# Introduction to PyTorch and Poutyne¶

Note

In this example, we train a simple fully-connected network and a simple convolutional network on MNIST. First, we train it by coding our own training loop as the PyTorch library expects of us to. Then, we use Poutyne to simplify our code.

Let’s import all the needed packages.

import math

import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import transforms, utils
from torchvision.datasets.mnist import MNIST

from poutyne import set_seeds, Model


Also, we need to set Pythons’s, NumPy’s and PyTorch’s seeds by using Poutyne function so that our training is (almost) reproducible.

set_seeds(42)


## Basis of Training a Neural Network¶

In stochastic gradient descent, a batch of m examples are drawn from the train dataset. In the so-called forward pass, these examples are passed through the neural network and an average of their loss values is done. In the backward pass, the average loss is backpropagated through the network to compute the gradient of each parameter. In practice, the m examples of a batch are drawn without replacement. Thus, we define one epoch of training being the number of batches needed to loop through the entire training dataset.

In addition to the training dataset, a validation dataset is used to evaluate the neural network at the end of each epoch. This validation dataset can be used to select the best model during training and thus avoiding overfitting the training set. It also can have other uses such as selecting hyperparameters

Finally, a test dataset is used at the end to evaluate the final model.

### Training constants¶

Now, let’s set our training constants. We first have the CUDA device used for training if one is present. Second, we set the train_split to 0.8 (80%) to use 80% of the dataset for training and 20% for validation. Third, we set the number of classes (i.e. one for each number). Finally, we set the batch size (i.e. the number of elements to see before updating the model), the learning rate for the optimizer, and the number of epochs (i.e. the number of times we see the full dataset).

cuda_device = 0
device = torch.device("cuda:%d" % cuda_device if torch.cuda.is_available() else "cpu")

train_split_percent = 0.8

num_classes = 10

batch_size = 32
learning_rate = 0.1
num_epochs = 5


The following loads the MNIST dataset and creates the PyTorch DataLoaders that split our datasets into batches. The train DataLoader shuffles the examples of the train dataset to draw the examples without replacement.

full_train_dataset = MNIST('./datasets/', train=True, download=True, transform=transforms.ToTensor())

num_data = len(full_train_dataset)
indices = list(range(num_data))
np.random.shuffle(indices)

split = math.floor(train_split_percent * num_data)

train_indices = indices[:split]
train_dataset = Subset(full_train_dataset, train_indices)

valid_indices = indices[split:]
valid_dataset = Subset(full_train_dataset, valid_indices)



Let’s look at some examples of the dataset by looking at the first batch in our train DataLoader and formatting it into a grid and plotting it.

inputs = next(iter(train_loader))[0]
input_grid = utils.make_grid(inputs)

fig = plt.figure(figsize=(10, 10))
inp = input_grid.numpy().transpose((1, 2, 0))
plt.imshow(inp)
plt.show()


Here the resulting image

### Neural Network Architectures¶

We train a fully-connected neural network and a convolutional neural network with approximately the same number of parameters.

#### Fully-connected Network¶

In short, the fully-connected network follows this architecture: Input -> [Linear -> ReLU]*3 -> Linear. The following table shows it in details:

Layer Type

Output size

# of Parameters

Input

1x28x28

0

Flatten

1*28*28

0

Linear with 256 neurons

256

28*28*256 + 256 = 200,960

ReLU

*

0

Linear with 128 neurons

128

256*128 + 128 = 32,896

ReLU

*

0

Linear with 64 neurons

64

128*64 + 64 = 8,256

ReLU

*

0

Linear with 10 neurons

10

64*10 + 10 = 650

Total # of parameters of the fully-connected network: 242,762

#### Convolutional Network¶

The convolutional neural network architecture starts with some convolution and max-pooling layers. These are then followed by fully-connected layers. We calculate the total number of parameters that the network needs. In short, the convolutional network follows this architecture: Input -> [Conv -> ReLU -> MaxPool]*2 -> Dropout -> Linear -> ReLU -> Dropout -> Linear. The following table shows it in details:

