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# Seq2seq 챗봇

### current community

In this post, we’ll assume basic knowledge about Deep Learning (Convolutions, LSTMs, etc.). For readers new to Computer Vision and Natural Language Processing, have a look at the famous Stanford classes cs231n and cs224n. The seq2seq model is a network that converts a given sequence of words into a different sequence and is capable of relating the words that seem more important. Here, LSTM network is a good example for seq2seq model

· Each layer has a straightforward position-wise fully connected feed-forward network and consist of a multi-head self-attention layer.The output is $$p_j$$ : the probability of generating the one-hot vector $${\bf y}_j$$ of the $$j$$-th word

par - parallel. strict - strict sequencing. seq - weak sequencing. critical - critical region. The loop construct represents a recursive application of the seq operator where the loop operand is sequenced after the result of earlier iterations 1. The first flaw of RNN is its sequential nature. It means that each hidden state depends on the output of the previous hidden state. This becomes a huge problem for GPUs. As they have huge a computational power, they resent having to wait for data from the network to become available. This makes RNN unfit even with technologies like CuDNN which slow down the whole process for GPU.

seq2seq的基础是一个LSTM编码器加上一个LSTM解码器。 在机器翻译的语境中, 最常听到的是: 用一种语言造句, 编码器把它变成一个固定大小的陈述。 解码器将他转换成一个句子, 可能和之前的句子长度不同 Unlike sequence prediction with a single RNN, where every input corresponds to an output, the seq2seq model frees us from sequence length and order, which makes it ideal for translation between two languages.My input should be [[1,5,6,7,4], [4,5,7,5,4], [7,5,4,2,1]] where int numbers are representations of words.In this case, we use the $${\rm tanh}$$ as the activation function. And we must use the encoder’s hidden vector of the last position as the decoder’s hidden vector of first position as following: seq2seq.SimpleSeq2Seq. By T Tak. Here are the examples of the python api seq2seq.SimpleSeq2Seq taken from open source projects. By voting up you can indicate which examples are most useful and appropriate

loading.. Transformer has shown tremendous results being able to outperform its recurrent equivalents and LSTM-based models, in spite of overlooking the traditional recurrent architectures.

The above method aims at modelling the distribution of the next word conditionned on the beginning of the sentenceThe encoder uses the embeddings of the source sentence for its keys, values and queries, whereas the decoder uses the output of the encoder for its keys and values, and embedding of the target sentence for its query.In my last post about named entity recognition, I explained how to predict a tag for a word, which can be considered as a relatively simple task. However, some tasks like translation require more complicated systems. You may have heard from some recent breakthroughs in Neural Machine Translation that led to (almost) human-level performance systems (used in real-life by Google Translation, see for instance this paper enabling zero-shot translation). These new architectures rely on a common paradigm called encoder-decoder (or sequence to sequence), whose goal is to produce an entire sequence of tokens.

## GitHub - deepseasw/seq2seq_chatbot: 케라스로 만드는 Seq2Seq 챗봇

Sequelize. Sequelize is a promise-based Node.js ORM for Postgres, MySQL, MariaDB, SQLite and Microsoft SQL Server. It features solid transaction support, relations, eager and lazy loading, read replication and more import matplotlib.pyplot as plt plt.switch_backend('agg') import matplotlib.ticker as ticker import numpy as np def showPlot(points): plt.figure() fig, ax = plt.subplots() # this locator puts ticks at regular intervals loc = ticker.MultipleLocator(base=0.2) ax.yaxis.set_major_locator(loc) plt.plot(points) Evaluation¶ Evaluation is mostly the same as training, but there are no targets so we simply feed the decoder’s predictions back to itself for each step. Every time it predicts a word we add it to the output string, and if it predicts the EOS token we stop there. We also store the decoder’s attention outputs for display later.

