In Keras, a Vision Transformer (ViT) representation can mean a sequence of patch-level vectors, a class-token vector, a pooled image vector, an intermediate layer’s activations, attention scores, or positional embeddings. These are different objects: choose the one that matches the question, then inspect it without treating a visualization as a complete explanation of a prediction.
What does a ViT representation contain?
A ViT divides an image into patches, projects each patch into a token, adds positional information, and processes the token sequence through Transformer blocks. A token therefore carries information produced by the model from the corresponding patch and its interactions with other tokens. The exact form of the final image representation depends on the model implementation and how it aggregates tokens.
The original ViT convention can use a dedicated class token to summarize an image. The Keras image-classification example instead normalizes the final patch-token outputs and flattens them before classification; it also identifies global average pooling as an alternative. Check the architecture and classifier head rather than assuming every model uses the same representation (Keras image-classification example).
Which representation should you inspect?
| Inspection target | What it contains | Useful for |
|---|---|---|
| Intermediate block output | Token features at a selected point in the Transformer | Examining how features change across depth |
| Final patch-token sequence | A vector for each image patch after the final block | Studying spatially localized features before aggregation |
| Class-token representation | A dedicated token used by some ViT implementations to summarize the image | Inspecting the image-level vector when the architecture uses a class token |
| Pooled vector | An image-level vector formed by aggregating patch features, for example with global average pooling | Inspecting a model’s aggregated output |
| Attention scores | Weights describing attention relationships for a selected layer and head | Visualizing which tokens receive attention in that specific computation |
| Positional embedding | Learned or configured information associated with token positions | Comparing how the model encodes patch positions |
These targets are related, but not interchangeable. For example, a pooled vector no longer exposes a separate output vector for each patch, while attention weights describe token relationships rather than the same thing as token features.
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How do I extract intermediate features from a Keras model?
For a Functional Keras model, build another model that shares the original input and returns the chosen layer’s output. Keras documents this pattern for feature extraction (Keras Functional API: extract and reuse nodes in the graph of layers).
- Find the tensor you need. Identify the layer output in the model graph. Confirm that it corresponds to the intermediate block or other representation you intend to inspect.
- Create a feature-extraction model. In Python, the general Functional pattern is
keras.Model(inputs=original_model.inputs, outputs=selected_layer.output). Use the input and output tensors belonging to the model you loaded or built. - Prepare the image for that model. Apply its expected input shape and preprocessing, then call the feature model with the resulting input. Keras examples use model-specific preprocessing; there is no single universal ViT input pipeline.
- Check the output shape and token handling. Determine whether the output contains patch tokens, a class token, or an already aggregated vector before interpreting or plotting it.
The expression above illustrates the documented Functional-model approach; it is not a complete, model-independent loading or preprocessing script. For subclassed models or architectures that do not expose the desired tensor as a Functional layer output, inspect that model’s current API and implementation rather than assuming the same layer-access pattern applies.
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How can you visualize and compare ViT representations?
Attention-map overlays
An attention map can be displayed over the input image to show where weights are concentrated for a selected layer and head. The Keras example “Investigating Vision Transformer representations” demonstrates attention-map overlays with DINO. As that example puts it, “A simple yet useful way to probe into the representation of a Vision Transformer is to visualise the attention maps overlayed on the input images.” Treat the overlay as a probe of attention weights for that input and configuration, not as a standalone causal explanation of the model’s prediction.
Feature activations
Intermediate or final token features let you inspect what the model produces at particular depths. To compare visualizations meaningfully, keep the input, preprocessing, selected layer, token handling, and display scale consistent. An apparent difference may otherwise come from the setup rather than the model family or representation.
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Positional-embedding similarity
Comparing positional embeddings can reveal similarities in how positions are encoded. It answers a different question from an attention overlay or a feature-activation view: embeddings concern position information, while attention weights and token features describe other parts of the model’s computation.
How do supervised ViT, DeiT, and DINO differ in this workflow?
The Keras probing example examines supervised ImageNet-pretrained ViTs, DeiT, and self-supervised DINO. It is a comparison of specific model examples, not evidence that every model in one family has identical representations. Use the same image, preprocessing appropriate to each model, layer depth, token treatment, and visualization scale when making comparisons. Model-specific preprocessing still matters even when the goal is to compare models.
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What architecture settings affect the representation?
KerasHub’s ViTBackbone documentation exposes settings including patch size, layer and head counts, hidden dimension, MLP dimension, and whether a class token is used. These choices affect the token sequence and the representation available to inspect. Align the backbone configuration with the checkpoint and task; do not assume that a setting or token convention from one ViT transfers to another.
The number and arrangement of patch tokens depend on the input and patching configuration. When comparing early and late blocks or models with different patch sizes, record the selected layer and the spatial arrangement represented by the tokens. Otherwise, a visual difference can be mistaken for a difference in learned features when the inspected tensors do not correspond in the same way.
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Which Keras references should you consult?
- Investigating Vision Transformer representations demonstrates model probing, including attention-map overlays and positional-embedding similarity. The example was last modified 2023-11-20.
- Image classification with Vision Transformer illustrates patch-token handling and classification. The example dates to 2021-01-18.
- Keras Functional API feature extraction documents returning selected layer outputs from a Functional model.
- KerasHub ViTBackbone documents backbone configuration options.
Because the two example pages are dated, use them for the concepts and methods they demonstrate, and check the current Keras or KerasHub API and the selected model’s preprocessing requirements before adapting code.
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