In Keras, a Vision Transformer (ViT) representation can mean a sequence of patch-token features, an aggregated image vector, a class-token feature, or an intermediate tensor from a Transformer block. Choose the output that matches what you want to inspect: these representations are related, but they are not interchangeable. Keras examples show how to examine attention maps and positional embeddings, while the Functional API provides a direct way to expose intermediate layer outputs.
What a ViT representation contains
A Vision Transformer splits an image into patches, projects each patch into a token, adds positional information, and processes the token sequence with Transformer blocks. The resulting features encode information about the image, but the form of the final representation depends on the model architecture and classification head.
In the original ViT convention, a class token can serve as an image-level representation. In the Keras image-classification example, the final patch-token outputs are normalized and flattened before classification; global average pooling is also identified as an alternative aggregation. Check the model implementation rather than assuming every ViT uses a class token or returns the same kind of feature.
KerasHub’s ViTBackbone documentation exposes settings including patch size, number of layers and heads, hidden dimension, MLP dimension, and whether a class token is used. These settings affect the tokens and features available for inspection, so align them with the checkpoint and task.
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Choose what you want to inspect
|
Inspection target |
What it represents |
Useful question |
|---|---|---|
|
Intermediate block output |
Features produced at a selected point in the Transformer |
How do features change between earlier and later blocks? |
|
Final patch-token sequence |
A feature vector for each image patch, after the final block |
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|
|
Class-token representation |
An image-level feature from an architecture that uses a class token |
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|
Pooled image vector |
An image-level feature aggregated from token features |
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|
|
Attention scores |
Weights showing how tokens attend to other tokens for a selected head and layer |
Where is attention concentrated for this input? |
|
Positional embedding |
Learned information associated with token positions |
How similar are the model’s learned representations of different positions? |
These objects answer different questions. An attention map is not a token feature, and a pooled image vector does not preserve the patch-by-patch sequence. The model’s architecture determines which tensors exist and how they relate.
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How to extract intermediate features from a Keras model
For a Functional model, create another Keras model that reuses the original inputs and returns the layer tensor or tensors you want. Keras documents this pattern for feature extraction in its Functional API guide.
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Load or build the model, then identify the layer whose output you want. For a ViT, that might be a Transformer block output or a final token tensor.
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Create a feature-extraction model using the original model’s inputs and the selected layer’s output. For example, with a Functional model named
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Preprocess the image as required by that specific model, then pass it to the feature model. The returned tensor contains the selected layer’s activation.
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Check the output shape and determine which dimensions correspond to batch, tokens, and feature channels before attempting to plot or compare values.
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Layer names and graph structure vary among models, so replace the illustrative layer name with one that exists in your model. If the model is subclassed rather than Functional, intermediate outputs may require a model-specific approach; the Functional API pattern applies when the selected tensor is available in the model graph.
What Keras’s ViT examples demonstrate
The Keras example Investigating Vision Transformer representations compares supervised ImageNet-pretrained ViTs, DeiT, and self-supervised DINO. It explores attention-map overlays and similarities among learned positional embeddings. The example describes visualization of attention maps over input images as “A simple yet useful way to probe into the representation of a Vision Transformer.”
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“Vision Transformer” can refer broadly to computer-vision models built with Transformer blocks; it does not always mean the original ViT design. The models in the example differ in family and pretraining, so its visualizations should be understood in the context of those specific models and inputs.
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Attention maps
An attention-map overlay can show where attention weights are concentrated for a selected input, layer, and head. The Keras probing example uses DINO for its attention-map demonstration. Treat the overlay as a probe into attention behavior, not as a complete causal explanation of a prediction: it does not, on its own, establish why the model produced a particular output.
Feature activations
Intermediate activations show values produced by a chosen layer. To compare them meaningfully, make clear which block and tensor are being viewed, and distinguish a patch-token sequence from any pooled or class-token output. The activation is evidence about the selected representation, not a complete account of the model’s decision.
Positional-embedding similarities
Comparing positional embeddings can reveal similarity patterns among the model’s learned position vectors. This is a different inspection target from attention or image-conditioned feature activations: positional embeddings concern position information, while attention maps concern token interactions for a particular input.
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Compare representations on consistent terms
When comparing models or layers, hold constant the factors that can change what a visualization means. Keras’s probing example uses model-specific preprocessing, so there is no single universal ViT input pipeline.
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Use the preprocessing required by each model, and record it rather than silently applying one model’s normalization to another.
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Use the same image and comparable layer depths when the goal is to compare model behavior.
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Keep token handling consistent: state whether the comparison uses patch tokens, a class token, or a pooled vector.
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Use comparable visualization scales and identify the attention head and layer when presenting attention maps.
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Account for patch size and token arrangement. A different patch size changes the spatial granularity of the token sequence.
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Identify the pretraining family, such as supervised ViT, DeiT, or DINO, because model family and training approach differ across the Keras example.
Check the API and preprocessing for your model
The Keras representation-probing example was last modified on 2023-11-20, and the image-classification example dates to 2021-01-18. They provide useful conceptual and methodological examples, but their dates mean you should verify current Keras or KerasHub API details and the selected model’s preprocessing requirements before adapting code. The KerasHub ViTBackbone API reference documents current backbone configuration options.
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