## python - Keras seq2seq - word embedding - Stack Overflo

It is well known that the seq2seq model learns much better when the source sentences are reversed. The paper[1] says that “While the LSTM is capable of solving problems with long term dependencies, we discovered that the LSTM learns much better when the source sentences are reversed (the target sentences are not reversed). By doing so, the LSTM’s test perplexity dropped from 5.8 to 4.7, and the test BLEU scores of its decoded translations increased from 25.9 to 30.6.” So, at the first line in the forward, the input sentences are reversed xs = [x[::-1] for x in xs].and you recognize the standard cross entropy: we actually are minimizing the cross entropy between the target distribution (all one-hot vectors) and the predicted distribution outputed by our model (our vectors $p_i$).Encoder block actually just does a bunch of matrix multiplications which is followed by element-wise transformation. This operation makes the transformer super-fast, as everything is just parallelizable matrix multiplication. Thus, by piling these transformations on top of each other, we create a very powerful network.

### Seq2Seq with Attention and Beam Search Guillaume Genthial blo

• Beyond Seq2Seq with Augmented RNNs. author: Edward Grefenstette, Google, Inc. published: Aug. 23, 2016, recorded: August 2016, views: 20607
• Consider the sentence “Je ne suis pas le chat noir” → “I am not the black cat”. Most of the words in the input sentence have a direct translation in the output sentence, but are in slightly different orders, e.g. “chat noir” and “black cat”. Because of the “ne/pas” construction there is also one more word in the input sentence. It would be difficult to produce a correct translation directly from the sequence of input words.
• Seq2Seqを使ってポケモンの名前を作ったった. Generate empty target sequence of length 1. target_seq = np.zeros((1, 1, num_decoder_tokens)) # Populate the first character of target sequence with the start character. target_seq[0, 0, target_token_index['\t']] = 1. # Sampling loop for a batch of..
• tfa.seq2seq.sequence_loss. View source on GitHub. Weighted cross-entropy loss for a sequence of logits
• , where i is the dimension and pos gives the position. The model easily learns to get the relative positions as each dimension of the positional encoding is a wave with a different frequency.
• Generate regular sequences. seq is a standard generic with a default method. seq.int is a primitive which can be much faster but has a few seq is generic, and only the default method is described here. Note that it dispatches on the class of the first argument irrespective of argument names

### seq2seq (Sequence to Sequence) Model for Deep Learning with

• The vector $c_t$ is the attention (or context) vector. We compute a new context vector at each decoding step. First, with a function $f (h_{t-1}, e_{t’}) \mapsto \alpha_{t’} \in \mathbb{R}$, compute a score for each hidden state $e_{t’}$ of the encoder. Then, normalize the sequence of $\alpha_{t’}$ using a softmax and compute $c_t$ as the weighted average of the $e_{t’}$.
• Attention models, just like humans, try to translate a part of the sentence at a time which makes them much more efficient when compared to any RNN based seq2seq model.
• Not only that, but because of its highly parallelizable nature, the Transformer is able to do this at a significantly-reduced number of FLOPs (floating-point operations) in training.
• You will also find the previous tutorials on NLP From Scratch: Classifying Names with a Character-Level RNN and NLP From Scratch: Generating Names with a Character-Level RNN helpful as those concepts are very similar to the Encoder and Decoder models, respectively.

### Write a Sequence to Sequence (seq2seq) Model — Chainer

• Similar to the character encoding used in the character-level RNN tutorials, we will be representing each word in a language as a one-hot vector, or giant vector of zeros except for a single one (at the index of the word). Compared to the dozens of characters that might exist in a language, there are many many more words, so the encoding vector is much larger. We will however cheat a bit and trim the data to only use a few thousand words per language.
• Seq2seq goal-oriented bot [docs]. One can use several pre-trained English, multi-lingual and Russian BERT models that are listed above. Two main components of BERT classifier pipeline in DeepPavlov are BertPreprocessor and BertClassifierModel
• The main takeaway from the discussion above is that for the same model, we can define different behaviors. In particular, we defined a specific behavior that speeds up training.
• But if we pass the entire sentence at once to the decoder the model just repeats the sentence and learns nothing. This is prevented by masked multi-head attention block by masking the future tokens when we decode a certain word.
• 케라스로 만드는 Seq2Seq 챗봇. Contribute to deepseasw/seq2seq_chatbot development by creating an account on GitHub

