. Every TorchVision ``Dataset`` includes two arguments: ``transform`` and\n",
"``target_transform`` to modify the samples and labels respectively.\n",
"\n"
]
},
{
"cell_type": "code",
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{
"name": "stdout",
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"text": [
"Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz\n",
"Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to data/FashionMNIST/raw/train-images-idx3-ubyte.gz\n"
]
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"text": [
"Extracting data/FashionMNIST/raw/train-images-idx3-ubyte.gz to data/FashionMNIST/raw\n",
"\n",
"Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz\n",
"Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to data/FashionMNIST/raw/train-labels-idx1-ubyte.gz\n"
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"text": [
"Extracting data/FashionMNIST/raw/train-labels-idx1-ubyte.gz to data/FashionMNIST/raw\n",
"\n",
"Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz\n",
"Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz\n"
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"Extracting data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw\n",
"\n",
"Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz\n",
"Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz\n"
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{
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"text": [
"Extracting data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/ta180m/.local/lib/python3.9/site-packages/torchvision/datasets/mnist.py:498: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /build/python-pytorch/src/pytorch-1.9.0-opt/torch/csrc/utils/tensor_numpy.cpp:174.)\n",
" return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n"
]
}
],
"source": [
"# Download training data from open datasets.\n",
"training_data = datasets.FashionMNIST(\n",
" root=\"data\",\n",
" train=True,\n",
" download=True,\n",
" transform=ToTensor(),\n",
")\n",
"\n",
"# Download test data from open datasets.\n",
"test_data = datasets.FashionMNIST(\n",
" root=\"data\",\n",
" train=False,\n",
" download=True,\n",
" transform=ToTensor(),\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We pass the ``Dataset`` as an argument to ``DataLoader``. This wraps an iterable over our dataset, and supports\n",
"automatic batching, sampling, shuffling and multiprocess data loading. Here we define a batch size of 64, i.e. each element\n",
"in the dataloader iterable will return a batch of 64 features and labels.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Shape of X [N, C, H, W]: torch.Size([64, 1, 28, 28])\n",
"Shape of y: torch.Size([64]) torch.int64\n"
]
}
],
"source": [
"batch_size = 64\n",
"\n",
"# Create data loaders.\n",
"train_dataloader = DataLoader(training_data, batch_size=batch_size)\n",
"test_dataloader = DataLoader(test_data, batch_size=batch_size)\n",
"\n",
"for X, y in test_dataloader:\n",
" print(\"Shape of X [N, C, H, W]: \", X.shape)\n",
" print(\"Shape of y: \", y.shape, y.dtype)\n",
" break"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Read more about `loading data in PyTorch <data_tutorial.html>`_.\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"--------------\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Creating Models\n",
"------------------\n",
"To define a neural network in PyTorch, we create a class that inherits\n",
"from `nn.Module <https://pytorch.org/docs/stable/generated/torch.nn.Module.html>`_. We define the layers of the network\n",
"in the ``__init__`` function and specify how data will pass through the network in the ``forward`` function. To accelerate\n",
"operations in the neural network, we move it to the GPU if available.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using cpu device\n",
"NeuralNetwork(\n",
" (flatten): Flatten(start_dim=1, end_dim=-1)\n",
" (linear_relu_stack): Sequential(\n",
" (0): Linear(in_features=784, out_features=512, bias=True)\n",
