What is torch.nn really?¶
by Jeremy Howard, fast.ai. Thanks to Rachel Thomas and Francisco Ingham.
We recommend running this tutorial as a notebook, not a script. To download the notebook (.ipynb) file, click the link at the top of the page.
PyTorch provides the elegantly designed modules and classes torch.nn ,
to help you create and train neural networks.
In order to fully utilize their power and customize
them for your problem, you need to really understand exactly what they’re
doing. To develop this understanding, we will first train basic neural net
on the MNIST data set without using any features from these models; we will
initially only use the most basic PyTorch tensor functionality. Then, we will
incrementally add one feature from
DataLoader at a time, showing exactly what each piece does, and how it
works to make the code either more concise, or more flexible.
This tutorial assumes you already have PyTorch installed, and are familiar with the basics of tensor operations. (If you’re familiar with Numpy array operations, you’ll find the PyTorch tensor operations used here nearly identical).
MNIST data setup¶
We will use the classic MNIST dataset, which consists of black-and-white images of hand-drawn digits (between 0 and 9).
We will use pathlib for dealing with paths (part of the Python 3 standard library), and will download the dataset using requests. We will only import modules when we use them, so you can see exactly what’s being used at each point.
from pathlib import Path import requests DATA_PATH = Path("data") PATH = DATA_PATH / "mnist" PATH.mkdir(parents=True, exist_ok=True) URL = "http://deeplearning.net/data/mnist/" FILENAME = "mnist.pkl.gz" if not (PATH / FILENAME).exists(): content = requests.get(URL + FILENAME).content (PATH / FILENAME).open("wb").write(content)
This dataset is in numpy array format, and has been stored using pickle, a python-specific format for serializing data.
import pickle import gzip with gzip.open((PATH / FILENAME).as_posix(), "rb") as f: ((x_train, y_train), (x_valid, y_valid), _) = pickle.load(f, encoding="latin-1")
Each image is 28 x 28, and is being stored as a flattened row of length 784 (=28x28). Let’s take a look at one; we need to reshape it to 2d first.
from matplotlib import pyplot import numpy as np pyplot.imshow(x_train.reshape((28, 28)), cmap="gray") print(x_train.shape)
torch.tensor, rather than numpy arrays, so we need to
convert our data.
import torch x_train, y_train, x_valid, y_valid = map( torch.tensor, (x_train, y_train, x_valid, y_valid) ) n, c = x_train.shape x_train, x_train.shape, y_train.min(), y_train.max() print(x_train, y_train) print(x_train.shape) print(y_train.min(), y_train.max())
Neural net from scratch (no torch.nn)¶
Let’s first create a model using nothing but PyTorch tensor operations. We’re assuming you’re already familiar with the basics of neural networks. (If you’re not, you can learn them at course.fast.ai).
PyTorch provides methods to create random or zero-filled tensors, which we will use to create our weights and bias for a simple linear model. These are just regular tensors, with one very special addition: we tell PyTorch that they require a gradient. This causes PyTorch to record all of the operations done on the tensor, so that it can calculate the gradient during back-propagation automatically!
For the weights, we set
requires_grad after the initialization, since we
don’t want that step included in the gradient. (Note that a trailling
PyTorch signifies that the operation is performed in-place.)
We are initializing the weights here with Xavier initialisation (by multiplying with 1/sqrt(n)).
import math weights = torch.randn(784, 10) / math.sqrt(784) weights.requires_grad_() bias = torch.zeros(10, requires_grad=True)
Thanks to PyTorch’s ability to calculate gradients automatically, we can use any standard Python function (or callable object) as a model! So let’s just write a plain matrix multiplication and broadcasted addition to create a simple linear model. We also need an activation function, so we’ll write log_softmax and use it. Remember: although PyTorch provides lots of pre-written loss functions, activation functions, and so forth, you can easily write your own using plain python. PyTorch will even create fast GPU or vectorized CPU code for your function automatically.
