mission-control-keras-finetuninglisted

# Mission Control Keras Fine-Tuning ## Purpose Use Mission Control to plan or execute Keras fine-tuning so transfer-learning changes preserve baseline comparisons and evaluation evidence. The Codex chat agent is not the Mission Control Manager. It is the bridge between the user and the Mission Control Manager. ## Use when - The user wants TensorFlow Hub or pretrained-model fine-tuning. - The repo already has a Keras model but the training strategy needs work. - Baseline versus tuned behavior needs to stay explicit. ## Workflow 1. Establish the current baseline model and metrics. 2. Ask Mission Control to plan the freeze, unfreeze, and evaluation loop. 3. Capture before/after evidence instead of declaring the tuned model better by instinct. 4. Keep export compatibility visible if the tuned model must ship. ## Mission Control calls Tools: - `mission_control_start_task` - `mission_control_get_status` - `mission_control_get_handoff_summary` Resources: - `mission-control://projects/{project_id}/validation-summary` - `mission-control://projects/{project_id}/handoff` ## Never do - Do not fine-tune blindly without a baseline. - Do not confuse a transfer-learning experiment with a product-ready model. ## Example invocation `Use Mission Control to fine-tune the Keras model and compare it against the current baseline.`