Quick overview
Quick overview This workflow exposes two webhooks to train and run inference for a 3-hidden-layer neural network built entirely with n8n Code and Merge nodes, persisting learned weights in workflow static data and returning class probabilities, predictions, and evaluation metrics. How it works Receives a POST request on /train with epochs, learning rate, and an optional dataset to train the network. Runs a vectorized forward and backpropagation pass in JavaScript, updates weights and biases with gradient descent, and stores them in n8n workflow static data. Receives a POST request on /predict containing one or more 8‑dimensional feature vectors (and optional trueClass labels). Computes a feedforward pass through three ReLU hidden layers (9 neurons per layer) using the stored weights (or deterministic fallback weights if none are trained). Produces 4 output logits, applies a temperature-scaled softmax to return probabilities, predicted class, and confidence. Calculates per-sample cross-entropy loss and batch accuracy using trueClass (when provided) and responds with the prediction results as JSON. Setup Activate the workflow and copy the production webhook URLs for POST /train and POST /predict. Send training data to /train (or rely on the default dataset) to initialize and persist weights in workflow static data. Send inference requests to /predict as JSON containing x: [8 numbers] (optionally add sampleId and trueClass: 1..4) and review the JSON response for probabilities, predictions, loss, and accuracy. An n8n automation workflow template by Alex Raj.
Alex Raj
web
free
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