def log_softmax(x): return x - x.exp().sum(-1).log().unsqueeze(-1) def model(xb): return log_softmax(xb @ weights + bias)
In the above, the
@ stands for the dot product operation. We will call
our function on one batch of data (in this case, 64 images). This is
one forward pass. Note that our predictions won’t be any better than
random at this stage, since we start with random weights.
bs = 64 # batch size xb = x_train[0:bs] # a mini-batch from x preds = model(xb) # predictions preds, preds.shape print(preds, preds.shape)
As you see, the
preds tensor contains not only the tensor values, but also a
gradient function. We’ll use this later to do backprop.
Let’s implement negative log-likelihood to use as the loss function (again, we can just use standard Python):
def nll(input, target): return -input[range(target.shape), target].mean() loss_func = nll
Let’s check our loss with our random model, so we can see if we improve after a backprop pass later.
yb = y_train[0:bs] print(loss_func(preds, yb))
Let’s also implement a function to calculate the accuracy of our model. For each prediction, if the index with the largest value matches the target value, then the prediction was correct.
def accuracy(out, yb): preds = torch.argmax(out, dim=1) return (preds == yb).float().mean()
Let’s check the accuracy of our random model, so we can see if our accuracy improves as our loss improves.
We can now run a training loop. For each iteration, we will:
- select a mini-batch of data (of size
- use the model to make predictions
- calculate the loss
loss.backward()updates the gradients of the model, in this case,
We now use these gradients to update the weights and bias. We do this
torch.no_grad() context manager, because we do not want these
actions to be recorded for our next calculation of the gradient. You can read
more about how PyTorch’s Autograd records operations
We then set the
gradients to zero, so that we are ready for the next loop.
Otherwise, our gradients would record a running tally of all the operations
that had happened (i.e.
loss.backward() adds the gradients to whatever is
already stored, rather than replacing them).
You can use the standard python debugger to step through PyTorch
code, allowing you to check the various variable values at each step.
set_trace() below to try it out.
from IPython.core.debugger import set_trace lr = 0.5 # learning rate epochs = 2 # how many epochs to train for for epoch in range(epochs): for i in range((n - 1) // bs + 1): # set_trace() start_i = i * bs end_i = start_i + bs xb = x_train[start_i:end_i] yb = y_train[start_i:end_i] pred = model(xb) loss = loss_func(pred, yb) loss.backward() with torch.no_grad(): weights -= weights.grad * lr bias -= bias.grad * lr weights.grad.zero_() bias.grad.zero_()
That’s it: we’ve created and trained a minimal neural network (in this case, a logistic regression, since we have no hidden layers) entirely from scratch!
Let’s check the loss and accuracy and compare those to what we got earlier. We expect that the loss will have decreased and accuracy to have increased, and they have.
print(loss_func(model(xb), yb), accuracy(model(xb), yb))
We will now refactor our code, so that it does the same thing as before, only
we’ll start taking advantage of PyTorch’s
nn classes to make it more concise
and flexible. At each step from here, we should be making our code one or more
of: shorter, more understandable, and/or more flexible.
The first and easiest step is to make our code shorter by replacing our
hand-written activation and loss functions with those from
(which is generally imported into the namespace
F by convention). This module
contains all the functions in the
torch.nn library (whereas other parts of the
library contain classes). As well as a wide range of loss and activation
functions, you’ll also find here some convenient functions for creating neural
nets, such as pooling functions. (There are also functions for doing convolutions,
linear layers, etc, but as we’ll see, these are usually better handled using
other parts of the library.)
