Graph#

Graph view visualization for neural networks.

Overview#

The graph renderer displays neural network architectures as computational graphs, with nodes representing layers and edges showing data flow. This mode is ideal for understanding model topology and works with any Keras model type.

When to use:

  • Models with complex topology (branches, merges, skip connections)

  • Understanding computational structure is important

  • Functional or Subclassed models

  • Any model where connections between non-adjacent layers matter

  • Comparative topology analysis

When NOT to use:

  • Simple sequential models (layered, graph, or functional all work, but layered is simplest)

  • Models where tensor shape progression is important (use layered or functional)

  • Very wide models with many parallel paths (may be too dense to read)

API Reference#

visualkeras.graph.graph_view(model, to_file=None, color_map=None, node_size=50, background_fill='white', padding=10, layer_spacing=250, node_spacing=10, connector_fill='gray', connector_width=1, ellipsize_after=10, inout_as_tensor=True, show_neurons=True, styles=None, image_fit='contain', circular_crop=True, layered_groups=None, *, options=None, preset=None)[source]#

Render a Keras model as a graph-style architecture diagram.

This renderer emphasizes connectivity rather than tensor volume. It is a good fit for models with branching, merges, skip connections, or other topologies where the main question is how layers connect rather than how tensor shapes evolve from left to right.

Parameters:

  • model (Any) –

    Keras model instance to visualize.

    Graph view works across sequential, functional, and many subclassed models. It is usually the clearest choice when the architecture has multiple paths or when you need a compact topology-first diagram.

  • to_file (str, optional) –

    Path to save the rendered image. The image format is inferred from the file extension.

    The rendered PIL.Image is returned whether or not this value is supplied. Use this when you want to save the figure and continue working with the in-memory image in the same call.

  • color_map (dict, optional) –

    Mapping from layer class to broad style values such as fill and outline.

    This is the simplest way to assign consistent colors by layer type. It works well for high-level styling rules, while styles is better when you need per-layer overrides or image-based customization.

  • node_size (int, default=50) –

    Default node diameter or box size in pixels.

    Increase this for presentation-sized figures or when layer labels need more room. Smaller values produce denser diagrams and are often useful for large networks.

  • background_fill (Any, default='white') –

    Background color for the final image.

    This accepts any Pillow-compatible color value. Choose a background that keeps nodes, connectors, and annotation text easy to distinguish.

  • padding (int, default=10) –

    Outer padding around the full diagram in pixels.

    This affects the margin between the rendered graph and the image edges. Increase it when group boxes, larger nodes, or custom overlays feel too close to the boundary.

  • layer_spacing (int, default=250) –

    Horizontal spacing between layer columns.

    This is the main control for how spread out the graph feels from input to output. Larger values improve readability in complex graphs, while smaller values keep the figure compact.

  • node_spacing (int, default=10) –

    Vertical spacing between nodes within the same column.

    Use this to separate dense stacks of nodes or to compress layers when a model has many nodes per rank. It works together with node_size to define the overall vertical density of the diagram.

  • connector_fill (Any, default='gray') –

    Color used for connector lines between nodes.

    Neutral connector colors are usually easier to read because the nodes themselves already carry most of the semantic styling.

  • connector_width (int, default=1) –

    Line width used for connectors.

    Thicker connectors can improve readability in exported figures or when the graph contains long cross-column edges.

  • ellipsize_after (int, default=10) –

    Maximum number of neuron markers to draw before collapsing the remainder into ellipsis markers.

    This keeps large dense layers from dominating the diagram. Lower values trade detail for compactness, while higher values preserve more of the true node count at the cost of visual density.

  • inout_as_tensor (bool, default=True) –

    If True, represent each input or output tensor as a single node. If False, flatten tensor shapes into scalar-like node counts when possible.

    Tensor mode is usually better for topology diagrams. Expanded mode is more literal, but it can grow quickly for layers with larger shapes.

  • show_neurons (bool, default=True) –

    If True, draw individual neuron markers for supported layers. If False, represent each layer as a single node or box.

    Turning this off produces a more abstract graph and is often helpful for large models or diagrams intended for documentation rather than low-level inspection.

  • styles (mapping, optional) –

    Fine-grained style overrides keyed by layer name or layer class.

    Use this when color_map is too coarse. Graph styles can override values such as fill, outline, node_size, connector_fill, connector_width, box_scale, embedded images, and related node presentation details.

  • image_fit ({'contain', 'cover', 'fill', 'match_aspect'}, default='contain') –

    Default fit mode for images attached through styles.

    This controls how per-layer images are resized inside graph nodes. Use contain when preserving the full image is important and cover when full node coverage is more important than edge cropping.

  • circular_crop (bool, default=True) –

    If True, crop node images to a circle where supported.

    Circular crops often produce cleaner node icons in graph view. Disable this when you need the full rectangular image or when square logos read better in your figure.

  • layered_groups (sequence of dict, optional) –

    Group definitions used to draw labeled background regions behind sets of nodes.

    Groups are useful for highlighting architectural stages or conceptual blocks such as encoder, bottleneck, and decoder sections. They provide structure without altering the graph layout itself.

  • options (GraphOptions or mapping, optional) –

    Configuration bundle applied after preset and before explicit keyword arguments.

    Use this when you want to reuse a graph style across multiple models or keep application code more readable than a long keyword-argument call.

  • preset (str, optional) –

    Name of a preset from visualkeras.GRAPH_PRESETS. Graph mode currently provides default, compact, and presentation.

    Presets are intended as starting points. They can be combined with options and explicit overrides when you want a curated base style without giving up control.

Returns:

Rendered graph diagram.

Return type:

PIL.Image.Image

Notes

Configuration precedence is preset followed by options followed by explicit keyword arguments.

Full documentation: https://visualkeras.readthedocs.io/en/latest/api/graph.html

Configuration Options#

Use visualkeras.options.GraphOptions when you want to bundle graph layout, node styling, connector behavior, and related settings for reuse. Curated presets are available through visualkeras.options.GRAPH_PRESETS.

Key Parameters#

Core Parameters

  • model: Keras model instance

  • to_file: Path to save output (optional)

  • preset: Use preset configuration (‘default’, ‘compact’, ‘presentation’)

  • options: GraphOptions object for bundled configuration

Layout Control

  • layer_spacing: Space between layers (default: 250)

  • node_spacing: Space between nodes (default: 10)

  • padding: Border space (default: 10)

Styling

  • color_map: Dict mapping layer types to {'fill': '...', 'outline': '...'}

  • connector_fill: Connection line color (default: ‘gray’)

  • connector_width: Line thickness (default: 1)

  • node_size: Node size in pixels (default: 50)

  • background_fill: Background color (default: ‘white’)

  • font_color: Text color (default: ‘black’)

Content Control

  • type_ignore: Layer types to skip

  • index_ignore: Layer indices to skip

Usage Examples#

Basic Usage#

import visualkeras

image = visualkeras.show(model, mode='graph')
image.show()

With Custom Node Size#

image = visualkeras.show(
    model,
    mode='graph',
    node_size=75,
    layer_spacing=300
)

With Color Customization#

image = visualkeras.show(
    model,
    mode='graph',
    color_map={
        keras.layers.Conv2D: {'fill': '#1976d2', 'outline': '#0d47a1'},
        keras.layers.Dense: {'fill': '#388e3c', 'outline': '#1b5e20'},
    },
    preset='presentation'
)

See Also#