" (1): ReLU()\n",
" (2): Linear(in_features=512, out_features=512, bias=True)\n",
" (3): ReLU()\n",
" (4): Linear(in_features=512, out_features=10, bias=True)\n",
" (5): ReLU()\n",
" )\n",
")\n"
]
}
],
"source": [
"# Get cpu or gpu device for training.\n",
"device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
"print(\"Using {} device\".format(device))\n",
"\n",
"# Define model\n",
"class NeuralNetwork(nn.Module):\n",
" def __init__(self):\n",
" super(NeuralNetwork, self).__init__()\n",
" self.flatten = nn.Flatten()\n",
" self.linear_relu_stack = nn.Sequential(\n",
" nn.Linear(28*28, 512),\n",
" nn.ReLU(),\n",
" nn.Linear(512, 512),\n",
" nn.ReLU(),\n",
" nn.Linear(512, 10),\n",
" nn.ReLU()\n",
" )\n",
"\n",
" def forward(self, x):\n",
" x = self.flatten(x)\n",
" logits = self.linear_relu_stack(x)\n",
" return logits\n",
"\n",
"model = NeuralNetwork().to(device)\n",
"print(model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Read more about `building neural networks in PyTorch <buildmodel_tutorial.html>`_.\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"--------------\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Optimizing the Model Parameters\n",
"----------------------------------------\n",
"To train a model, we need a `loss function <https://pytorch.org/docs/stable/nn.html#loss-functions>`_\n",
"and an `optimizer <https://pytorch.org/docs/stable/optim.html>`_.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [],
"source": [
"loss_fn = nn.CrossEntropyLoss()\n",
"optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In a single training loop, the model makes predictions on the training dataset (fed to it in batches), and\n",
"backpropagates the prediction error to adjust the model's parameters.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [],
"source": [
"def train(dataloader, model, loss_fn, optimizer):\n",
" size = len(dataloader.dataset)\n",
" for batch, (X, y) in enumerate(dataloader):\n",
" X, y = X.to(device), y.to(device)\n",
"\n",
" # Compute prediction error\n",
" pred = model(X)\n",
" loss = loss_fn(pred, y)\n",
"\n",
" # Backpropagation\n",
" optimizer.zero_grad()\n",
" loss.backward()\n",
" optimizer.step()\n",
"\n",
" if batch % 100 == 0:\n",
" loss, current = loss.item(), batch * len(X)\n",
" print(f\"loss: {loss:>7f} [{current:>5d}/{size:>5d}]\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We also check the model's performance against the test dataset to ensure it is learning.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [],
"source": [
"def test(dataloader, model, loss_fn):\n",
" size = len(dataloader.dataset)\n",
" num_batches = len(dataloader)\n",
" model.eval()\n",
" test_loss, correct = 0, 0\n",
" with torch.no_grad():\n",
" for X, y in dataloader:\n",
" X, y = X.to(device), y.to(device)\n",
" pred = model(X)\n",
" test_loss += loss_fn(pred, y).item()\n",
" correct += (pred.argmax(1) == y).type(torch.float).sum().item()\n",
" test_loss /= num_batches\n",
" correct /= size\n",
" print(f\"Test Error: \\n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \\n\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The training process is conducted over several iterations (*epochs*). During each epoch, the model learns\n",
"parameters to make better predictions. We print the model's accuracy and loss at each epoch; we'd like to see the\n",
"accuracy increase and the loss decrease with every epoch.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1\n",
"-------------------------------\n",
"loss: 1.758146 [ 0/60000]\n",
"loss: 1.820034 [ 6400/60000]\n",
"loss: 1.846449 [12800/60000]\n",
"loss: 1.975245 [19200/60000]\n",
"loss: 1.612495 [25600/60000]\n",
"loss: 1.748993 [32000/60000]\n",
"loss: 1.628008 [38400/60000]\n",
"loss: 1.655061 [44800/60000]\n",
"loss: 1.770255 [51200/60000]\n",
"loss: 1.654287 [57600/60000]\n",
"Test Error: \n",
" Accuracy: 37.7%, Avg loss: 1.749445 \n",
"\n",
"Epoch 2\n",
"-------------------------------\n",