If you’re using negative log likelihood loss and log softmax activation,
then Pytorch provides a single function
F.cross_entropy that combines
the two. So we can even remove the activation function from our model.
import torch.nn.functional as F loss_func = F.cross_entropy def model(xb): return xb @ weights + bias
Note that we no longer call
log_softmax in the
model function. Let’s
confirm that our loss and accuracy are the same as before:
print(loss_func(model(xb), yb), accuracy(model(xb), yb))
Refactor using nn.Module¶
Next up, we’ll use
nn.Parameter, for a clearer and more
concise training loop. We subclass
nn.Module (which itself is a class and
able to keep track of state). In this case, we want to create a class that
holds our weights, bias, and method for the forward step.
nn.Module has a
number of attributes and methods (such as
which we will be using.
nn.Module (uppercase M) is a PyTorch specific concept, and is a
class we’ll be using a lot.
nn.Module is not to be confused with the Python
concept of a (lowercase
which is a file of Python code that can be imported.
from torch import nn class Mnist_Logistic(nn.Module): def __init__(self): super().__init__() self.weights = nn.Parameter(torch.randn(784, 10) / math.sqrt(784)) self.bias = nn.Parameter(torch.zeros(10)) def forward(self, xb): return xb @ self.weights + self.bias
Since we’re now using an object instead of just using a function, we first have to instantiate our model:
model = Mnist_Logistic()
Now we can calculate the loss in the same way as before. Note that
nn.Module objects are used as if they are functions (i.e they are
callable), but behind the scenes Pytorch will call our
Previously for our training loop we had to update the values for each parameter by name, and manually zero out the grads for each parameter separately, like this:
with torch.no_grad(): weights -= weights.grad * lr bias -= bias.grad * lr weights.grad.zero_() bias.grad.zero_()
Now we can take advantage of model.parameters() and model.zero_grad() (which
are both defined by PyTorch for
nn.Module) to make those steps more concise
and less prone to the error of forgetting some of our parameters, particularly
if we had a more complicated model:
with torch.no_grad(): for p in model.parameters(): p -= p.grad * lr model.zero_grad()
We’ll wrap our little training loop in a
fit function so we can run it
def fit(): for epoch in range(epochs): for i in range((n - 1) // bs + 1): start_i = i * bs end_i = start_i + bs xb = x_train[start_i:end_i] yb = y_train[start_i:end_i] pred = model(xb) loss = loss_func(pred, yb) loss.backward() with torch.no_grad(): for p in model.parameters(): p -= p.grad * lr model.zero_grad() fit()
Let’s double-check that our loss has gone down:
Refactor using nn.Linear¶
We continue to refactor our code. Instead of manually defining and
self.bias, and calculating
self.weights + self.bias, we will instead use the Pytorch class
nn.Linear for a
linear layer, which does all that for us. Pytorch has many types of
predefined layers that can greatly simplify our code, and often makes it
class Mnist_Logistic(nn.Module): def __init__(self): super().__init__() self.lin = nn.Linear(784, 10) def forward(self, xb): return self.lin(xb)
We instantiate our model and calculate the loss in the same way as before:
model = Mnist_Logistic() print(loss_func(model(xb), yb))
We are still able to use our same
fit method as before.
fit() print(loss_func(model(xb), yb))
Refactor using optim¶
Pytorch also has a package with various optimization algorithms,
We can use the
step method from our optimizer to take a forward step, instead
of manually updating each parameter.
This will let us replace our previous manually coded optimization step:
with torch.no_grad(): for p in model.parameters(): p -= p.grad * lr model.zero_grad()
and instead use just:
optim.zero_grad() resets the gradient to 0 and we need to call it before
computing the gradient for the next minibatch.)
from torch import optim
We’ll define a little function to create our model and optimizer so we can reuse it in the future.
def get_model(): model = Mnist_Logistic() return model, optim.SGD(model.parameters(), lr=lr) model, opt = get_model() print(loss_func(model(xb), yb)) for epoch in range(epochs): for i in range((n - 1) // bs + 1): start_i = i * bs end_i = start_i + bs xb = x_train[start_i:end_i] yb = y_train[start_i:end_i] pred = model(xb) loss = loss_func(pred, yb) loss.backward() opt.step() opt.zero_grad() print(loss_func(model(xb), yb))
Refactor using Dataset¶
PyTorch has an abstract Dataset class. A Dataset can be anything that has
__len__ function (called by Python’s standard
len function) and
__getitem__ function as a way of indexing into it.
walks through a nice example of creating a custom
as a subclass of
PyTorch’s TensorDataset is a Dataset wrapping tensors. By defining a length and way of indexing, this also gives us a way to iterate, index, and slice along the first dimension of a tensor. This will make it easier to access both the independent and dependent variables in the same line as we train.
