```html Visual Debugger - ε Platform

ε

Visual Debugger for ε

Gain real-time insights into your machine learning model workflows with interactive visualization tools.

View Features Get Started

Key Features

Layer Visualization

Explore model architectures with interactive layer-by-layer breakdowns.

Training Dashboard

Track loss curves, metrics, and parameter distributions in real-time.

Error Analysis

Identify prediction errors with confusion matrices and saliency visualizations.

Why Use Visual Debugger?

Debug Faster

Visualize training progress and model parameters without writing additional code.

Improve Models

Understand which features contribute most to predictions with gradient and activation heatmaps.

Explainable AI

Generate SHAP values, LIME explanations, and feature contribution reports.

Collaborate Better

Share interactive reports to analyze model behavior with your team members.

How It Works

1

Attach Debugger

Add a single import to your training script to enable logging. 

from epsilon import debug
2

Run Training

Launch your training session—automatically logs layer activations and metrics. 

epsilon.debug.start(export="debugger.db", format="visualizer")
3

Analyze Results

Open the visualization tool to explore your logs. 

epsilon visualizer open ./debugger.db
Debugger Screenshot

Quick Example 


from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

# Basic MNIST classifier
model = Sequential()
model.add(Dense(128, input_dim=784, activation='relu'))
model.add(Dense(10, activation='softmax'))

# Add debugging hooks
epsilon.debug.attach(model)

model.compile(loss='categorical_crossentropy', optimizer='adam')
epsilon.debug.start(log_dir='debugger')

Start Debugging Today

Understand your models with ε's most powerful visualization platform yet.

View Documentation
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