There are a lot of varieties of seq2seq models. We can use the different RNN models in terms of: (1) directionality (unidirectional or bidirectional), (2) depth (single-layer or multi-layer), (3) type (a vanilla RNN, a Long Short-term Memory (LSTM), or a gated recurrent unit (GRU)), and (4) additional functionality (s.t. Attention Mechanism).You could simply run plt.matshow(attentions) to see attention output displayed as a matrix, with the columns being input steps and rows being output steps:def readLangs(lang1, lang2, reverse=False): print("Reading lines...") # Read the file and split into lines lines = open('data/%s-%s.txt' % (lang1, lang2), encoding='utf-8').\ read().strip().split('\n') # Split every line into pairs and normalize pairs = [[normalizeString(s) for s in l.split('\t')] for l in lines] # Reverse pairs, make Lang instances if reverse: pairs = [list(reversed(p)) for p in pairs] input_lang = Lang(lang2) output_lang = Lang(lang1) else: input_lang = Lang(lang1) output_lang = Lang(lang2) return input_lang, output_lang, pairs Since there are a lot of example sentences and we want to train something quickly, we’ll trim the data set to only relatively short and simple sentences. Here the maximum length is 10 words (that includes ending punctuation) and we’re filtering to sentences that translate to the form “I am” or “He is” etc. (accounting for apostrophes replaced earlier).· Each layer has one sub-layer of a fully-connected feed-forward network and two sub-layers of multi-head attention mechanisms.· The first multi-head attention sub-layer is modified to the “masked multi-head attention”, to prevent positions from attending to subsequent positions, as we don’t want to look into the future of the target sequence when predicting the current position.

### Sequence to sequence - training - Keras Documentatio

• additional patch for SEQ16 user model of this sequencer is KORG SQ-1. Mode There are 7 running modes. Row B if you choose seq mode 3 to 7, sequencer becomes 8 x 2(row A and row B). In this situation, row A controls midi note and row B controls value of assigned parameter
• Then we call train many times and occasionally print the progress (% of examples, time so far, estimated time) and average loss.
• If we use beam search, a small error at the first step might be rectified at the next step, as we keep the gold hypthesis in the beam!
• WordPress Shortcode. Link. RNN, LSTM and Seq-2-Seq Models. The two networks are trained jointly to maximize the conditional probability of the target sequence given a source sequence. Each box in the picture above represents a cell of the RNN

## Why do Transformers yield Superior Sequence to Sequence(Seq2Seq)

Seq creates the visibility you need to quickly identify and diagnose problems in complex applications and microservices. Collect application logs. Seq is a centralized log file with superpowers. Intuitive expression-based filtering, combined with free-text and regular expression searches, mean you can.. There is another problem that I will explain with an example. Let’s take a look at the following sentences –To learn from a variety of representations, the Multi-Head Attention applies different linear transformations to the keys, values and queries for each “head” of attention as shown in the figure below. Seq2Seq and Neural Machine Translation - TensorFlow and Deep Learning Singapore - Продолжительность: 52:20 Engineers.SG 5 725 просмотров # Turn a Unicode string to plain ASCII, thanks to # https://stackoverflow.com/a/518232/2809427 def unicodeToAscii(s): return ''.join( c for c in unicodedata.normalize('NFD', s) if unicodedata.category(c) != 'Mn' ) # Lowercase, trim, and remove non-letter characters def normalizeString(s): s = unicodeToAscii(s.lower().strip()) s = re.sub(r"([.!?])", r" \1", s) s = re.sub(r"[^a-zA-Z.!?]+", r" ", s) return s To read the data file we will split the file into lines, and then split lines into pairs. The files are all English → Other Language, so if we want to translate from Other Language → English I added the reverse flag to reverse the pairs.

