Ayesha188/Tensorflow_Playground

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

TensorFlow App

This app allows users to configure a TensorFlow model with various parameters, providing insights into how each one influences the model's performance. Below are the key parameters you can customize:

Parameters

Learning Rate: Controls how much to change the model in response to the estimated error during weight updates; affects convergence speed.

Number of Hidden Layers: Defines the complexity of the model; more layers can capture intricate patterns but may lead to overfitting.

Activation Function: Determines the output of each neuron in the hidden layers; affects how the model learns non-linear relationships.

Batch Size: Specifies the number of training samples to process before updating the model parameters; impacts training stability and memory usage.

Regularization Rate: helps prevent overfitting by adding a penalty on the size of coefficients; improves generalization of the model.