A Beginner‘s Guide to Codeless Deep Learning: MNIST Digit Classification
Deep learning has revolutionized the field of machine learning in recent years, achieving human-like performance on tasks like image classification, speech recognition, and language translation. However, the barriers to entry have traditionally been high, requiring coding skills and mathematical expertise.
The rise of codeless deep learning tools promises to democratize access to this powerful technology. Platforms like KNIME Analytics allow users to visually build and train complex models without writing a single line of code. In this post, we‘ll use KNIME to tackle the classic MNIST handwritten digit classification problem and introduce key deep learning concepts along the way.
The MNIST Dataset and Its Impact
The MNIST dataset has become a standard benchmark in the machine learning community since its release in 1998. It consists of 70,000 grayscale images of handwritten digits from 0 to 9, each 28×28 pixels in size, with 60,000 images for training and 10,000 for testing.
Sample images from the MNIST dataset. Source: Wikipedia
While recognizing handwritten digits may seem like a trivial task for humans, it‘s quite challenging for computers and was a major milestone in the development of deep learning algorithms. Some key milestones in MNIST accuracy:
- 1998: LeNet-5 (convolutional neural network) achieves 99.05% accuracy (LeCun et al.)
- 2012: Multi-column deep neural networks achieve 99.77% accuracy (Ciregan et al.)
- 2013: Regularization of neural networks using DropConnect achieves 99.79% accuracy (Wan et al.)
Today, state-of-the-art models exceed 99.8% accuracy on MNIST, surpassing human-level performance and approaching the theoretical upper bound. While MNIST is no longer a challenging problem for modern techniques, it remains an important didactic dataset for teaching core deep learning concepts and a useful testbed for new architectures and tools.
Accurate digit recognition also has real-world applications in areas like postal mail sorting, bank check processing, and data entry automation. The techniques developed on MNIST have paved the way for more complex applications like facial recognition, self-driving cars, and medical image analysis.
Images as Numerical Tensors
At the core of any computer vision task is the representation of images as numerical tensors. A grayscale image like those in MNIST can be thought of as a 2D grid of pixel intensity values between 0 (black) and 255 (white). The 28×28 MNIST images are therefore represented as 28×28 matrices:
A 28×28 grayscale image represented as a matrix of pixel intensities. Source: 3Blue1Brown
In deep learning, we typically work with multi-dimensional arrays called tensors, which generalize vectors and matrices to higher dimensions. A color image, for example, has three dimensions – height, width, and color channels (red, green, blue):
A color image represented as a 3D tensor with height, width, and color channel dimensions. Source: PetaPixel
When training a model, we often stack multiple image tensors into a single higher-dimensional tensor, such as a 4D tensor of shape (batch size, height, width, color channels). This allows for efficient parallel processing of multiple images on modern hardware like GPUs. We‘ll see this tensor shape referenced in the KNIME nodes later.
Convolutional Neural Networks
Convolutional neural networks (CNNs) are the workhorse of modern computer vision, powering applications from facial recognition to autonomous driving. CNNs are specially-designed to process the grid-like structure of images, learning hierarchical features that allow them to effectively distinguish between classes.
A CNN consists of three main types of layers stacked together:
- Convolutional layers: These layers perform the core operation of a CNN – the convolution. A convolution slides a small learnable filter over the input image, computing dot products at each location. This produces a feature map highlighting areas where the filter pattern is present. CNNs learn many such filters to detect various features like edges, textures, and shapes. Stacking convolutional layers allows the network to learn increasingly abstract and complex features.
Animation of a convolution operation sliding a 3×3 filter over an input image to produce a feature map. Source: Udacity
- Pooling layers: Pooling layers downsample the feature maps by computing aggregate statistics over sliding windows. The most common pooling operation is max pooling, which outputs the maximum value in each window. This helps the network become invariant to small translations and reduces computation in later layers.
Illustration of max pooling downsampling a feature map by taking the maximum value in each 2×2 window. Source: Stanford CS231n
- Dense layers: After a series of convolutional and pooling layers, the final feature maps are flattened into a 1D vector and passed through one or more dense (fully-connected) layers. Dense layers learn weighted combinations of the features to produce class scores. The final layer typically has one unit per class with softmax activation to output a probability distribution over the classes.
Example architecture of a CNN with convolutional, pooling, and dense layers. Source: Mathworks
During training, the model weights are iteratively updated via backpropagation to minimize a loss function quantifying the difference between predicted and true class labels. With enough training data and compute power, CNNs can learn rich feature hierarchies that generalize well to new images.
Building an MNIST CNN in KNIME
Now let‘s build a CNN for MNIST digit classification in KNIME Analytics Platform, a popular codeless data science tool. KNIME provides a graphical workflow editor where nodes representing data processing steps can be visually connected to create sophisticated pipelines.