In the constructor, it initializes all parameters with values sampled from a uniform distribution $$U(-1, 1)$$. I am trying to do ATAC-Seq experiment using plant samples. I am wondering whether there is any size selection step after PCR amplification ( Using ampure I am very interested to see bioanalyzer trace of a successful ATAC-Seq experiment. If possible, would you please share your bioanalyzer trace The Seq2Seq Model¶. A Recurrent Neural Network, or RNN, is a network that operates on a sequence and uses its own output as input for subsequent steps. Unlike sequence prediction with a single RNN, where every input corresponds to an output, the seq2seq model frees us from sequence length and.. > je suis desolee si je vous ai effrayes . = i m sorry if i frightened you . < i m sorry if i frightened you . <EOS> > il est vieux . = he is old . < he s old . <EOS> > il est prompt a exprimer ses inquietudes . = he is quick to voice his concerns . < he is a to his business . <EOS> > elle est sourde a mes conseils . = she is deaf to my advice . < she is a to my job . <EOS> > je suis ravi de te rencontrer . = i m delighted to meet you . < i m delighted to meet you . <EOS> > tu gaspilles de l eau . = you re wasting water . < you re wasting water water . <EOS> > je suis tellement genee que je veux mourir . = i m so embarrassed i want to die . < i m so embarrassed i want to die . <EOS> > je suis paresseuse . = i am lazy . < i m lazy . <EOS> > vous n etes pas trop en retard . = you re not too late . < you re not too late . <EOS> > c est toi la chef . = you re the leader . < you re the leader . <EOS> Visualizing Attention¶ A useful property of the attention mechanism is its highly interpretable outputs. Because it is used to weight specific encoder outputs of the input sequence, we can imagine looking where the network is focused most at each time step.

주요 개념 1. seq2seq 2. attention 3. teacher forcing 참고자료: NEURAL MACHINE TRANSLATION BY JOINTLY LEARNING TO ALIGN AND TRAN.. This video explains Convolutional Sequence to Sequence (Seq2Seq) Learning published by Facebook in detail Use the SQL object's string to separate the elements in seq. Note that Composed objects are iterable too, so they can be used as class psycopg2.sql.Composed(seq)¶. A Composable object made of a sequence of Composable. The object is usually created using Composable operators and methods 2. The second is the long-range dependencies. We know that, theoretically, LSTMs can possess long-term memory, yet memorizing things for a long period of time is a challenge. What you write about SEQ N and ACK N and how they are incremented is not written in any TCP article I read, but that's exactly how real TCP works! Every byte of data exchanged across a TCP connection, along with the SYN and FIN flags, is assigned a seq Now, this is the issue which is solved by Transformer using the Multi-Head Attention block. This block computes multiple attention weighted sums instead of a single attention pass over the values — hence the name “Multi-Head” Attention.

### NLP From Scratch: Translation with a Sequence to Sequence Network

1. We see there is a masked multi-head attention block which gets its name because we need to hide the inputs from the decoder to the future time-steps.
2. In the Seq2seq model, the output vector of the LSTM Encoder is the input for the LSTM Decoder, and. When training on continuous sequence pairs, such as long conversations, use the Conversational model instead of Seq2seq model, with argument context_sensitive=True
3. def translate(self, xs, max_length=100): batch = len(xs) with chainer.no_backprop_mode(), chainer.using_config('train', False): xs = [x[::-1] for x in xs] exs = sequence_embed(self.embed_x, xs) h, c, _ = self.encoder(None, None, exs) ys = self.xp.full(batch, EOS, numpy.int32) result = [] for i in range(max_length): eys = self.embed_y(ys) eys = F.split_axis(eys, batch, 0) h, c, ys = self.decoder(h, c, eys) cys = F.concat(ys, axis=0) wy = self.W(cys) ys = self.xp.argmax(wy.array, axis=1).astype(numpy.int32) result.append(ys) # Using xp.concatenate(...) instead of xp.stack(result) here to # support NumPy 1.9. result = chainer.get_device('@numpy').send( self.xp.concatenate([x[None, :] for x in result]).T) # Remove EOS taggs outs = [] for y in result: inds = numpy.argwhere(y == EOS) if len(inds) > 0: y = y[:inds[0, 0]] outs.append(y) return outs After the model learned the parameters, the function translate is called to generate the translated sentences outs from the source sentences xs.
4. Seq2seq is a family of machine learning approaches used for language processing. Applications include language translation, image captioning, conversational models and text summarization. The algorithm was developed by Google for use in machine translation
5. We can very well observe that the Transformer still uses the same encoder-decoder from the trivial seq2seq model used in NMT (Neural Machine Translation).

