Overview of mini-batch learning.
Minibatch learning is one of the most widely used and efficient learning methods in machine learning, which is computationally more efficient and applicable to large datasets compared to the usual gradient descent (Gradient Descent) method. This section provides an overview of mini-batch learning.
Mini-batch learning is a learning method in which the entire dataset is not processed at once, but rather samples are grouped into several pieces (called mini-batches) and processed in batches, where the gradient of the loss function is calculated for each mini-batch and the parameters are updated using the gradient.
The specific steps are as follows.
1. shuffling the dataset: the entire dataset is randomly shuffled. This prevents training bias.
2. creating mini-batches: extracting samples of a specified mini-batch size from the shuffled dataset.
3. computing the gradient: computing the gradient of the loss function for the samples in the mini-batch. For example, in the case of a neural network, this would involve calculating the gradient of the parameters of each layer using backpropagation.
4. updating parameters: using the calculated gradients, the parameters of the model are updated. Usually, the Gradient Descent method (Gradient Descent) or its variants (e.g. Adam, RMSprop) is used.
5. repeat until all data have been processed: the above procedure is carried out for all mini-batches. This is called one epoch.
Advantages of mini-batch learning include
- Increased computational efficiency: computing the gradient for each mini-batch is more computationally efficient than processing the entire dataset at once, and is particularly effective for large datasets.
- Improved generalisability: mini-batch learning updates parameters using randomly retrieved samples rather than statistics from the entire batch. This improves the stability and generalisability of the learning.
- Efficient use of GPUs: mini-batch processing makes maximum use of the parallel processing power of GPUs and improves the learning rate, as calculations are performed in parallel for each mini-batch.
In mini-batch learning, the choice of mini-batch size is important and generally requires the following considerations
- Large datasets: larger mini-batch sizes improve computational efficiency. However, GPU memory constraints and computational resource limitations should be taken into account.
- Small mini-batch sizes: smaller mini-batch sizes result in more frequent model updates and more stable learning. However, learning may become more sensitive to noise.
The choice of mini-batch size depends on the nature of the problem and available computational resources, with mini-batches containing tens to hundreds of samples typically being used.
Minibatch learning is an effective learning technique applied to a variety of machine learning models and is widely used in practical implementations.
Algorithms associated with mini-batch learning.
Algorithms related to mini-batch learning are widely used in machine learning and deep learning. Typical algorithms are described below.
1. gradient descent: the gradient descent method is the most basic algorithm for updating parameters to minimise the loss function. Variants include.
- Batch Gradient Descent: the gradient is calculated using all the training data and the parameters are updated.
- Stochastic Gradient Descent (SGD): computes the gradient only on one randomly selected data point and updates the parameters.
- Mini-Batch Gradient Descent (Mini-Batch Gradient Descent): calculates the gradient at randomly selected data points in a mini-batch and updates the parameters.
2. Adam: Adam (Adaptive Moment Estimation) is an algorithm that improves the efficiency of gradient descent methods by adaptively adjusting the learning rate. It is characterised by a combination of Momentum (exponential moving average of past gradients) and RMSprop (exponential moving average of the square of the gradient), which adjusts the learning rate for each parameter and converges very effectively.
3. RMSprop: RMSprop (Root Mean Square Propagation) is an improved version of AdaGrad that adjusts the learning rate. It is characterised by keeping the exponential moving average of the square of the past gradient for each parameter, adjusting the learning rate and improving the stability of learning.
4. AdaGrad: AdaGrad (Adaptive Gradient Algorithm) is an algorithm that adjusts the learning rate for each parameter. It uses the accumulated squares of past gradients to adaptively change the learning rate of each parameter, scaling the parameters and improving the learning rate.
5. Adadelta: Adadelta is an improved version of AdaGrad, which adjusts the rate of decay of the learning rate to prevent unnecessary reduction of the learning rate. Characteristics include the use of an exponential moving average of the learning rate to update parameters, relatively simple hyper-parameter setting and no learning rate adjustment required.
6. AdamW: AdamW is a type of Adam that regularises the weights. Features include additional weight decay to prevent over-learning and automatic learning rate scheduling.
Application of mini-batch learning.
Minibatch learning is widely used as an efficient learning method for large data sets and complex models. The following sections describe examples where mini-batch learning is applied.
1. deep learning: mini-batch learning plays a very important role in deep learning. The following are examples of mini-batch learning in deep learning
Image classification: convolutional neural networks (CNNs) are trained using image datasets (e.g. CIFAR-10, ImageNet, etc.) A single batch contains many images and the gradient for each image is calculated and parameters are updated.
Natural language processing (NLP): recurrent neural networks (RNNs) and transformers are trained using textual datasets (e.g. IMDB reviews, Twitter data, etc.). Multiple text sequences are included in a mini-batch and the gradient for each sequence is calculated.
2. machine learning: many machine learning methods also apply mini-batch learning. Examples of machine learning are described below.
Linear regression: linear regression models are trained using numerical data sets. A mini-batch containing several features and objective variables for them is used to update the parameters.
