{"library":"saliency","title":"Saliency","description":"Framework-agnostic library for computing saliency maps (e.g., integrated gradients, SmoothGrad, XRAI) for deep learning models. Current version: 0.2.1. Release cadence is low, with updates driven by research contributions.","language":"python","status":"active","last_verified":"Fri May 01","install":{"commands":["pip install saliency"],"cli":null},"imports":["from saliency import core","from saliency.core import IntegratedGradients","from saliency.core import SmoothGrad","from saliency.xrai import XRAI","from saliency.core import visualize"],"auth":{"required":false,"env_vars":[]},"quickstart":{"code":"import tensorflow as tf\nfrom saliency.core import IntegratedGradients, visualize\n\n# Build a simple model\nmodel = tf.keras.Sequential([\n    tf.keras.layers.Dense(10, activation='relu', input_shape=(4,)),\n    tf.keras.layers.Dense(1, activation='sigmoid')\n])\n\n# Dummy input and baseline\nx_input = tf.constant([[1.0, 2.0, 3.0, 4.0]])\nbaseline = tf.zeros_like(x_input)\n\n# Call model wrapper\ndef model_fn(x):\n    return model(x)\n\n# Compute integrated gradients\nig = IntegratedGradients()\nattributions = ig.GetMask(x_input, model_fn, baseline, x_steps=25)\nprint(attributions)","lang":"python","description":"Compute integrated gradients on a simple Keras model.","tag":null,"tag_description":null,"last_tested":null,"results":[]},"compatibility":null}