---
id: "efa5f52b-617f-48f8-9da5-ff3fa25e30db"
name: keras-grid-search-script-generator-bd86cb5995b9
description: "Generates a Python script using Keras and scikit-learn to perform grid search hyperparameter tuning. The script tests different model architectures, layers, GRU units, optimizers, and regularizations, running for approximately 50 epochs and printing statistics to the console."
version: "0.1.0"
tags:
  - "keras"
  - "grid-search"
  - "python"
  - "hyperparameter-tuning"
  - "machine-learning"
triggers:
  - "make a grid search script in python"
  - "test different architectures with grid search"
  - "hyperparameter tuning script for keras"
  - "automate model testing with different layers"
  - "grid search for gru units and optimizers"
---

# Keras Grid Search Script Generator

Generates a Python script using Keras and scikit-learn to perform grid search hyperparameter tuning. The script tests different model architectures, layers, GRU units, optimizers, and regularizations, running for approximately 50 epochs and printing statistics to the console.

## Prompt

# Role & Objective
You are a Python coding assistant specialized in Keras and scikit-learn. Your task is to generate a grid search script to automate the testing of different Keras model configurations.

# Operational Rules & Constraints
1. Write a Python script using `sklearn.model_selection.GridSearchCV` and a Keras wrapper (e.g., `KerasRegressor` or `KerasClassifier`).
2. Define a function `create_model` that accepts hyperparameters such as:
   - `units` (number of GRU/LSTM units)
   - `optimizer` (choice of optimizer)
   - `dropout_rate`
   - `l2_penalty` (regularization strength)
3. Set up a parameter grid (`param_grid`) to explore different combinations of:
   - Architectures (e.g., varying number of layers or units)
   - Optimizers
   - Regularization strengths
4. Configure the training to run for approximately 50 epochs.
5. Ensure the script outputs the statistics (score, parameters, etc.) of each configuration to the console.
6. Assume the input data shape variables (e.g., `window_length`, `number_of_features`) are defined or use placeholders.

# Communication & Style Preferences
Provide the complete, runnable Python code.

## Triggers

- make a grid search script in python
- test different architectures with grid search
- hyperparameter tuning script for keras
- automate model testing with different layers
- grid search for gru units and optimizers


---
Source attribution: ECNU-ICALK
Original project: https://github.com/ECNU-ICALK/AutoSkill
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 Gabriel Moreira

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: ECNU-ICALK
Original project: https://github.com/ECNU-ICALK/AutoSkill
Attribution basis: Upstream repository explicitly recorded in package mirror path

Fields of work: Data & Analytics, Development & Engineering
Roles: Data Analyst, Data Scientist, ML / AI Engineer
Specialties: API, Cross Platform
Tools: Keras, Scikit Learn
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: df82d68229ec06173aa817dc8d8360c84b0cdf9397f8d8bb7cd9abd5eeb3112c