Layer Type

Output Size

# of Parameters

Input

1x28x28

0

Conv with 16 3x3 filters with padding of 1

16x28x28

16*1*3*3 + 16 = 160

ReLU

16x28x28

0

MaxPool 2x2

16x14x14

0

Conv with 32 3x3 filters with padding of 1

32x14x14

32*16*3*3 + 32 = 4,640

ReLU

32x14x14

0

MaxPool 2x2

32x7x7

0

Dropout of 0.25

32x7x7

0

Flatten

32*7*7

0

Linear with 128 neurons

128

32*7*7*128 + 128 = 200,832

ReLU

128

0

Dropout of 0.5

128

0

Linear with 10 neurons

10

128*10 + 10 = 1290

Total # of parameters of the convolutional network: 206,922

def create_fully_connected_network():
"""
This function returns the fully-connected network layed out above.
"""
return nn.Sequential(
nn.Flatten(),
nn.Linear(28*28, 256),
nn.ReLU(),
nn.Linear(256, 128),
nn.ReLU(),
nn.Linear(128, 64),
nn.ReLU(),
nn.Linear(64, num_classes)
)

def create_convolutional_network():
"""
This function returns the convolutional network layed out above.
"""
return nn.Sequential(
nn.ReLU(),
nn.MaxPool2d(2),
nn.ReLU(),
nn.MaxPool2d(2),
nn.Dropout(0.25),
nn.Flatten(),
nn.Linear(32*7*7, 128),
nn.ReLU(),
nn.Dropout(0.5),
nn.Linear(128, num_classes)
)


## Training the PyTorch way¶

That is, doing your own training loop.

def pytorch_accuracy(y_pred, y_true):
"""
Computes the accuracy for a batch of predictions

Args:
y_pred (torch.Tensor): the logit predictions of the neural network.
y_true (torch.Tensor): the ground truths.

Returns:
The average accuracy of the batch.
"""
y_pred = y_pred.argmax(1)
return (y_pred == y_true).float().mean() * 100

def pytorch_train_one_epoch(pytorch_network, optimizer, loss_function):
"""
Trains the neural network for one epoch on the train DataLoader.

Args:
pytorch_network (torch.nn.Module): The neural network to train.
optimizer (torch.optim.Optimizer): The optimizer of the neural network
loss_function: The loss function.

Returns:
A tuple (loss, accuracy) corresponding to an average of the losses and
an average of the accuracy, respectively, on the train DataLoader.
"""
pytorch_network.train(True)
loss_sum = 0.
acc_sum = 0.
example_count = 0
# Transfer batch on GPU if needed.
x = x.to(device)
y = y.to(device)

y_pred = pytorch_network(x)

loss = loss_function(y_pred, y)

loss.backward()

optimizer.step()

# Since the loss and accuracy are averages for the batch, we multiply
# them by the the number of examples so that we can do the right
# averages at the end of the epoch.
loss_sum += float(loss) * len(x)
acc_sum += float(pytorch_accuracy(y_pred, y)) * len(x)
example_count += len(x)

avg_loss = loss_sum / example_count
avg_acc = acc_sum / example_count
return avg_loss, avg_acc

"""
Tests the neural network on a DataLoader.

Args:
pytorch_network (torch.nn.Module): The neural network to test.
loss_function: The loss function.

Returns:
A tuple (loss, accuracy) corresponding to an average of the losses and
an average of the accuracy, respectively, on the DataLoader.
"""
pytorch_network.eval()
loss_sum = 0.
acc_sum = 0.
example_count = 0
# Transfer batch on GPU if needed.
x = x.to(device)
y = y.to(device)

y_pred = pytorch_network(x)
loss = loss_function(y_pred, y)

# Since the loss and accuracy are averages for the batch, we multiply
# them by the the number of examples so that we can do the right
# averages at the end of the test.
loss_sum += float(loss) * len(x)
acc_sum += float(pytorch_accuracy(y_pred, y)) * len(x)
example_count += len(x)

avg_loss = loss_sum / example_count
avg_acc = acc_sum / example_count
return avg_loss, avg_acc

def pytorch_train(pytorch_network):
"""
This function transfers the neural network to the right device,
trains it for a certain number of epochs, tests at each epoch on
the validation set and outputs the results on the test set at the
end of training.

