NeuralBuilder is a user-friendly Python library designed to simplify the process of creating, training, and evaluating deep learning models for classification and regression tasks. It provides an intuitive and high-level abstraction over TensorFlow, making it easier for beginners to work with deep learning models.
-
Simplified Model Creation: NeuralBuilder offers an easy-to-use interface for defining the architecture of deep learning models. With just a few lines of code, you can specify the number of layers, layer types, and units to create your desired model architecture.
-
Versatile Model Architectures: The library supports various model architectures, including sequential models, LSTM models, and convolutional models. You can choose the architecture that best suits your task requirements.
-
Flexibility and Customization: NeuralBuilder allows for flexible customization of the model architecture. You have the freedom to choose different layer types, such as dense layers, dropout layers, LSTM layers, and convolutional layers, and customize the number of units in each layer.
-
Easy Training Process: Training your deep learning model is a breeze with NeuralBuilder. Once you have defined the architecture, you can use the built-in training functions to train the model on your training dataset. The library handles the complexity of compiling the model, choosing the appropriate loss function, and optimizing the model parameters.
-
Comprehensive Evaluation: NeuralBuilder provides evaluation functions to assess the performance of your trained models. Whether you are working on classification or regression tasks, you can easily evaluate the accuracy, precision, recall, F1 score, and visualize the results using scatter plots.
-
Visualize Training Progress: Monitoring the progress of your model during training is crucial. NeuralBuilder includes plotting functions that allow you to visualize the accuracy and loss values over epochs. This helps you gain insights into the model's performance and identify potential issues.
You can install NeuralBuilder using pip:
pip install neuralbuilderTo create, train, and evaluate a deep learning model using NeuralBuilder, follow these steps:
-
Import the necessary modules and functions from NeuralBuilder.
-
Define the architecture of your model by specifying the number of layers, layer types, and units.
-
Train your model using your training dataset.
-
Evaluate the model's performance using the evaluation functions provided by NeuralBuilder.
-
Visualize the accuracy and loss values during training to gain insights into the model's progress.
-
For detailed usage examples and instructions, please refer to the examples directory in the NeuralBuilder repository.
We welcome contributions from the community! If you encounter any issues or have suggestions for improvement, please don't hesitate to open an issue or submit a pull request on GitHub. We appreciate your feedback and collaboration.
NeuralBuilder is released under the MIT License. You can find more information in the LICENSE file.
We hope that NeuralBuilder simplifies your deep learning journey and enables you to focus on building powerful models without getting overwhelmed by the complexities of TensorFlow. Happy coding!