We‘ll build a simple CNN with two convolutional layers, two max pooling layers, and two dense layers:
KNIME workflow for MNIST CNN. Source: Author
Here‘s a step-by-step walkthrough:
-
Create a new KNIME workflow and add a Container Input node to read in the MNIST images and labels from the built-in dataset. Configure the node to output grayscale images.
-
Add a Partitioning node to split the dataset into training and test sets. Connect it to the Container Input node and configure it for a 60,000/10,000 split.
-
Add a Keras Network node to define the CNN architecture. Connect the training data to its input. Double-click to open the layer editor.
-
Add a Keras Input Layer node and set the shape to [28, 28, 1] for the height, width, and grayscale channel.
-
Chain a Keras Convolution 2D Layer with 32 3×3 filters and ReLU activation. Follow it with a Keras Max Pooling 2D Layer with 2×2 pool size and stride.
-
Repeat step 5 to add a second convolutional and pooling layer.
-
Flatten the 2D feature maps with a Keras Flatten Layer, then pass to a Keras Dense Layer with 128 units and ReLU activation.
-
End with a Keras Dense Layer with 10 units and softmax activation to predict the digit class probabilities.
-
Close the Keras Network node and add a Keras Network Learner node. Connect the network and training data. Set 12 epochs, categorical cross-entropy loss, and the Adam optimizer.
-
Feed the trained model and test data to a Keras Network Predictor node. Evaluate accuracy with a Scorer node comparing predictions to true labels.
On the 10,000 MNIST test images, this simple CNN achieves 98.6% accuracy after just 12 training epochs, demonstrating the power of deep learning for computer vision tasks. With further architecture and hyperparameter tuning, it‘s possible to push MNIST accuracy above 99.5% in KNIME.
Comparison of Codeless Deep Learning Tools
KNIME is just one of many codeless deep learning tools aiming to lower the barriers to powerful AI. Here‘s a quick comparison of some popular options:
| Tool | Ease of Use | Flexibility | Performance | License |
|---|---|---|---|---|
| KNIME Analytics | High | High | High | Open-source |
| Weka | Medium | Medium | Medium | Open-source |
| Microsoft Azure ML | High | Medium | High | Commercial |
| Apple Create ML | High | Low | High | Commercial |
| Google AutoML | Medium | Low | High | Commercial |
Comparison of popular codeless deep learning tools. Ratings based on author‘s experience and public benchmarks.
Each tool has its strengths and weaknesses. KNIME stands out for its extensive library of data processing and machine learning nodes, making it well-suited for end-to-end workflows. Azure ML and Apple Create ML offer tight integration with their respective cloud ecosystems. AutoML is known for its neural architecture search capabilities.
Ultimately, the choice of tool depends on factors like use case, existing skills and infrastructure, and budget. The key is that all of these tools make the power of deep learning accessible to a wider audience beyond just coding experts.
Societal Impacts and Considerations
As codeless AI tools proliferate, it‘s important to consider their potential societal impacts. On one hand, lowering the technical barriers can democratize access to machine learning and accelerate innovation across industries. Domain experts in fields like healthcare, agriculture, and education can more easily translate their knowledge into intelligent systems.
On the other hand, the increased accessibility also raises concerns about potential misuse and unintended consequences. Biased or malicious actors could more quickly deploy models at scale without understanding their limitations and failure modes. The abstraction of the underlying algorithms could lead to overreliance on "black box" systems in high-stakes domains.
It‘s crucial that the development of codeless AI be accompanied by efforts to promote responsible and ethical use. This includes:
- Emphasizing interpretability and transparency in models
- Rigorously testing for fairness and robustness
- Providing education on AI limitations and best practices
- Encouraging interdisciplinary collaboration and oversight
By proactively addressing these considerations, we can work towards a future where the power of AI benefits society broadly and equitably.
Conclusion
This post has hopefully given you a taste of the exciting world of codeless deep learning. With user-friendly tools like KNIME Analytics Platform, anyone can get hands-on experience building powerful models without writing complex code.
The MNIST handwritten digit dataset served as a great example to introduce core concepts like image representation, convolutional neural networks, and model evaluation. The same techniques can be readily applied to other computer vision tasks with a bit of modification.
As you continue your deep learning journey, remember that there‘s always more to learn. Keep experimenting with different architectures, tuning hyperparameters, and testing on new datasets. Supplement your hands-on experience with theory from courses, books, and research papers.
Most importantly, think critically about the implications and limitations of the AI systems you build. Let‘s work together to create a future where codeless AI empowers people from all walks of life to solve meaningful problems responsibly.
Further Reading
- Deep Learning with Python by Francois Chollet
- Neural Networks and Deep Learning by Michael Nielsen
- Machine Learning Yearning by Andrew Ng
- KNIME Courses from KNIME.com
- Convolutional Neural Networks for Visual Recognition from Stanford CS231n
