lstm keras

recognition, one is CNN+LSTM+CTC structure [15-16] (see Figure 11), the other is Cnn+lstm+seq2seq [17] (see Figure 12). The main body of the two methods is consistent, first using CNN to learn the relationship between neighboring pixels, and then adopt the bidirectional long-short-term Memory neural network (bidirectional long short-term memory,blstm) to learn the long-span context (the whole line of the S

this column is to help you to screen out interesting papers, to interpret the core ideas of the paper, to provide reading guidance for intensive reading. NIPS (Neural information processing systems, the Progress conference on Neural Information Processing systems) is a top-level meeting of AI and machine learning, hosted by the NIPS Foundation in December each year, which attracts machine learning, artificial intelligence, A number of international experts in the fields of statistics. The autho

The original paper: A structured self-attentive sentence embedding introduction This article presents a model that uses self-attention techniques to generate explanatory sentence vectors. Usually we use vectors to represent words, phrases, or sentence vectors, and in this article, the authors suggest that two-dimensional sentences can be used to represent sentences, and that each line of the matrix represents different parts of the sentence. The authors performed 3 different tasks on 3 different

network outage causes model weights such as Keras load Vgg16 to fail,The direct workaround is to delete the downloaded file and download it again.windows-weights Path : C:\Users\ your user name \.keras\models linux-weights Path : . keras/models/Note: Files with dots in Linux are hidden and need to be viewed hidden file to display

Setting up a deep learning machine from Scratch (software)A detailed guide-to-setting up your machine for deep learning. Includes instructions to the install drivers, tools and various deep learning frameworks. This is tested on a a-bit machine with Nvidia Titan X, running Ubuntu 14.04There is several great guides with a similar goal. Some is limited in scope, while others is not up to date. This are based on (with some portions copied verbatim from): Caffe Installation for Ubuntu R

calculate gradients and update weight coefficients; Remember to perform optimizer output. Use a predefined common loss function: Initializes using Xavier, and Tf.layer automatically sets the weighting factor (weight) and the offset (bias). C. Senior Wrapper--keras Keras can be understood as a layer at the top of the TensorFlow, which can make some work simpler (and also support Theano backend). Define

gradient (vanishing gradient problem), the proper initialization of W matrix can alleviate the effect of vanishing gradient, and a better method is to use Relu instead of Tanh or sigmoid function.Note: The vanishing gradient issue is described in detail in the 2013 article on the difficulty of training recurrent neural networks, which also speaks of an expansion gradient in the algorithm (exploding gradient Problem) problem, but can use predefined threshold clipping gradient method to solve the

and effective LSTM (Long short-term Memory)The disappearance of the gradient causes the classifier to react only to changes in the recent message, to downplay previously trained parameters, and not to use a ratio method to solve A RNN model contains two inputs, one is a past state, one is a new data, two outputs, one is a prediction, one is a future state In the middle is a simple neural network Switching the middle p

BP algorithm is the foundation and the most important part of neural network. The loss function needs to be adjusted because the gradient disappears or explodes during the reverse propagation of the error. In the lstm, through the sigmoid to achieve three doors to solve the memory problem, in the process of tensorflow implementation, the need for gradient pruning operations to prevent gradient explosion. RNN's BPTT algorithm also has such problems, so

GuideThis paper discusses the reasons why deep neural network training is difficult and how to use highway Networks to solve the problem of deep neural network training, and realizes Highway Networks on Pytorch.I. The relationship between Highway Networks and deep NetworksThe deep neural network has better effect compared with the shallow neural network, in many aspects has achieved very good results, especially in the image processing has made a great breakthrough, however, with the increase in

cases, Rnns will break the latter limit by directing information back to the hidden unit from the output unit, which is known as "projections", and the input of the hidden layer includes the state of the previous hidden layer, that is, the nodes within the hidden layer can be connected and interconnected. lstm Because in the RNN, the update parameter adopts the bpTT method, when the length of the sequence is long, the problem of the gradient disappea

). For example, we could build a 2-tier loop network like the following code: Y1 = rnn1.step (x) y = rnn2.step (y1) In other words, we have two rnn: one rnn takes the input vector and the second rnn the output of the first RNN as its input. In fact, as far as RNN itself is concerned, they don't care who is who's input: all vectors are in and out, all of which are gradient through each model in reverse propagation. A better network. What needs to be stated briefly is that in practice a slightly

), variables (Variable). lesson three TensorFlow linear regression and simple use of classifications. The fourth lesson Softmax, cross-entropy (cross-entropy), dropout, and the introduction of various optimizations in TensorFlow. Fifth Lesson, CNN, and CNN to solve the problem of mnist classification. The sixth lesson uses Tensorboard to visualize the structure and visualize the process of the network operation. The seventh lesson is the explanation of recurrent neural network

This article introduces a very simple threshold rnn (gated recurrent neural network),Here are two doors horizontal/forget gate and Vertical/input Gate, i.e.which (Logistic sigmoid function)The following assumes that the input data XT meet the following properties,If the hidden layer node is initialized to 0, that is, the network response to the Pulse XT is,With attenuation to 0, the forget gate controls the attenuation speed, so when the hidden-layer node HT (i) encounters a strong signal,HT (i)

problem and image feature, then co-attention Learning, and then the two weighted feature to be combined, and then input into the memory network, The choice of the final answer. Image Embedding: extracting feature with pre-trained model; Question embedding: using bidirectional LSTM network to study language features;Sequential co-attention:Here the synergistic attention mechanism, taking into account the common characteristics of images and texts, int

, they learned the way of interpolate space in the ICLR 2016 paper "Generating sentences from a continuous", which shows the hidden states in the generated picture. , we can see the gradual evolution of the image process. At the same time, they also say that Vector arithmetic are used in images and have some of the following results: GRAN The penultimate paper to be recommended today [5] also has a lot of similarities with DRAW. It has been said that sequential version can be considered when i

A course of recurrent neural Network (1)-RNN Introduction source:http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/ As a popular model, recurrent neural Network (Rnns) has shown great application prospect in NLP. Despite the recent popularity, there are few resources to explain the Rnns principle and how to implement it fully. So there's this tutorial. This tutorial will contain the following four sections: Rnns Introduction to use Python and Theano to

task, and overlapping red circles represent shared feature areas, which are used to capture common features that exist between different tasks.This article uses confrontation training to ensure that the shared space contains only multi-tasking shared information, as well as the use of orthogonal constraints to eliminate redundant information between shared and private spaces. 2.2 Recurrent Models for Text classificationThis article uses the long Short-term memory (

Deep learning is a prominent topic in the AI field. it has been around for a long time. It has received much attention because it has made breakthroughs beyond human capabilities in computer vision (ComputerVision) and AlphaGO. Since the last investigation, attention to deep learning has increased significantly. Deep learning is a prominent topic in the AI field. it has been around for a long time. It has received much attention because it has made breakthroughs beyond human capabilities in Comp

Oaching to me and hides the screen.Specifically, Keras is used to implement neural network for learning his face, a Web camera was used to recognize that he I s approaching, and switching the screen.MissionThe mission is-to-switch the screen automatically when my boss was approaching to me.The situation is as follows:It is on 6 or 7 meters from the seat to my seat. He reaches my seat in 4 or 5 seconds after he leaves his seat. Therefore, it's necessa