Captum · Model Interpretability for PyTorch
import numpy as np import torch import torch.nn as nn from captum.attr import IntegratedGradients class ToyModel(nn.Module): def __init__(self): super().__init__() self.lin1 = nn.Linear(3, 3) self.relu = nn.ReLU() self.lin2 = nn.Linear(3, 2) # initialize weights and biases self.lin1.weight = nn.Parameter(torch.arange(-4.0, 5.0).view(3, 3)) self.lin1.bias = nn.Parameter(torch.zeros(1,3)) self.lin2.
Home - Keras-vis Documentation
Home Keras Visualization Toolkit Quick links Getting Started Installation Visualizations Citation Visualizations Activation Maximization Saliency Maps Class Activation Maps API Docs Visualization Optimization losses regularizers optimizer callbacks Modifiers input_modifiers grad_modifiers backprop_modifiers Utils utils Backend Specific backend Keras Visualization Toolkit keras-vis is a high-level
『繋ãŒã‚Šã€ã‚’見る：　Cytoscapeã¨å‘¨è¾ºãƒ„ールを使ã£ãŸã‚°ãƒ©ãƒ•ãƒ‡ãƒ¼ã‚¿å¯è¦–化入門
『繋ãŒã‚Šã€ã‚’見る：　Cytoscapeã¨å‘¨è¾ºãƒ„ールを使ã£ãŸã‚°ãƒ©ãƒ•ãƒ‡ãƒ¼ã‚¿å¯è¦–化入門AI-enhanced description The document discusses Cytoscape, a software environment for visualizing biomolecular interaction networks, highlighting its capabilities, tools, and models like the Watts-Strogatz and Barabási-Albert for network generation. It contains technical details about network visualization and examples of node interactions along with a sample
qcv
QCV is a Qt-based computer vision framework library that provides an easy to use interface to display, analyze and run computer vision algorithms. The library is provided with practical examples to show what you can do with just a few lines of code. OpenCV is used as the supporting computer vision library. QCV provides interfaces for C and C++. The C interface is a set of simple function calls to
高次元データã®æ¬¡å…ƒå‰Šæ¸›ãŠã‚ˆã³2次元プãƒãƒƒãƒˆæ‰‹æ³• - Qiita
ã¯ã˜ã‚㫠本記事ã¯Python2.7, numpy 1.11, scipy 0.17, scikit-learn 0.18, matplotlib 1.5, seaborn 0.7, pandas 0.17を使用ã—ã¦ã„ã¾ã™ï¼Ž jupyter notebook上ã§å‹•ä½œç¢ºèªæ¸ˆã¿ã§ã™ï¼Ž(%matplotlib inlineã¯é©å½“ã«ä¿®æ£ã—ã¦ãã ã•ã„) Sklearnã®Manifold learningã®è¨˜äº‹ã‚’å‚考ã«ã—ã¦ã„ã¾ã™ï¼Ž 多様体å¦ç¿’ã¨è¨€ã‚れる手法ã«ã¤ã„ã¦ï¼Œsklearnã®digitsサンプルを用ã„ã¦èª¬æ˜Žã—ã¾ã™ï¼Ž 特ã«t-SNEã¯Kaggleãªã©ã§ã‚‚ãŸã¾ã«ä½¿ç”¨ã•ã‚Œã¦ã„る，多次元データã®å¯è¦–化ã«é©ã—ãŸæ‰‹æ³•ã§ã™ï¼Ž ã¾ãŸå¯è¦–化ã ã‘ã§ãªã，元ã®ãƒ‡ãƒ¼ã‚¿ã¨åœ§ç¸®ã•ã‚ŒãŸãƒ‡ãƒ¼ã‚¿ã‚’çµåˆã™ã‚‹ã“ã¨ã§ï¼Œå˜ç´”ãªåˆ†é¡žå•é¡Œã®ç²¾åº¦ã‚’å‘上ã™ã‚‹ã“ã¨ãŒã§ãã¾ã™ï¼Ž 目次 データã®ç”Ÿæˆ ç·šå½¢è¦ç´ ã«æ³¨ç›®ã—ãŸæ¬¡å…ƒå‰Šæ¸› Random Proj
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ニテンイãƒãƒªãƒ¥ã‚¦ : cinematrics
ãƒãƒ³ãƒ‰ãƒ³ã® the Royal Academy of Artsã®å¦ç”Ÿã€Frederic Brodbeckæ°ãŒå’æ¥åˆ¶ä½œã¨ã—ã¦ä½œã‚Šä¸Šã’ãŸã€Œcinematricsã€ã¨ã„ã†ä½œå“ãŒé¢ç™½ã„。 ã“ã®ä½œå“ã¯ã€Pythonã®ãƒ—ãƒã‚°ãƒ©ãƒ を使ã£ã¦æ˜ ç”»ã®è¦ç´ ï¼ˆæ˜ åƒã€éŸ³æ¥½ã€å—幕ãªã©ï¼‰ã‚’切り出ã—ã€å„シーンã”ã¨ã«ç´°ã„細切れã«ã—ã¦ç’°çŠ¶ã«æ˜ 画をå†å®šç¾©ã™ã‚‹ã¨ã„ã†ã‚‚ã®ã€‚ 実際ã«ä½œå“を見ã¦ã„ãŸã ã„ãŸã»ã†ãŒåˆ†ã‹ã‚Šã‚„ã™ã„ã¨æ€ã†ã®ã§ã€ä»¥ä¸‹å¾¡è¦§ãã ã•ã„ éžå¸¸ã«æ˜ åƒè¡¨ç¾ã¨ã—ã¦ã‚‚ã€ã‚¤ãƒ³ã‚¿ãƒ©ã‚¯ãƒ†ã‚£ãƒ–ãªè¡¨ç¾ã¨ã—ã¦ã‚‚é¢ç™½ã„作å“ã¨ãªã£ã¦ã„ã¾ã™ã€‚
Data Structure Visualization
Currently, we have visualizations for the following data structures and algorithms: Basics Stack: Array Implementation Stack: Linked List Implementation Queues: Array Implementation Queues: Linked List Implementation Lists: Array Implementation (available in java version) Lists: Linked List Implementation (available in java version) Recursion Factorial Reversing a String N-Queens Problem Indexing
Gephi - The Open Graph Viz Platform
Too complicated? Make a deal with Gephisto the one-click Gephi. Powerful Network Analysis Discover structural patterns in your connected data. From social networks to biological systems, Gephi handles complex networks of ten to ten million nodes with advanced algorithms and metrics. Algorithms Built-in algorithms for community detection, centrality measures, and clustering analysis. Data Manipulat
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GitHub - utkuozbulak/pytorch-cnn-visualizations: Pytorch implementation of convolutional neural network visualization techniques
GitHub - ContextLab/hypertools: A Python toolbox for gaining geometric insights into high-dimensional data
GitHub - zhoubolei/cnnvisualizer: Visualizer for Deep Neural Networks
Quick Start — Netscope
Brown University
GitHub - gwding/draw_convnet
å¯è¦–化 - Keras Documentation
VITA : Visualization system for Interaction with Transmitted Audio signals
CINEMETRICS
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