Gradient Flow Plot Calculator

Gradient Analysis Results

Enter gradient data, then select Analyze Gradient Flow.

Analyzed layers0

Average mean gradient0

Largest gradient0

Smallest nonzero0

Highest L2 norm0

Weakest layer—

Strongest layer—

Overall statusNot analyzed

Detected conditions

No analysis yet.

Suggested actions

Recommendations appear after analysis.

Analysis and Plot Options

Plot type

X-axis grouping

Y-axis scale

Transformation

Smoothing method

Smoothing window

Layer sorting

Chart orientation

Decimal precision

Line width

Marker size

Chart height

Legend position

Custom plot title

X-axis label

Y-axis label

Reverse layer order

Show point markers

Show connecting lines

Show gridlines

Show zero baseline

Use scientific notation

Exclude bias gradients

Exclude frozen layers

Exclude normalization layers

Diagnostic Thresholds

Vanishing threshold

Near-zero threshold

Exploding threshold

High standard deviation

Weight/bias imbalance ratio

Adjacent-layer spike ratio

Maximum safe L2 norm

Minimum expected L2 norm

Zero-gradient warning percent

Layer Gradient Data

Use scientific notation when needed. Mark frozen layers explicitly.

Import CSV/JSON

Include	Layer name	Layer type	Step	Mean absolute	Maximum absolute	L2 norm	Bias gradient	Weight gradient	Std. deviation	Parameters	Frozen	Remove

Run Comparison

Paste a second run as CSV or JSON. Matching layer names will be compared.

Primary run label

Comparison run label

Comparison metric

Interactive Gradient Flow Plot

Processed Gradient Data

Layer	Type	Step	Mean	Maximum	L1 estimate	L2	RMS	Variance	Max/mean	Bias	Weight	Std.	Status
No processed data yet.

Formula Used

Mean absolute gradient: mean(|gᵢ|)

L1 norm: Σ|gᵢ|

L2 norm: √Σ(gᵢ²)

Root mean square gradient: √mean(gᵢ²)

Z-score: (x − μ) / σ

How to Use

Enter one row for every neural network layer.
Select a plot type and transformation method.
Adjust warning thresholds for your model scale.
Choose excluded layers and optional smoothing controls.
Select Analyze Gradient Flow to update results.
Review warnings, recommendations, and processed values.
Export the chart, data, or printable PDF report.

Example Gradient Data

Layer	Type	Mean Absolute Gradient	Maximum Gradient	L2 Norm
Input Dense	Dense	0.00008	0.00031	0.0012
Hidden 1	Dense	0.00045	0.00210	0.0084
Hidden 2	Dense	0.00320	0.01800	0.0410
Output	Dense	0.02100	0.09500	0.1840

Gradient Flow Interpretation Guide

Gradient flow reveals how strongly each layer receives learning signals. Very small values may indicate vanishing gradients. Very large values may indicate unstable optimization.

Mean gradients describe typical behavior across parameters. Maximum gradients reveal sharp spikes or isolated instability. Comparing both values gives a clearer diagnosis.

Gradient norms summarize total update strength for each layer. Normalization and residual connections can improve difficult flows. Always interpret thresholds using model scale.

Frequently Asked Questions

What is a gradient flow plot?

It displays gradient magnitudes across model layers. The plot helps reveal weak, unstable, or missing training signals.

What indicates vanishing gradients?

Gradients consistently below a meaningful threshold may indicate vanishing behavior. The correct threshold depends on model scale.

What indicates exploding gradients?

Large gradient spikes or excessive norms may indicate instability. Gradient clipping and learning-rate changes may help.

Should bias gradients be included?

Bias gradients can reveal unusual imbalance. They may be excluded when focusing only on weight updates.

Why use a logarithmic scale?

A logarithmic scale makes very small and large gradients visible together. Zero values cannot appear directly.

What does L2 norm show?

L2 norm summarizes total gradient energy. It is useful for detecting unusually strong or weak layers.

Can runs be compared?

Yes. Paste a second run and select a comparison metric. Matching layer names are overlaid.

How does smoothing help?

Smoothing reduces visual noise across layers or steps. Excessive smoothing may hide important spikes.

What causes zero gradients?

Detached tensors, frozen layers, dead activations, or incorrect loss connections can produce zero gradients.

Can the data be exported?

Processed rows can be exported as CSV or JSON. The chart can be downloaded as PNG.

Does this replace framework debugging tools?

No. It supports diagnosis and reporting. Framework hooks remain necessary for collecting real training gradients.

Copyable Analysis Output

No analysis yet.