# Get started¶

## Installation¶

You can install the stable version of Ensemble-PyTorch with the following command:

\$ pip install torchensemble


Ensemble-PyTorch is designed to be portable and has very few package dependencies. It is recommended to use the package environment and PyTorch installed from Anaconda.

Since Ensemble-PyTorch uses different ensemble methods to improve the performance, a key input argument is your deep learning model, serving as the base estimator. Same as PyTorch, the class of your model should inherit from torch.nn.Module, and it should at least implement two methods:

• __init__(): Instantiate sub-modules for your model and assign them as the member variables.

• forward(): Define the data forward process for your model.

For example, the code snippet below defines a multi-layered perceptron (MLP) of the structure Input(784) - 128 - 128 - Output(10):

import torch.nn as nn
from torch.nn import functional as F

class MLP(nn.Module):

def __init__(self):
super(MLP, self).__init__()
self.linear1 = nn.Linear(784, 128)
self.linear2 = nn.Linear(128, 128)
self.linear3 = nn.Linear(128, 10)

def forward(self, data):
data = data.view(data.size(0), -1)  # flatten
output = F.relu(self.linear1(data))
output = F.relu(self.linear2(output))
output = self.linear3(output)
return output


## Set the Logger¶

Ensemble-PyTorch uses a global logger to track and print the intermediate logging information. The code snippet below shows how to set up a logger:

from torchensemble.utils import set_logger

logger = set_logger('classification_mnist_mlp')


With this logger, all logging information will be printed on the command line and saved to the specified text file: classification_mnist_mlp.

In addition, when passing use_tb_logger=True into the method set_logger(), you can use tensorboard to have a better visualization result on training and evaluating the ensemble.

tensorboard --logdir=logs/


## Choose the Ensemble¶

After defining the base estimator, we can then wrap it using one of ensemble models available in Ensemble-PyTorch. Different models have very similar APIs, take the VotingClassifier as an example:

from torchensemble import VotingClassifier

model = VotingClassifier(
estimator=MLP,
n_estimators=10,
cuda=True,
)


The meaning of different arguments is listed as follow:

• estimator: The class of your model, used to instantiate base estimators in the ensemble.

• n_estimators: The number of base estimators in the ensemble.

• cuda: Specify whether to use GPU for training and evaluating the ensemble.

## Set the Optimizer¶

After declaring the ensemble, another step before the training stage is to set the optimizer. Suppose that we are going to use the Adam optimizer with learning rate 1e-3 and weight decay 5e-4 to train the ensemble, this can be achieved by calling the set_optimizer method of the ensemble:

model.set_optimizer('Adam',             # parameter optimizer
lr=1e-3,            # learning rate of the optimizer
weight_decay=5e-4)  # weight decay of the optimizer


Notice that all arguments after the optimizer name (i.e., Adam) should be in the form of keyword arguments. They be will delivered to the torch.optim.Optimizer to instantiate the internal parameter optimizer.

Setting the learning rate scheduler for the ensemble is also supported, please refer to the set_scheduler() in API Reference.

## Train and Evaluate¶

Given the ensemble with the optimizer already set, Ensemble-PyTorch provides Scikit-Learn APIs on the training and evaluating stage of the ensemble:

# Training
epochs=100)                 # the number of training epochs

# Evaluating


In the code snippet above, train_loader and test_loader is the PyTorch DataLoader object that contains your data. In addition, epochs specifies the number of training epochs. Since VotingClassifier is used for the classification, the predict() will return the classification accuracy on the test_loader.

Notice that the test_loader can also be passed to fit(), in this case, the ensemble will treat it as validation data, and evaluate the ensemble on the test_loader after each training epoch.

By setting the save_model to True and save_dir to a directory in the fit(), model parameters will be automatically saved to the path save_dir (By default, it will be saved in the same folder as the running script). After then, you can use the following code snippet to load the saved ensemble.

from torchensemble.utils import io



where new_ensemble is an ensemble instantiated in the same way as the original ensemble.

## Example on MNIST¶

The script below shows an example on using VotingClassifier with 10 MLPs for classification on the MNIST dataset.

import torch
import torch.nn as nn
from torch.nn import functional as F
from torchvision import datasets, transforms

from torchensemble import VotingClassifier
from torchensemble.utils.logging import set_logger

class MLP(nn.Module):

def __init__(self):
super(MLP, self).__init__()
self.linear1 = nn.Linear(784, 128)
self.linear2 = nn.Linear(128, 128)
self.linear3 = nn.Linear(128, 10)

def forward(self, data):
data = data.view(data.size(0), -1)
output = F.relu(self.linear1(data))
output = F.relu(self.linear2(output))
output = self.linear3(output)
return output

transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])

test = datasets.MNIST('../Dataset', train=False, transform=transform)

# Set the Logger
logger = set_logger('classification_mnist_mlp')

# Define the ensemble
model = VotingClassifier(
estimator=MLP,
n_estimators=10,
cuda=True,
)

# Set the optimizer