from torch.utils.data import TensorDataset
y_train can be combined in a single
which will be easier to iterate over and slice.
train_ds = TensorDataset(x_train, y_train)
Previously, we had to iterate through minibatches of x and y values separately:
xb = x_train[start_i:end_i] yb = y_train[start_i:end_i]
Now, we can do these two steps together:
xb,yb = train_ds[i*bs : i*bs+bs]
model, opt = get_model() for epoch in range(epochs): for i in range((n - 1) // bs + 1): xb, yb = train_ds[i * bs: i * bs + bs] pred = model(xb) loss = loss_func(pred, yb) loss.backward() opt.step() opt.zero_grad() print(loss_func(model(xb), yb))
Refactor using DataLoader¶
DataLoader is responsible for managing batches. You can
DataLoader from any
DataLoader makes it easier
to iterate over batches. Rather than having to use
train_ds[i*bs : i*bs+bs],
the DataLoader gives us each minibatch automatically.
from torch.utils.data import DataLoader train_ds = TensorDataset(x_train, y_train) train_dl = DataLoader(train_ds, batch_size=bs)
Previously, our loop iterated over batches (xb, yb) like this:
for i in range((n-1)//bs + 1): xb,yb = train_ds[i*bs : i*bs+bs] pred = model(xb)
Now, our loop is much cleaner, as (xb, yb) are loaded automatically from the data loader:
for xb,yb in train_dl: pred = model(xb)
model, opt = get_model() for epoch in range(epochs): for xb, yb in train_dl: pred = model(xb) loss = loss_func(pred, yb) loss.backward() opt.step() opt.zero_grad() print(loss_func(model(xb), yb))
Thanks to Pytorch’s
our training loop is now dramatically smaller and easier to understand. Let’s
now try to add the basic features necessary to create effecive models in practice.
In section 1, we were just trying to get a reasonable training loop set up for use on our training data. In reality, you always should also have a validation set, in order to identify if you are overfitting.
Shuffling the training data is important to prevent correlation between batches and overfitting. On the other hand, the validation loss will be identical whether we shuffle the validation set or not. Since shuffling takes extra time, it makes no sense to shuffle the validation data.
We’ll use a batch size for the validation set that is twice as large as that for the training set. This is because the validation set does not need backpropagation and thus takes less memory (it doesn’t need to store the gradients). We take advantage of this to use a larger batch size and compute the loss more quickly.
train_ds = TensorDataset(x_train, y_train) train_dl = DataLoader(train_ds, batch_size=bs, shuffle=True) valid_ds = TensorDataset(x_valid, y_valid) valid_dl = DataLoader(valid_ds, batch_size=bs * 2)
We will calculate and print the validation loss at the end of each epoch.
(Note that we always call
model.train() before training, and
before inference, because these are used by layers such as
nn.Dropout to ensure appropriate behaviour for these different phases.)
model, opt = get_model() for epoch in range(epochs): model.train() for xb, yb in train_dl: pred = model(xb) loss = loss_func(pred, yb) loss.backward() opt.step() opt.zero_grad() model.eval() with torch.no_grad(): valid_loss = sum(loss_func(model(xb), yb) for xb, yb in valid_dl) print(epoch, valid_loss / len(valid_dl))
Create fit() and get_data()¶
We’ll now do a little refactoring of our own. Since we go through a similar
process twice of calculating the loss for both the training set and the
validation set, let’s make that into its own function,
computes the loss for one batch.