"loss: 1.670408 [ 0/60000]\n",
"loss: 1.743051 [ 6400/60000]\n",
"loss: 1.773547 [12800/60000]\n",
"loss: 1.924395 [19200/60000]\n",
"loss: 1.529726 [25600/60000]\n",
"loss: 1.692361 [32000/60000]\n",
"loss: 1.559834 [38400/60000]\n",
"loss: 1.593531 [44800/60000]\n",
"loss: 1.712157 [51200/60000]\n",
"loss: 1.605115 [57600/60000]\n",
"Test Error: \n",
" Accuracy: 38.1%, Avg loss: 1.694516 \n",
"\n",
"Epoch 3\n",
"-------------------------------\n",
"loss: 1.607648 [ 0/60000]\n",
"loss: 1.684907 [ 6400/60000]\n",
"loss: 1.716139 [12800/60000]\n",
"loss: 1.888849 [19200/60000]\n",
"loss: 1.474264 [25600/60000]\n",
"loss: 1.652733 [32000/60000]\n",
"loss: 1.514825 [38400/60000]\n",
"loss: 1.549373 [44800/60000]\n",
"loss: 1.670293 [51200/60000]\n",
"loss: 1.571395 [57600/60000]\n",
"Test Error: \n",
" Accuracy: 39.0%, Avg loss: 1.653676 \n",
"\n",
"Epoch 4\n",
"-------------------------------\n",
"loss: 1.561757 [ 0/60000]\n",
"loss: 1.640771 [ 6400/60000]\n",
"loss: 1.669458 [12800/60000]\n",
"loss: 1.862879 [19200/60000]\n",
"loss: 1.435348 [25600/60000]\n",
"loss: 1.623189 [32000/60000]\n",
"loss: 1.482370 [38400/60000]\n",
"loss: 1.515045 [44800/60000]\n",
"loss: 1.638349 [51200/60000]\n",
"loss: 1.545919 [57600/60000]\n",
"Test Error: \n",
" Accuracy: 39.9%, Avg loss: 1.621615 \n",
"\n",
"Epoch 5\n",
"-------------------------------\n",
"loss: 1.525517 [ 0/60000]\n",
"loss: 1.604991 [ 6400/60000]\n",
"loss: 1.630397 [12800/60000]\n",
"loss: 1.841878 [19200/60000]\n",
"loss: 1.406707 [25600/60000]\n",
"loss: 1.599460 [32000/60000]\n",
"loss: 1.456716 [38400/60000]\n",
"loss: 1.485950 [44800/60000]\n",
"loss: 1.612476 [51200/60000]\n",
"loss: 1.525381 [57600/60000]\n",
"Test Error: \n",
" Accuracy: 40.7%, Avg loss: 1.595456 \n",
"\n",
"Done!\n"
]
}
],
"source": [
"epochs = 5\n",
"for t in range(epochs):\n",
" print(f\"Epoch {t+1}\\n-------------------------------\")\n",
" train(train_dataloader, model, loss_fn, optimizer)\n",
" test(test_dataloader, model, loss_fn)\n",
"print(\"Done!\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Read more about `Training your model <optimization_tutorial.html>`_.\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"--------------\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Saving Models\n",
"-------------\n",
"A common way to save a model is to serialize the internal state dictionary (containing the model parameters).\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Saved PyTorch Model State to model.pth\n"
]
}
],
"source": [
"torch.save(model.state_dict(), \"model.pth\")\n",
"print(\"Saved PyTorch Model State to model.pth\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Loading Models\n",
"----------------------------\n",
"\n",
"The process for loading a model includes re-creating the model structure and loading\n",
"the state dictionary into it.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"<All keys matched successfully>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = NeuralNetwork()\n",
"model.load_state_dict(torch.load(\"model.pth\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This model can now be used to make predictions.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted: \"Ankle boot\", Actual: \"Ankle boot\"\n"
]
}
],
"source": [
"classes = [\n",
" \"T-shirt/top\",\n",
" \"Trouser\",\n",
" \"Pullover\",\n",
" \"Dress\",\n",
" \"Coat\",\n",
" \"Sandal\",\n",
" \"Shirt\",\n",
" \"Sneaker\",\n",
" \"Bag\",\n",
" \"Ankle boot\",\n",
"]\n",
"\n",
"model.eval()\n",
"x, y = test_data[0][0], test_data[0][1]\n",
"with torch.no_grad():\n",
" pred = model(x)\n",
" predicted, actual = classes[pred[0].argmax(0)], classes[y]\n",
" print(f'Predicted: \"{predicted}\", Actual: \"{actual}\"')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Read more about `Saving & Loading your model <saveloadrun_tutorial.html>`_.\n",
"\n",
"\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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