Support vector machines (SVMs): train SVMs for classification and regression problems; SVMs are large-margin classifiers and use mini-batch learning to deal with large data sets.
3. reinforcement learning: mini-batch learning is also used in reinforcement learning.
Deep Q networks (DQNs): used in gaming environments such as Atari, DQNs are trained by applying mini-batch learning. Episodes of gameplay are used as mini-batches to learn the value of an agent’s actions.
4. online advertising: in online advertising and recommendation systems, mini-batch learning is used to personalise advertising and content in real-time.
Click-through rate (CTR) prediction: models are trained to predict the click-through rate of advertisements using user behaviour data as mini-batches.
5. data mining: mini-batch learning is also useful in data mining to discover patterns and relationships in large data sets.
Clustering: clustering algorithms such as k-means are trained to update cluster centres using data points in mini-batches.
Mini-batch learning is widely used to efficiently handle large datasets and complex models.
Examples of mini-batch learning implementations.
The method of implementing mini-batch learning varies between different frameworks and libraries, but a general procedure is given. In this section, we describe an example using Python and PyTorch, a leading deep learning library.
1. preparing the dataset: first, prepare the dataset to be used for mini-batch learning; a custom dataset can be created using PyTorch’s torch.utils.data.Dataset class.
import torch
from torch.utils.data import Dataset, DataLoader
class CustomDataset(Dataset):
def __init__(self, data, labels):
self.data = data
self.labels = labels
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
return self.data[idx], self.labels[idx]
#Create random data and labels as an example
data = torch.randn(1000, 10) # 1000 data points, 10 features for each point
labels = torch.randint(0, 2, (1000,)) # Labels for classification tasks with two classes
dataset = CustomDataset(data, labels)2. creating a data loader: the next step is to create a data loader using the custom data set you have created. The data loader provides an interface for efficiently retrieving mini batches.
batch_size = 32
# Creating a data loader
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)3. define the model: define the model to be trained. A simple all-associative neural network is shown here as an example.
import torch.nn as nn
class NeuralNetwork(nn.Module):
def __init__(self):
super(NeuralNetwork, self).__init__()
self.fc1 = nn.Linear(10, 64)
self.relu = nn.ReLU()
self.fc2 = nn.Linear(64, 2) # Two classes of outputs
def forward(self, x):
x = self.fc1(x)
x = self.relu(x)
x = self.fc2(x)
return x
model = NeuralNetwork()4. definition of loss functions and optimisation methods: define the loss functions and optimisation methods to be used for learning. Cross-entropy loss described in “Overview of cross-entropy and related algorithms and implementation examples” and the Adam optimiser are used here.
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)5. run the learning loop: finally, define and run the learning loop. A mini-batch is retrieved from the data loader and the model is trained.
num_epochs = 10
for epoch in range(num_epochs):
for inputs, labels in dataloader:
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
print(f"Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item()}")Challenges and measures for mini-batch learning.
The following section describes the challenges and countermeasures for mini-batch learning.
1. batch size selection:
Challenges: the batch size needs to be chosen appropriately; a batch size that is too small may be susceptible to noise and make the learning unstable, while a batch size that is too large may cause memory and computational problems.
Solution: adjust the batch size according to the nature of the dataset and the model used. It can be effective to incorporate batch size selection into hyperparameter tuning using methods such as grid search.
2. tuning the learning rate:
Challenge: if an appropriate learning rate is not selected, learning will not converge or will diverge. Adjusting the learning rate is difficult when the gradient varies significantly from batch to batch.
Solution: scheduling or decaying the learning rate. For example, the learning rate can be decreased at each epoch. Alternatively, methods such as Adam or RMSprop, which automatically adjust the learning rate by an optimiser, could be used.
3. variability within mini-batches:
Challenge: samples within a mini-batch may have variability. This is particularly noticeable in unbalanced datasets or when outliers are included.
Solution: shuffle the batch to randomise the order of samples within the mini-batch. Use methods such as oversampling, undersampling and class weighting for unbalanced data.
4. convergence to local solutions:
Challenge: with non-convex loss functions, convergence to a local solution may occur.
Solution: increase the likelihood of convergence to different local solutions by learning from multiple initial values. 4. make it less likely to fall into local solutions by using momentum and adaptive learning rate adjustment.
5. memory and computational constraints:
Challenge: memory and computational complexity can be a constraint when using large datasets and complex models.
Solution: optimise memory usage by splitting the dataset and using a data loader to load data in batches. Reducing the size of the model, optimising the architecture of the model, etc. may also be used to reduce computational complexity.
Reference Information and Reference Books
For reference information, see “General Machine Learning and Data Analysis” “Small Data Learning, Combining Logic and Machine Learning, Local/Group Learning,” and “Machine Learning with Sparsity”
For Reference book “
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Deep Learning – Ian Goodfellow, Yoshua Bengio, Aaron Courville
Pattern Recognition and Machine Learning – Christopher M. Bishop
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