We pass an optimizer in for the training set, and use it to perform backprop. For the validation set, we don’t pass an optimizer, so the method doesn’t perform backprop.
def loss_batch(model, loss_func, xb, yb, opt=None): loss = loss_func(model(xb), yb) if opt is not None: loss.backward() opt.step() opt.zero_grad() return loss.item(), len(xb)
fit runs the necessary operations to train our model and compute the
training and validation losses for each epoch.
import numpy as np def fit(epochs, model, loss_func, opt, train_dl, valid_dl): for epoch in range(epochs): model.train() for xb, yb in train_dl: loss_batch(model, loss_func, xb, yb, opt) model.eval() with torch.no_grad(): losses, nums = zip( *[loss_batch(model, loss_func, xb, yb) for xb, yb in valid_dl] ) val_loss = np.sum(np.multiply(losses, nums)) / np.sum(nums) print(epoch, val_loss)
get_data returns dataloaders for the training and validation sets.
def get_data(train_ds, valid_ds, bs): return ( DataLoader(train_ds, batch_size=bs, shuffle=True), DataLoader(valid_ds, batch_size=bs * 2), )
Now, our whole process of obtaining the data loaders and fitting the model can be run in 3 lines of code:
train_dl, valid_dl = get_data(train_ds, valid_ds, bs) model, opt = get_model() fit(epochs, model, loss_func, opt, train_dl, valid_dl)
You can use these basic 3 lines of code to train a wide variety of models. Let’s see if we can use them to train a convolutional neural network (CNN)!
Switch to CNN¶
We are now going to build our neural network with three convolutional layers. Because none of the functions in the previous section assume anything about the model form, we’ll be able to use them to train a CNN without any modification.
We will use Pytorch’s predefined
as our convolutional layer. We define a CNN with 3 convolutional layers.
Each convolution is followed by a ReLU. At the end, we perform an
average pooling. (Note that
view is PyTorch’s version of numpy’s
class Mnist_CNN(nn.Module): def __init__(self): super().__init__() self.conv1 = nn.Conv2d(1, 16, kernel_size=3, stride=2, padding=1) self.conv2 = nn.Conv2d(16, 16, kernel_size=3, stride=2, padding=1) self.conv3 = nn.Conv2d(16, 10, kernel_size=3, stride=2, padding=1) def forward(self, xb): xb = xb.view(-1, 1, 28, 28) xb = F.relu(self.conv1(xb)) xb = F.relu(self.conv2(xb)) xb = F.relu(self.conv3(xb)) xb = F.avg_pool2d(xb, 4) return xb.view(-1, xb.size(1)) lr = 0.1
Momentum is a variation on stochastic gradient descent that takes previous updates into account as well and generally leads to faster training.
model = Mnist_CNN() opt = optim.SGD(model.parameters(), lr=lr, momentum=0.9) fit(epochs, model, loss_func, opt, train_dl, valid_dl)
torch.nn has another handy class we can use to simply our code:
Sequential object runs each of the modules contained within it, in a
sequential manner. This is a simpler way of writing our neural network.
To take advantage of this, we need to be able to easily define a
custom layer from a given function. For instance, PyTorch doesn’t
have a view layer, and we need to create one for our network.