### Seq2Seq tutorial (Tensorflow) - YouTub

• dexseq rna-seq written 1 day ago by rohitsatyam102 • 90 • updated 9 hours ago by Kevin Blighe 59k. assembly next-gen rna-seq R written 3 months ago by Balatheskulter • 0 • updated 2 days ago by Biostar 20
• 172.17..21: seq=3 ttl=64 time=0.080 ms. If you print out the environment variables you will see the following (I have removed the other environment variables
• · Both the source and target sequences first go through embedding layers to produce data of the same dimension, equals 512.
• def prepareData(lang1, lang2, reverse=False): input_lang, output_lang, pairs = readLangs(lang1, lang2, reverse) print("Read %s sentence pairs" % len(pairs)) pairs = filterPairs(pairs) print("Trimmed to %s sentence pairs" % len(pairs)) print("Counting words...") for pair in pairs: input_lang.addSentence(pair[0]) output_lang.addSentence(pair[1]) print("Counted words:") print(input_lang.name, input_lang.n_words) print(output_lang.name, output_lang.n_words) return input_lang, output_lang, pairs input_lang, output_lang, pairs = prepareData('eng', 'fra', True) print(random.choice(pairs)) Out:
• For example, when using the uni-directional RNN of one layer, the process can be represented as the following function $$\Psi^{(s)}$$:
• · Each sub-layer adopts a residual connection and a layer normalization. All the sub-layer’s output data is of the same dimension i.e. 512.

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Explore and run machine learning code with Kaggle Notebooks | Using data from multiple data sources.. class EncoderRNN(nn.Module): def __init__(self, input_size, hidden_size): super(EncoderRNN, self).__init__() self.hidden_size = hidden_size self.embedding = nn.Embedding(input_size, hidden_size) self.gru = nn.GRU(hidden_size, hidden_size) def forward(self, input, hidden): embedded = self.embedding(input).view(1, 1, -1) output = embedded output, hidden = self.gru(output, hidden) return output, hidden def initHidden(self): return torch.zeros(1, 1, self.hidden_size, device=device) The Decoder¶ The decoder is another RNN that takes the encoder output vector(s) and outputs a sequence of words to create the translation. seq2seq 是一类特殊的 RNN，在机器翻译、文本自动摘要和语音识别中有着成功的应用。 翻译实现过程. 制定解码器采样的贪婪策略。 这很简单，因为可以使用 tf.contrib.seq2seq.GreedyEmbeddingHelper 中定义的库，由于不知道目标句子的准确长度，因此这里..

