RNN – Recurrent Neural Network (RNN) – LSTM GRU BRNN
Tensorflow における RNN – 実践ガイドと文書化されていない特徴 * RNNs in Tensorflow, a Practical Guide and Undocumented Features の簡単な要約です。 * 図表は原文を参照してください。 データ前処理: tf.SequenceExample を使用する RNN は複数の時間ステップにおける入力 and/or 出力を持つシーケンシャル・データのために使用されます。TensorFlow にはそのようなデータを扱うための protocol buffer 定義があります: tf.SequenceExample Python/Numpy 配列からデータを直接ロードできますが、代わりに tf.SequenceExample を使用することが多分得策でしょう。このデータ構造は non-sequential な特徴のための
Tensorflow RNN weight matrices initialization
How to initialize weight matrices for RNN? I believe people are using random normal initialization for weight matrices for RNN. Check out the example in TensorFlow GitHub Repo. As the notebook is a bit long, they have a simple LSTM model where they use tf.truncated_normal to initialize weights and tf.zeros to initialize biases (although I have tried using tf.ones to initialize biases before, seem
Recurrent Neural Networkの基礎（Peterさんのチュートリアルの翻訳） - Qiita
はじめに RNNについての分かりやすいチュートリアルとして、TensorFlowのRNNを基本的なモデルで試すでも触れられていた、Peter's notesのHow to implement a recurrent neural network Part1を翻訳しました。 Tutorial 翻訳 翻訳するにあたり、日本語にすべきか、英単語のままにすべきか悩むところが多かったです。 訳の誤りや分かりづらいところございましたら、アドバイス頂けると嬉しいです。 (なお、Peterさんには翻訳・アップロードの許諾を頂いてます。) チュートリアルの内容 簡単なRNNをPythonで実装していきます。 入力データとして、10回にわたって、0または1の数字を取得し、取得した数字を記憶しながら、最終的にその合計値を返す、という簡単なモデルをRNNで実装していきます。 RNNの基礎 BPTT法 勾配爆発、勾
Recurrent Neural Network Grammars
We introduce recurrent neural network grammars, probabilistic models of sentences with explicit phrase structure. We explain efficient inference procedures that allow application to both parsing and language modeling. Experiments show that they provide better parsing in English than any single previously published supervised generative model and better language modeling than state-of-the-art seque
ChainerによるRecurrent Neural Network(RNN)のお勉強 〜ExcelとRの乱数の精度検証〜 - Qiita
# -*- coding: utf-8 -*- from chainer import FunctionSet, Variable, optimizers,serializers from chainer import functions as F from chainer import links as L from sklearn import base from sklearn.cross_validation import train_test_split from abc import ABCMeta, abstractmethod import numpy as np import six import math import cPickle as pickle class BaseChainerEstimator(base.BaseEstimator): __metaclas
Recurrent Neural Network - Laksh Gupta
sequence to sequence learning, part-of-speech tagging, backpropagation-Through-Time Posted on March 5, 2016 Tweet Recurrent Neural Network¶Let us now move on to Recurrent Neural Network (RNN). Recurrent neural network is good in handling sequential data because they have a memory component which enables this network to remember past (few) information making it better for a model requiring varying
How to use the Embedding Layer for Recurrent Neural Network (RNN) in Keras
I'm rather new to Neural Networks and the Keras Library and I'm wondering how I can use the Embedding Layer as described here to mask my input data from a 2D tensor to a 3D tensor for a RNN. Say my timeseries data looking as follows (with an increasing time): X_train = [ [1.0,2.0,3.0,4.0], [2.0,5.0,6.0,7.0], [3.0,8.0,9.0,10.0], [4.0,11.0,12.0,13.0], ... ] # with a length of 1000 Now, say I would w
Storytelling of Photo Stream with Bidirectional Multi-thread Recurrent Neural Network
Visual storytelling aims to generate human-level narrative language (i.e., a natural paragraph with multiple sentences) from a photo streams. A typical photo story consists of a global timeline with multi-thread local storylines, where each storyline occurs in one different scene. Such complex structure leads to large content gaps at scene transitions between consecutive photos. Most existing imag
Recurrent Neural Network for Text Classification with Multi-Task Learning
Neural network based methods have obtained great progress on a variety of natural language processing tasks. However, in most previous works, the models are learned based on single-task supervised objectives, which often suffer from insufficient training data. In this paper, we use the multi-task learning framework to jointly learn across multiple related tasks. Based on recurrent neural network,
Deep Gate Recurrent Neural Network
This paper introduces two recurrent neural network structures called Simple Gated Unit (SGU) and Deep Simple Gated Unit (DSGU), which are general structures for learning long term dependencies. Compared to traditional Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), both structures require fewer parameters and less computation time in sequence classification tasks. Unlike GRU and LSTM
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GitHub - phreeza/tensorflow-vrnn: A variational recurrent neural network implementation in tensorflow
Hybrid Recurrent Neural Network for Nonlinear Hybrid Dynamical Systems Identification
GitHub - IntelLabs/rnnlm: Recurrent Neural Network Language Modeling (RNNLM) Toolkit
Recurrent Neural Networkとは何か、他のニューラルネットワークと何が違うのか | AI専門ニュースメディア AINOW
Generating Sequences With Recurrent Neural Network
ReSeg: A Recurrent Neural Network-based Model for Semantic Segmentation
Recurrent Convolutional Neural Network Regression for Continuous Pain Intensity Estimation in Video
Feed Forward Pre-training for Recurrent Neural Network Language Models
A Latent Variable Recurrent Neural Network for Discourse Relation Language Models
GitHub - icoxfog417/rnn_demo: Recurrent Neural Network Demo by PyBrain