will create a layer that we can then use when defining a network with
class Lambda(nn.Module): def __init__(self, func): super().__init__() self.func = func def forward(self, x): return self.func(x) def preprocess(x): return x.view(-1, 1, 28, 28)
The model created with
Sequential is simply:
model = nn.Sequential( Lambda(preprocess), nn.Conv2d(1, 16, kernel_size=3, stride=2, padding=1), nn.ReLU(), nn.Conv2d(16, 16, kernel_size=3, stride=2, padding=1), nn.ReLU(), nn.Conv2d(16, 10, kernel_size=3, stride=2, padding=1), nn.ReLU(), nn.AvgPool2d(4), Lambda(lambda x: x.view(x.size(0), -1)), ) opt = optim.SGD(model.parameters(), lr=lr, momentum=0.9) fit(epochs, model, loss_func, opt, train_dl, valid_dl)
- Our CNN is fairly concise, but it only works with MNIST, because:
- It assumes the input is a 28*28 long vector
- It assumes that the final CNN grid size is 4*4 (since that’s the average
pooling kernel size we used)
Let’s get rid of these two assumptions, so our model works with any 2d single channel image. First, we can remove the initial Lambda layer but moving the data preprocessing into a generator:
def preprocess(x, y): return x.view(-1, 1, 28, 28), y class WrappedDataLoader: def __init__(self, dl, func): self.dl = dl self.func = func def __len__(self): return len(self.dl) def __iter__(self): batches = iter(self.dl) for b in batches: yield (self.func(*b)) train_dl, valid_dl = get_data(train_ds, valid_ds, bs) train_dl = WrappedDataLoader(train_dl, preprocess) valid_dl = WrappedDataLoader(valid_dl, preprocess)
Next, we can replace
allows us to define the size of the output tensor we want, rather than
the input tensor we have. As a result, our model will work with any
model = nn.Sequential( nn.Conv2d(1, 16, kernel_size=3, stride=2, padding=1), nn.ReLU(), nn.Conv2d(16, 16, kernel_size=3, stride=2, padding=1), nn.ReLU(), nn.Conv2d(16, 10, kernel_size=3, stride=2, padding=1), nn.ReLU(), nn.AdaptiveAvgPool2d(1), Lambda(lambda x: x.view(x.size(0), -1)), ) opt = optim.SGD(model.parameters(), lr=lr, momentum=0.9)
Let’s try it out:
fit(epochs, model, loss_func, opt, train_dl, valid_dl)
Using your GPU¶
If you’re lucky enough to have access to a CUDA-capable GPU (you can rent one for about $0.50/hour from most cloud providers) you can use it to speed up your code. First check that your GPU is working in Pytorch:
And then create a device object for it:
dev = torch.device( "cuda") if torch.cuda.is_available() else torch.device("cpu")
preprocess to move batches to the GPU:
def preprocess(x, y): return x.view(-1, 1, 28, 28).to(dev), y.to(dev) train_dl, valid_dl = get_data(train_ds, valid_ds, bs) train_dl = WrappedDataLoader(train_dl, preprocess) valid_dl = WrappedDataLoader(valid_dl, preprocess)
Finally, we can move our model to the GPU.
model.to(dev) opt = optim.SGD(model.parameters(), lr=lr, momentum=0.9)
You should find it runs faster now:
fit(epochs, model, loss_func, opt, train_dl, valid_dl)
We now have a general data pipeline and training loop which you can use for training many types of models using Pytorch. To see how simple training a model can now be, take a look at the mnist_sample sample notebook.
Of course, there are many things you’ll want to add, such as data augmentation, hyperparameter tuning, monitoring training, transfer learning, and so forth. These features are available in the fastai library, which has been developed using the same design approach shown in this tutorial, providing a natural next step for practitioners looking to take their models further.
We promised at the start of this tutorial we’d explain through example each of
DataLoader. So let’s summarize
what we’ve seen:
Module: creates a callable which behaves like a function, but can also contain state(such as neural net layer weights). It knows what
Parameter(s) it contains and can zero all their gradients, loop through them for weight updates, etc.
Parameter: a wrapper for a tensor that tells a
Modulethat it has weights that need updating during backprop. Only tensors with the requires_grad attribute set are updated
functional: a module(usually imported into the
Fnamespace by convention) which contains activation functions, loss functions, etc, as well as non-stateful versions of layers such as convolutional and linear layers.
torch.optim: Contains optimizers such as
SGD, which update the weights of
Parameterduring the backward step
Dataset: An abstract interface of objects with a
__getitem__, including classes provided with Pytorch such as
DataLoader: Takes any
Datasetand creates an iterator which returns batches of data.
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