Example of data. Create some time serie data. df <- data.frame( date = seq(Sys.Date(), len=100, by=1 day)[sample(100, 50)], price = runif(50) ). df <- df[order(df$date), ] head(df) $${\bf E}^{(t)} \in {\mathbb R}^{D \times |{\mathcal V}^{(t)}|}$$ is the embedding matrix of the encoder.In the simplest seq2seq decoder we use only last output of the encoder. This last output is sometimes called the context vector as it encodes context from the entire sequence. This context vector is used as the initial hidden state of the decoder. ### Seq2Seq로 챗봇 구 1. guillaumegenthial Natural Language Processing and AI 2. $$\Psi$$ is the function to generate the hidden vectors $${\bf h}_j^{(t)}$$, and $$\Upsilon$$ is the function to calculate the generative probability of the one-hot vector $${\bf y}_j$$. When $$j=1$$, $${\bf h}_{j-1}^{(t)}$$ or $${\bf h}_0^{(t)}$$ is $$\bf z$$ generated by $$\Lambda({\bf X})$$, and $${\bf y}_{j-1}$$ or $${\bf y}_0$$ is the one-hot vector of BOS. 3. The full documentation for seq is maintained as a Texinfo manual. If the info and seq programs are properly installed at your site, the command. info coreutils aqseq invocationaq. should give you access to the complete manual 4. import io from nltk.translate import bleu_score import numpy import progressbar import six import chainer import chainer.functions as F import chainer.links as L from chainer import training 2.2.2 Define Training Settings¶ Define all training settings here. 5. She discussed #Seq2Seq #Seq2Tree #Paraphrases #ParaNet Bilingual Pivoting #NLP #LSTM pic.twitter.com/h07JOq0Nm7 - New Orleans Ernest N. Morial Convention Center. Tutorial on #seq2seq with @OriolVinyalsML and Navdeep Jaitly. #icml2017 pic.twitter.com/Dj0npCZ9uY 6. 用于构建 seq2seq 模型和动态解码的模块，建立在 tf.contrib.rnn 库的顶部。 attention 包装是 RNNCell 包装其他 RNNCell 对象并实现 attention 的对象。attention 的形式由一个子类 tf.contrib.seq2seq.AttentionMechanism 决定 7. The original seq2seq model is implemented with Long Short-Term Memory (LSTM) model, published by Google.(see their paper) It is basically a character-based model that generates texts according to a sequence of input Facebook uses this CNN seq2seq model for their machine translation model ### LSTM seq2seq with keras Kaggl 1. Distributed learning in Tensorflow seq2seq tutorial. When training seq2seq model with bucketing method do we keep separate RNNs for each bucket? Tensorflow max_gradient_norm parameter in Seq2Seq. tensorflow seq2seq inference stage wired result. Long Sequence In a seq2seq model with.. 2. STAR is robust against SNPs. Tophat2 has often problems to map reads with 2 or more differences (e.g. SNPs or seq. errors). It is currently for genome sequencing, but we soon will release an RNA-Seq version were you can benchmark STAR and Tophat2 and others (e.g. Gsnap, HIsat) on the.. 3. The seq2seq model converts an input sequence into an output sequence. Let the input sequence and the output sequence be $$\bf X$$ and $$\bf Y$$. The $$i$$-th element of the input sequence is represented as $${\bf x}_i$$, and the $$j$$-th element of the output sequence is also represented as $${\bf y}_j$$. Generally, each of the $${\bf x}_i$$ and the $${\bf y}_j$$ is the one-hot vector of the symbols. For example, in natural language processing(NLP), the one-hot vector represents the word and its size becomes the vocabulary size. ## seq2seq-lstm · PyP Sequence-to-sequence (seq2seq) models, adopted from neural machine translation (NMT), have achieved state-of-the-art performance on these tasks by treating source code as a sequence of tokens. We present CODE2SEQ: an al-ternative approach that leverages the syntactic structure of.. $${\bf E}^{(s)} \in {\mathbb R}^{D \times |{\mathcal V}^{(s)}|}$$ is the embedding matrix of the encoder. Reading lines... Read 135842 sentence pairs Trimmed to 10599 sentence pairs Counting words... Counted words: fra 4345 eng 2803 ['nous sommes en train de couler .', 'we re sinking .'] The Seq2Seq Model¶ A Recurrent Neural Network, or RNN, is a network that operates on a sequence and uses its own output as input for subsequent steps. Meena는 트랜스포머 Seq2Seq로 대화 히스토리를 입력받아 문장을 생성하는 단순한 방식입니다. 반면에 Blender는 이름 그대로 3가지의 그다음 이 답변들을 하나로 묶어 Generator에 넣고 문장을 생성합니다. 