Mastering Deep Learning with Skorch: A Comprehensive Guide to CNNs, PyTorch, and Scikit-learn Integration
Introduction
Embark on a thrilling journey into the domain of Convolutional Neural Networks (CNNs) and Skorch, a revolutionary fusion of PyTorch’s deep learning prowess and the simplicity of scikit-learn. Explore how CNNs emulate human visual processing to crack the challenge of handwritten digit recognition while Skorch seamlessly integrates PyTorch into machine learning pipelines. Join us as we solve the mysteries of advanced deep learning techniques and explore the power of CNNs for real-world applications.
Overview of Convolutional Neural Networks (CNNs)
Picture yourself sifting through a stack of scribbled numbers. Accurately identifying and classifying each digit is your job; while this may seem easy for humans, it may be really difficult for machines. This is the fundamental issue in the field of artificial intelligence, that is, handwritten digit recognition.
In order to address this issue using machines, researchers have utilized Convolutional Neural Networks (CNNs), a robust category of deep learning models that draw inspiration from the complex human visual system. CNNs resemble how layers of neurons in our brains analyze visual data, identifying objects and patterns at various scales.
Convolutional layers, the brains of CNNs, search input data for unique characteristics like edges, corners, and textures. Stacking these layers allows CNNs to learn abstract representations, capturing hierarchical patterns for applications like digital number identification.
CNNs use convolutions, pooling layers, downsampling, and backpropagation to reduce spatial dimension and improve computing efficiency. They can recognize handwritten numbers with precision, often outperforming conventional algorithms. CNNs open the door to a future where robots can decode and understand handwritten numbers using deep learning, mimicking human vision’s complexities.
What is Skorch and Its Benefits?
With its extensive library and framework ecosystem, Python has emerged as the preferred language for configuring deep learning models. TensorFlow, PyTorch, and Keras are a few well-known frameworks that give programmers a set of elegant tools and APIs for effectively creating and training CNN models.
PyTorch’s success is attributed to its “define-by-run” semantics, which dynamically creates the computational graph during operations, enabling more efficient debugging, model customization, and faster prototyping.
Skorch connects PyTorch and scikit-learn, allowing developers to use PyTorch’s deep learning capabilities while using the user-friendly scikit-learn API. This allows developers to integrate deep learning models into their existing machine learning pipelines.
Skorch is a wrapper that integrates with scikit-learn, allowing developers to use PyTorch’s neural network modules for training, validating, and making predictions. It supports features like grid search, cross-validation, and model persistence, allowing developers to maximize their existing knowledge and workflows. Skorch is easy to use and adaptable, allowing developers to use PyTorch’s deep learning capabilities without extensive training. This combination offers opportunities to create advanced CNN models and implement them in practical scenarios.
How to Work with Skorch?
Let us now go through some steps on how to install Skorch and build a CNN Model:
Step 1: Installing Skorch
We will use the pip command to install the Skorch library. It is required only once.
The basic command to install a package using pip is:
“`python
pip install skorch
“`
Alternatively, use the following command inside Jupyter Notebook/Colab:
“`python
!pip install skorch
“`
Step 2: Building a CNN model
Feel free to use the source code available here.
The very first step in coding is to import the necessary libraries. We will require NumPy, Scikit-learn for dataset handling and preprocessing, PyTorch for building and training neural networks, torchvision for performing image transformations as we are dealing with image data, and Skorch, of course, for integration of Pytorch with Scikit-learn.
“`python
print(‘Importing Libraries… ‘,end=”)
import numpy as np
from sklearn.datasets import fetch_openml
from sklearn.model_selection import train_test_split
from skorch import NeuralNetClassifier
from skorch.callbacks import EarlyStopping
from skorch.dataset import Dataset
import torch
from torch import nn
import torch.nn.functional as F
import matplotlib.pyplot as plt
import random
print(‘Done’)
“`
Step 3: Understanding the Data
The dataset we chose is called the USPS digit dataset. It is a collection of 9,298 grayscale samples. These samples are automatically scanned from envelopes by the U.S. Postal Service. Each sample is a 16×16 pixel image.
Next, we will perform standard data preprocessing followed by standardization. Next, we will split the dataset in the ratio of 70:30 for training and testing, respectively.
“`python
# Preprocessing
X = X / 16.0 # Scale the input to [0, 1] range
X = X.values.reshape(-1, 1, 16, 16).astype(np.float32) # Reshape for CNN input
y = y.astype(‘int’)-1
# Split train-test data in 70:30
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=11)
“`
Defining CNN Architecture Using PyTorch
Our CNN model consists of three convolution blocks and two fully connected layers. The convolutional layers are stacked to extract the features hierarchically, whereas the fully connected layers, sometimes called dense layers, are used to perform the classification task.
“`python
# Define CNN model
class DigitClassifier(nn.Module):
def __init__(self):
super(DigitClassifier, self).__init__()
self.conv1 = nn.Conv2d(1, 32, kernel_size=3, padding=1)
self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
self.fc1 = nn.Linear(128 * 4 * 4, 256)
self.dropout = nn.Dropout(0.2)
self.fc2 = nn.Linear(256, 10)
def forward(self, x):
x = F.relu(self.conv1(x))
x = F.max_pool2d(x, 2)
x = F.relu(self.conv2(x))
x = F.max_pool2d(x, 2)
x = F.relu(self.conv3(x))
x = x.view(-1, 128 * 4 * 4)
x = F.relu(self.fc1(x))
x = self.dropout(x)
x = self.fc2(x)
return x
“`
Using Skorch to Encapsulate CNN Model
Now comes the central part: how to wrap the PyTorch model in Skorch for Scikit-learn style training.
For this purpose, let us define the hyperparameters as:
“`python
# Hyperparameters
max_epochs = 25
lr = 0.001
batch_size = 32
patience = 5
device=”cuda” if torch.cuda.is_available() else ‘cpu’
“`
Next, this code creates a wrapper around a neural network model called DigitClassifier using Skorch. The wrapped model is configured with settings such as the maximum number of training epochs, learning rate, batch size for training and validation data, loss function, optimizer, early stopping callback, and the device to run the computations, that is, CPU or GPU.
“`python
# Wrap the model in Skorch NeuralNetClassifier
digit_classifier = NeuralNetClassifier(
module = DigitClassifier,
max_epochs = max_epochs,
lr = lr,
iterator_train__batch_size = batch_size,
iterator_train__shuffle = True,
iterator_valid__batch_size = batch_size,
iterator_valid__shuffle = False,
criterion = nn.CrossEntropyLoss,
optimizer = torch.optim.Adam,
callbacks = [EarlyStopping(patience=patience)],
device = device
)
“`
Conclusion
We explored Convolutional Neural Networks and Skorch reveals the powerful synergy between advanced deep learning methods and efficient Python frameworks. By leveraging CNNs for handwritten digit recognition and Skorch for seamless integration with scikit-learn, we’ve demonstrated the potential to bridge cutting-edge technology with user-friendly interfaces. This journey underscores the transformative impact of combining PyTorch’s robust capabilities with scikit-learn’s simplicity, empowering developers to implement sophisticated models with ease. As we navigate through the realms of deep learning and machine learning, the collaboration between CNNs and Skorch heralds a future where complex tasks become accessible and solutions become attainable.