Visualizing output of convolutional layer in tensorflow
I'm trying to visualize the output of a convolutional layer in tensorflow using the function tf.image_summary. I'm already using it successfully in other instances (e. g. visualizing the input image), but have some difficulties reshaping the output here correctly. I have the following conv layer: img_size = 256 x_image = tf.reshape(x, [-1,img_size, img_size,1], "sketch_image") W_conv1 = weight_var
Vehicle Color Recognition using Convolutional Neural Network
Vehicle color information is one of the important elements in ITS (Intelligent Traffic System). In this paper, we present a vehicle color recognition method using convolutional neural network (CNN). Naturally, CNN is designed to learn classification method based on shape information, but we proved that CNN can also learn classification based on color distribution. In our method, we convert the inp
畳み込みニューラルネットワーク（Convolutional Neural Network：CNN）の使い方、分かりますか？ - アイティメディア株式会社
畳み込みニューラルネットワーク（CNN：Convolutional Neural Networks）とは、一体何でしょうか。 簡単に言うとニューラルネットワークの進化における一段階で、ビジョンプロセッシング、手書き文字認識、声紋分析、ロボティクスといったアプリケーションで重要な技術になりつつあります。広範囲な組み込みシステムにて利用されてる可能性が高く、組み込みシステムの最新状況に通じていたいならば、大いに関心を持つべき事柄です。 しかし、CNNは従来の信号処理ツールとは仕組みも設計方法もかなり異なります。そのため、その応用範囲の広さとは裏腹に、当面は汎用的な技術ではなく、特定用途向けのブラックボックスまたはフレームワークとして登場し、ユーザーであるデザインチームから複雑性の大部分が隠されることになりそうです。 ここでは畳み込みニューラルネットワーク（CNN：Convolutional N
Attention-Based Convolutional Neural Network for Semantic Relation Extraction
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2526–2536, Osaka, Japan, December 11-17 2016. Attention-Based Convolutional Neural Network for Semantic Relation Extraction Yatian Shen, Xuanjing Huang Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science, Fudan University 825 Zhangheng Road, Shang
Detection of phase transition via convolutional neural network
We design a Convolutional Neural Network (CNN) which studies correlation between discretized inverse temperature and spin configuration of 2D Ising model and show that it can find a feature of the phase transition without teaching any a priori information for it. We also define a new order parameter via the CNN and show that it provides well approximated critical inverse temperature. In addition,
GitHub - dgschwend/zynqnet: Master Thesis "ZynqNet: An FPGA-Accelerated Embedded Convolutional Neural Network"
Image Understanding is becoming a vital feature in ever more applications ranging from medical diagnostics to autonomous vehicles. Many applications demand for embedded solutions that integrate into existing systems with tight real-time and power constraints. Convolutional Neural Networks (CNNs) presently achieve record-breaking accuracies in all image understanding benchmarks, but have a very hig
Convolutional Neural Network Language Models
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 1153–1162, Austin, Texas, November 1-5, 2016. c 2016 Association for Computational Linguistics Convolutional Neural Network Language Models Ngoc-Quan Pham and German Kruszewski and Gemma Boleda Center for Mind/Brain Sciences University of Trento {firstname.lastname}@unitn.it Abstract Convolutional Neural
Convolutional Neural Network AA - Shadertoy BETA
This help only covers the parts of GLSL ES that are relevant for Shadertoy. For the complete specification please have a look at GLSL ES specification Language: Version: WebGL 2.0 Arithmetic: ( ) + - ! * / % Logical/Relatonal: ~ < > <= >= == != && || Bit Operators: & ^ | << >> Comments: // /* */ Types: void bool int uint float vec2 vec3 vec4 bvec2 bvec3 bvec4 ivec2 ivec3 ivec4 uvec2 uvec3 uvec4 ma
Input data for convolutional neural network
I am trying learn deep learning and specifically using convolutional neural networks. I'd like to apply a simple network on some audio data. Now, as far as I understand CNNs are often used for image and object recognition, and therefore when using audio people often use the spectrogram (specifically mel-spectrogram) instead of the signal in the time-domain. My question is, is it better to use an i
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network
Recently, several models based on deep neural networks have achieved great success in terms of both reconstruction accuracy and computational performance for single image super-resolution. In these methods, the low resolution (LR) input image is upscaled to the high resolution (HR) space using a single filter, commonly bicubic interpolation, before reconstruction. This means that the super-resolut
AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery
Deep convolutional neural networks comprise a subclass of deep neural networks (DNN) with a constrained architecture that leverages the spatial and temporal structure of the domain they model. Convolutional networks achieve the best predictive performance in areas such as speech and image recognition by hierarchically composing simple local features into complex models. Although DNNs have been use
Deep Convolutional Neural Network Design Patterns
Recent research in the deep learning field has produced a plethora of new architectures. At the same time, a growing number of groups are applying deep learning to new applications. Some of these groups are likely to be composed of inexperienced deep learning practitioners who are baffled by the dizzying array of architecture choices and therefore opt to use an older architecture (i.e., Alexnet).