이렇게 하면 Seq2Seq만 사용한 것보다 훨씬 생생하고 구체적인 답변을 할 수 있습니다 The first layer, or the encoder embedding layer converts the each word in the input sentence to the embedding vector. When processing the $$i$$-th word in the input sentence, the input and the output of the layer are the following:def showAttention(input_sentence, output_words, attentions): # Set up figure with colorbar fig = plt.figure() ax = fig.add_subplot(111) cax = ax.matshow(attentions.numpy(), cmap='bone') fig.colorbar(cax) # Set up axes ax.set_xticklabels([''] + input_sentence.split(' ') + ['<EOS>'], rotation=90) ax.set_yticklabels([''] + output_words) # Show label at every tick ax.xaxis.set_major_locator(ticker.MultipleLocator(1)) ax.yaxis.set_major_locator(ticker.MultipleLocator(1)) plt.show() def evaluateAndShowAttention(input_sentence): output_words, attentions = evaluate( encoder1, attn_decoder1, input_sentence) print('input =', input_sentence) print('output =', ' '.join(output_words)) showAttention(input_sentence, output_words, attentions) evaluateAndShowAttention("elle a cinq ans de moins que moi .") evaluateAndShowAttention("elle est trop petit .") evaluateAndShowAttention("je ne crains pas de mourir .") evaluateAndShowAttention("c est un jeune directeur plein de talent .") Out: This question on Open Data Stack Exchange pointed me to the open translation site https://tatoeba.org/ which has downloads available at https://tatoeba.org/eng/downloads - and better yet, someone did the extra work of splitting language pairs into individual text files here: https://www.manythings.org/anki/ We help the daring build legendary companies from idea to IPO and beyond Repository of advanced Seq2Seq Learning models for Keras. 3 years ago by @dallmann. show all tags. 2Unsupervised Pre-training With Seq2Seq Reconstruction Loss for Deep Relation Extraction Models dfd79c2f A: hmac-sha1 79bf3cdc d00636fe 89fff227 0677c5a5 aa8a8e22 seq=0x00000000 replay=4 flags=0x20000000 state=mature created: Feb 23040(s) last: Feb 9 15:41:49 2013 hard: 0(s) soft: 0(s) current: 13086(bytes) hard: 0(bytes) soft: 0(bytes) allocated: 174 hard: 0 soft: 0 sadb_seq=3 pid=8374.. ### Seq2Seq for LaTeX generation - part I schemaName: public. sequenceName: seq_id. startValue: 5. CREATE SEQUENCE [public].seq_id AS int START WITH 5 INCREMENT BY 2 MINVALUE 10 MAXVALUE 1000 ORDER CYCL There are other forms of attention that work around the length limitation by using a relative position approach. Read about “local attention” in Effective Approaches to Attention-based Neural Machine Translation. © Copyright 2015, Preferred Networks, inc. and Preferred Infrastructure, inc. Revision df53bff3. ### Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence • This is made possible by the simple but powerful idea of the sequence to sequence network, in which two recurrent neural networks work together to transform one sequence to another. An encoder network condenses an input sequence into a vector, and a decoder network unfolds that vector into a new sequence. • def sequence_embed(embed, xs): x_len = [len(x) for x in xs] x_section = numpy.cumsum(x_len[:-1]) ex = embed(F.concat(xs, axis=0)) exs = F.split_axis(ex, x_section, 0) return exs self.encoder and self.decoder are the encoder and the decoder of the seq2seq model. Each element of the decoder output os is $$h_{[1:J]}^{(t)}$$ in the figure above. • Seq2Seq로 챗봇 구현. 딥러닝으로 챗봇을 만드는 한가지 방법은 Seq2Seq(Sequence-to-Sequence) 모델을 사용하는 것입니다. 문장을 그대로 입력 받아서 바로 문장이 출력되도록 하는 방식입니다. Encoder와 Decoder 두개의 RNN을 사용하여 구현합니다 • optimizer = chainer.optimizers.Adam() optimizer.setup(model) 2.2.9 Setup and Run Trainer¶ Let’s make a trainer object. • readWe all are familiar with the classic Recurrent Neural Network (RNN) and Lost Short-Term Memory (LSTM), that were regarded as the go-to architectures for translation. Not even two years back, LSTM based architectures formed the base of every NLP (Natural Language Processing challenge) • def evaluateRandomly(encoder, decoder, n=10): for i in range(n): pair = random.choice(pairs) print('>', pair[0]) print('=', pair[1]) output_words, attentions = evaluate(encoder, decoder, pair[0]) output_sentence = ' '.join(output_words) print('<', output_sentence) print('') Training and Evaluating¶ With all these helper functions in place (it looks like extra work, but it makes it easier to run multiple experiments) we can actually initialize a network and start training. ## Seq2Seq Model Sequence To Sequence With Attentio Get Seq2seq Expert Help in 6 Minutes. Codementor is an on-demand marketplace for top Seq2seq engineers, developers, consultants, architects, programmers, and