lstm

the task. For example, when predicting the emotion of a sentence, we only care about the final output, not the emotion of each word. Similarly, we do not need to have input at every step. The biggest feature of RNN is its hidden state, which captures some of the information in the sentence. WHAT CAN Rnns do? RNN has achieved great success in many NLP tasks. What I want to say at this point is that most of the RNN types are lstm,

Show and Tell:lessons learned from the 2015 Mscoco Image Captioning Challenge Code The Image caption task is given an image that describes the information contained in an image. It contains two aspects, image feature extraction and statement sequence description, in which CNN and RNN play an important role. The following figure is the first input of the 4096-dimensional image feature extracted by CNN as LSTM, and the content of the image is described

Introduced Image caption is a task combining computer vision and natural language processing, the author proposes a method based on neural network, which combines the CNN network for object recognition with the LSTM Network for machine translation, and trains the network by maximal correctly described likelihood function. When the paper was published, Bleu-1 scored the highest score of 25 points on the Pascal DataSet, and the author's model could reac

references: [Detection] CNN's "Object Detection" chapterApplication of RNN to target detection in computer visionMachine Learning Hardware Investment Research RNN LSTM [Welfare] in-depth understanding Rnns LSTM Web Learning materials @ Zhwhong [RNN] Simple lstm Code Implementation BPTT theory derivation @ zhwhong Application of

chronological order is called BPTT (back propagation Through time). However, although RNN is a very powerful dynamic system, its training process will still encounter a big problem, because the gradient at each time step may grow also may decline, so after many time steps of the reverse propagation, the gradient will often explode or disappear, the internal state of the network for the long-term past input memory is very weak.One solution to this problem is to add an explicit memory module to t

chronological order is called BPTT (back propagation Through time). However, although RNN is a very powerful dynamic system, its training process will still encounter a big problem, because the gradient at each time step may grow also may decline, so after many time steps of the reverse propagation, the gradient will often explode or disappear, the internal state of the network for the long-term past input memory is very weak.One solution to this problem is to add an explicit memory module to t

abandoned the Kubernetes and developed a training system designed for intensive learning Rapid, which can quickly replicate the results and data trained on multiple machines in a distributed system, and then update the training parameters;They used the GYM as a training environment. Gym is the OpenAI self-developed training environment for intensive learning, which includes various programs and background codes required by OpenAI Five.After the deployment is complete, OpenAI needs to address th

Team Introduction Author: Alberto Montes, Amaia Salvador, Santiago Pascual, Xavier Giro-i-nieto The authors are from a Spanish university in Universitat Politècnica de Catalunya (UPC), a very strong science and engineering, published in Nips Workshop article. In Activitynet Challenge 2016 got a good motive Using c3d[1] can capture the short time space-time feature, then lstm processing the long time information, untrimmed videos to carry on the cl

The main point of this article is from Google paper. Towards End-to-endspeech recognitionusing Deep neural Networks.Problem background: Traditional speech recognition system needs to be featured, acoustic modeling (State-phoneme-triphone), Language modeling series process, in which acoustic modeling requires the state clustering of context-related phoneme models, and the alignment of each frame feature is needed. The End-to-end system mainly raises the following questions: 1. Features indicate:

negotiating strategy to mislead each other by showing a false interest in the negotiation process and to benefit from the choice of the actual goal. Creating such an interactive robot is a new and very successful attempt. Future details about it and the code will be open source. Of course, the news that the robot invented a new language is a bit of a mystery. This is not a special thing to do when training (when negotiating with the same agent), abandoning the limitation of similarity to huma

Preface There are few practical projects in the direct use of depth learning to achieve end-to-end chat robot, but here we look at how to use the depth of learning SEQ2SEQ model to achieve a simple chat robot. This article will try to use TensorFlow to train a seq2seq chat robot to enable robots to answer questions based on corpus training. Seq2seq The mechanism of SEQ2SEQ can be seen in the previous article "in-depth study of the SEQ2SEQ model." Cyclic neural network Cyclic neural networks are

the learning sequence, Lapgan continuously downsample and upsample operations, and then in each pyramid level, only the residuals are passed to the discriminant model (D) for judgment. Such a combination of sequential + residuals can effectively reduce the content and difficulty that Gan needs to learn, thus achieving the purpose of "auxiliary" Gan learning. Another work based on sequential thought to improve GAN comes from the GRAN in [5]. Unlike Lapgan [4] Each sequential step (pyramid level

neural network to dynamic network.Supervised Sequence Labelling with Recurrent Neural Networks http://www.cs.toronto.edu /~ Graves/preprinthistory. The development of recurrent neural networks. VanillaRNN-> Enhanced the hidden layer function-> Simple RNN-> GRU-> LSTM-> CW-RNN-> Bidirectional deepening Network-> Bidirectional RNN-> Keep Bidrectional RNN-> Combination of the two: DBLSTMRecurrent Neural Networks, Part 1-Introduction to RNNs http://www.w

practice of small numbers work very well on a shallow network, but the performance on the deep network is not good. When the network is deep, many weight matrices are multiplied, so bad results are magnified. However, if it is a shallow network, SGD can help us solve the problem. So it is necessary to focus on initialization. Try a variety of different initializations, and the effort will be rewarded. If the network does not work at all (i.e. it cannot be implemented), continuing to imp

with Sequential2. Add the nerve layer with model.add and add the dense fully connected nerve layer.There are two parameters, one is the dimension of input data and output data, and X and Y are one dimension in the example of this code.If you need to add the next layer of nerves, you don't have to define the latitude of the input, because it defaults to the output of the previous layer as input to the current layer. In this case, just one layer is enough.# build a neural network from the 1st lay

RNN to train the text the vector, finally is uses the ordinary Ann as the classifier, here mainly says the author's process which produces the text vectorFirst, let's look at how the CNN model generates text vectors.For sentence of length L, each word is an M-dimensional word vector, and for a filter it operates as follows: The above picture is the shape of the filter is 3*m, in the convolution layer, can get c1c2. Cl-2, and then max-pooling the operation and finally gets a numericalUsing N fil

of a neural network is to give a known set of input and output, called a training sample, to do the steps (i.e. the model) is unknown, then how to determine the step (model) it. "Regression/Fit", using the simplest equation model to make an analogy ... directly on the formula. The training process of neural network is similar, and some coefficients in hidden layer nodes are determined by training. But the neural network model itself is non-linear, more complex. Feedforward, error reversal prop

The previous section describes the use of Pytorch to construct a CNN network, which introduces points to advanced things lstm. Please refer to the two famous blogs about Lstm's introduction to the theory: http://karpathy.github.io/2015/05/21/rnn-effectiveness/ http://colah.github.io/posts/2015-08-Understanding-LSTMs/ And one of my previous Chinese translation blogs: http://blog.csdn.net/q295684174/article/details/78973445

Model SaverIssue Link:saving and Restoring a trained LSTM in Tensor Flow Problem Description: when you save the RNN/LSTM model in TensorFlow, you need to define saver after the LSTM model is established, such as: # # # Model Training and Saving Code # # # # define the LSTM model code here saver = Tf.train.Saver () # #

　　Deep Attention Recurrent q-network5vision groups absrtact : This paper introduces the Attention mechanism of DQN, which makes learning more directional and instructive. (Some time ago to do a work plan to do so, who thought, so soon by these children to achieve, shame AH (⊙o⊙))Introduction : We know that DQN is a continuous 4 frames of video input into the CNN, then, although this has achieved good results, but still can only remember the 4 frames of information, the previous will be forgotten