Args:
pytorch_network (torch.nn.Module): The neural network to train.

Example:
This function displays something like this:

.. code-block:: python

Epoch 1/5: loss: 0.5026924496193726, acc: 84.26666259765625, val_loss: 0.17258917854229608, val_acc: 94.75
Epoch 2/5: loss: 0.13690324830015502, acc: 95.73332977294922, val_loss: 0.14024296019474666, val_acc: 95.68333435058594
Epoch 3/5: loss: 0.08836929737279813, acc: 97.29582977294922, val_loss: 0.10380942322810491, val_acc: 96.66666412353516
Epoch 4/5: loss: 0.06714504160980383, acc: 97.91874694824219, val_loss: 0.09626663728555043, val_acc: 97.18333435058594
Epoch 5/5: loss: 0.05063822727650404, acc: 98.42708587646484, val_loss: 0.10017542181412378, val_acc: 96.95833587646484
Test:
Loss: 0.09501855444908142
Accuracy: 97.12999725341797
"""
print(pytorch_network)

# Transfer weights on GPU if needed.
pytorch_network.to(device)

optimizer = optim.SGD(pytorch_network.parameters(), lr=learning_rate)
loss_function = nn.CrossEntropyLoss()

for epoch in range(1, num_epochs + 1):
# Training the neural network via backpropagation
train_loss, train_acc = pytorch_train_one_epoch(pytorch_network, optimizer, loss_function)

# Validation at the end of the epoch
valid_loss, valid_acc = pytorch_test(pytorch_network, valid_loader, loss_function)

print("Epoch {}/{}: loss: {}, acc: {}, val_loss: {}, val_acc: {}".format(
epoch, num_epochs, train_loss, train_acc, valid_loss, valid_acc
))

# Test at the end of the training
test_loss, test_acc = pytorch_test(pytorch_network, test_loader, loss_function)
print('Test:\n\tLoss: {}\n\tAccuracy: {}'.format(test_loss, test_acc))


Let’s train the convolutional network.

fc_net = create_fully_connected_network()
pytorch_train(fc_net)


Let’s train the convolutional network.

conv_net = create_convolutional_network()
pytorch_train(conv_net)


## Training the Poutyne way¶

That is, only 8 lines of code with a better output.

def poutyne_train(pytorch_network):
"""
This function creates a Poutyne Model (see https://poutyne.org/model.html), sends the
Model on the specified device, and uses the fit_generator method to train the
neural network. At the end, the evaluate_generator is used on  the test set.

Args:
pytorch_network (torch.nn.Module): The neural network to train.
"""
print(pytorch_network)

optimizer = optim.SGD(pytorch_network.parameters(), lr=learning_rate)
loss_function = nn.CrossEntropyLoss()

# Poutyne Model on GPU
model = Model(pytorch_network, optimizer, loss_function, batch_metrics=['accuracy'], device=device)

# Train

# Test
print('Test:\n\tLoss: {}\n\tAccuracy: {}'.format(test_loss, test_acc))


Let’s train the fully connected network.

fc_net = create_fully_connected_network()
poutyne_train(fc_net)


Let’s train the convolutional network.

conv_net = create_convolutional_network()
poutyne_train(conv_net)


## Poutyne Callbacks¶

One nice feature of Poutyne is callbacks. Callbacks allow doing actions during the training of the neural network. In the following example, we use three callbacks. One that saves the latest weights in a file to be able to continue the optimization at the end of training if more epochs are needed. Another one that saves the best weights according to the performance on the validation dataset. Finally, another one that saves the displayed logs into a TSV file.

def train_with_callbacks(name, pytorch_network):
"""
In addition to the the poutyne_train, this function saves checkpoints and logs as described above.