GitHub - IntelLabs/SkimCaffe: Caffe for Sparse Convolutional Neural Network
⚠️ DISCONTINUATION OF PROJECT - This project will no longer be maintained by Intel. Intel has ceased development and contributions including, but not limited to, maintenance, bug fixes, new releases, or updates, to this project. Intel no longer accepts patches to this project. If you have an ongoing need to use this project, are interested in independently developing it, or would like to maintain
Convolutional Neural Networkを使ったもう1つのスタイル変換手法 - Qiita
画像提供元: コンテンツ画像: 写真画像ぱくたそ 油彩のスタイル画像: chainer-goghのリポジトリ ペン画のスタイル画像: Alice-in-wonderland.net 概要 MRFを使ったスタイル変換では以下のような画像を生成します。 内容がコンテンツ画像に近い 局所的なスタイルが、スタイル画像の局所的にスタイルに近い Neural-styleが画像全体のスタイルをスタイル画像に近づけるのに対し、MRFでは局所的なスタイルをスタイル画像に近づけるという違いがあります。 既存の実装 Torch実装: https://github.com/chuanli11/CNNMRF アルゴリズム タイトルに"Markov Random Field (MRF, マルコフ確率場)"が入っていますが、MRFはアルゴリズムの肝ではないので説明は割愛させていただきます。 入力と出力 コンテンツ画像と
LeNet - Convolutional Neural Network in Python - PyImageSearch
In today’s blog post, we are going to implement our first Convolutional Neural Network (CNN) — LeNet — using Python and the Keras deep learning package. The LeNet architecture was first introduced by LeCun et al. in their 1998 paper, Gradient-Based Learning Applied to Document Recognition. As the name of the paper suggests, the authors’ implementation of LeNet was used primarily for OCR and charac
マルレク2016第一回「Convolutional Neural Network入門講座」 | Peatix
講演テーマ：「Convolutional Neural Network 入門講座」 講 演 ：丸山不二夫 開催日時：2016年5月13日（金） 19:00 ～ 21:00 開場：18：30 、 受付：18：30 ～ 19：30 会 場 ：ＴＩＳ株式会社 東京本社 14F 研修室 （西新宿） 受講チケット申込： 5月6日（金）12：00～5月13日（金）10:00、このページから。 講演の狙い ニューラル・ネットワーク技術の中核としてのCNN ニューラル・ネットワークに関心を持っている人が増えています。僕は、こうした意欲的な人たちに、是非、CNN(Convolutional Neural Network) を理解してもらいたいと思っています。CNNは、現在のニューラル・ネットワークの中心的な技術で、かつ、もっとも大きな成果を上げている技術の一つだからです。あのGoogleのAlphaGoも
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カジュアルトーク Vol.20 NLP×CNNの実現性は？ 〜自然言語処理の最先端はココまで来ている！〜 | AI専門ニュースメディア AINOW
Semantic Clustering and Convolutional Neural Network for Short Text Categorization · Issue #195 · arXivTimes/arXivTimes
GitHub - anantzoid/Conditional-PixelCNN-decoder: Tensorflow implementation of Gated Conditional Pixel Convolutional Neural Network
GitHub - dennybritz/cnn-text-classification-tf: Convolutional Neural Network for Text Classification in Tensorflow