Args:
name (str): a name used to save logs and checkpoints.
pytorch_network (torch.nn.Module): The neural network to train.
"""
print(pytorch_network)

# We are saving everything into ./saves/{name}.
save_path = os.path.join('saves', name)

# Creating saving directory if necessary.
os.makedirs(save_path, exist_ok=True)

callbacks = [
# Save the latest weights to be able to continue the optimization at the end for more epochs.
ModelCheckpoint(os.path.join(save_path, 'last_epoch.ckpt')),

# Save the weights in a new file when the current model is better than all previous models.
ModelCheckpoint(os.path.join(save_path, 'best_epoch_{epoch}.ckpt'), monitor='val_acc', mode='max',
save_best_only=True, restore_best=True, verbose=True),

# Save the losses and accuracies for each epoch in a TSV.
CSVLogger(os.path.join(save_path, 'log.tsv'), separator='\t'),
]

optimizer = optim.SGD(pytorch_network.parameters(), lr=learning_rate)
loss_function = nn.CrossEntropyLoss()

model = Model(pytorch_network, optimizer, loss_function, batch_metrics=['accuracy'], device=device)

print('Test:\n\tLoss: {}\n\tAccuracy: {}'.format(test_loss, test_acc))


Let’s train the fully connected network with callbacks.

fc_net = create_fully_connected_network()
train_with_callbacks('fc', fc_net)


Let’s train the convolutional network with callbacks.

conv_net = create_convolutional_network()
train_with_callbacks('conv', conv_net)


While Poutyne provides a great number of predefined callbacks, it is sometimes useful to make your own callback. In addition to the documentation of the Callback class, see the Making Your Own Callback section in the Tips and Tricks page for an example.

## Poutyne Experiment¶

Most of the time when using Poutyne (or even Pytorch in general), we will find ourselves in an iterative model hyperparameters finetuning loop. For efficient model search, we will usually wish to save our best performing models, their training and testing statistics and even sometimes wish to retrain an already trained model for further tuning. All of the above can be easily implemented with the flexibility of Poutyne Callbacks, but having to define and initialize each and every Callback object we wish for our model quickly feels cumbersome.

This is why Poutyne provides an Experiment, which aims specifically at enabling quick model iteration search, while not sacrifying on the quality of a single experiment - statistics logging, best models saving, etc. Experiment is actually a simple wrapper between a PyTorch network and Poutyne’s core Callback objects for logging and saving. Given a working directory where to output the various logging files and a PyTorch network, the Experiment class reduces the whole training loop to a single line.

The following code uses Poutyne’s Experiment class to train a network for 5 epochs. The code is quite simpler than the code in the Poutyne Callbacks section while doing more (only 3 lines). Once trained for 5 epochs, it is then possible to resume the optimization at the 5th epoch for 5 more epochs until the 10th epoch using the same function.

def experiment_train(pytorch_network, name, epochs=5):
"""
This function creates a Poutyne Experiment, trains the input module
on the train loader and then tests its performance on the test loader.
All training and testing statistics are saved, as well as best model
checkpoints.

Args:
pytorch_network (torch.nn.Module): The neural network to train.
working_directory (str): The directory where to output files to save.
epochs (int): The number of epochs. (Default: 5)
"""
print(pytorch_network)

optimizer = optim.SGD(pytorch_network.parameters(), lr=learning_rate)

# Everything is going to be saved in ./saves/{name}.
save_path = os.path.join('saves', name)

# Poutyne Experiment
expt = Experiment(save_path, pytorch_network, device=device, optimizer=optimizer, task='classif')

# Train

# Test

Let’s train the convolutional network with Experiment for 5 epochs. Everything is saved in ./conv_net_experiment.
conv_net = create_convolutional_network()

conv_net = create_convolutional_network()