tutors. Get your projects built by vetted Seq2seq freelancers or learn from expert mentors with team training & coaching experiences The basic attention mechanism is simply a dot product between the query and the key. The size of the dot product tends to grow with the dimensionality of the query and key vectors, so the Transformer re-scales the dot product to prevent it from exploding into huge values.For example, when using the uni-directional RNN of one layer, the process can be represented as the following function $$\Psi^{(t)}$$:Now, let’s think about the processing steps in seq2seq model. The feature of seq2seq model is that it consists of the two processes:In this tutorial, we use French-English corpus from WMT15 website that contains 10^9 documents. We must prepare additional libraries, dataset, and parallel corpus. To understand the pre-processing, see 2.3.1 Requirements. ## Implementing a Sequence-to-Sequence Model Hacker Noo 2. Are seq2seq models more robust than the well-evaluated CNN-based image classiers? We provide an afrmative answer to the rst question by developing an effective adversarial attack framework called Seq2Sick. It is an optimization-based framework that aims to learn an input sequence that is close.. The encoder recurrent layer generates the hidden vectors from the embedding vectors. When processing the $$i$$-th embedding vector, the input and the output of the layer are the following:If you run this notebook you can train, interrupt the kernel, evaluate, and continue training later. Comment out the lines where the encoder and decoder are initialized and run trainIters again. Keras seq2seq - word embedding Ask Question Asked 2 years, 2 months ago Active 1 year, 4 months ago Viewed 4k times .everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty{ margin-bottom:0; } 6 1 I am working on a generative chatbot based on seq2seq in Keras. I used code from this site: https://machinelearningmastery.com/develop-encoder-decoder-model-sequence-sequence-prediction-keras/$ pwd /root2chainer/chainer/examples/seq2seq python seq2seq.py --gpu=0 giga-fren.preprocess.en giga-fren.preprocess.fr \ vocab.en vocab.fr \ --validation-source newstest2013.preprocess.en \ --validation-target newstest2013.preprocess.fr > log 100% (22520376 of 22520376) |#############| Elapsed Time: 0:09:20 Time: 0:09:20 100% (22520376 of 22520376) |#############| Elapsed Time: 0:10:36 Time: 0:10:36 100% (3000 of 3000) |#####################| Elapsed Time: 0:00:00 Time: 0:00:00 100% (3000 of 3000) |#####################| Elapsed Time: 0:00:00 Time: 0:00:00 epoch iteration main/loss validation/main/loss main/perp validation/main/perp validation/main/bleu elapsed_time 0 200 171.449 991.556 85.6739 0 400 143.918 183.594 172.473 0 600 133.48 126.945 260.315 0 800 128.734 104.127 348.062 0 1000 124.741 91.5988 436.536 ... NoteThe encoder generates an attention-based representation with the capability to locate a specific piece of information from a potentially infinitely-large context. Posted by Anna Goldie and Denny Britz, Research Software Engineer and Google Brain Resident, Google Brain Team. (Crossposted on the Google Open Source Blog). Last year, we announced Google Neural Machine Translation (GNMT), a sequence-to-sequence.. 43.5 ms 64 bytes from dead:beef::250:56ff:feb9:7caa: icmp_seq=2 ttl=63 time=42.9 ms ‍ --- dead:beef::0250:56ff:feb9:7caa ping statistics --- 2 packets transmitted, 2 received, 0% packet loss, time 4ms rtt min/avg/max/mdev = The decoder recurrent layer generates the hidden vectors from the embedding vectors. When processing the $$j$$-th embedding vector, the input and the output of the layer are the following: trainer.run() if args.save is not None: # Save a snapshot chainer.serializers.save_npz(args.save, trainer) 2.3 Run Example¶ 2.3.1 Requirements¶ Before running the example, you must prepare additional libraries, dataset, and parallel corpus. Seq / Arp (92 plug-ins) Sequence Generation. Description. Generate regular sequences. seq is a standard generic with a default method. seq.int is a primitive which seq is generic, and only the default method is described here. Note that it dispatches on the class of the first argument irrespective of argument names ### seq2seq model in Machine Learning - GeeksforGeek 1. Transformer has now been elongated in brand new models, most recently Bidirectional Encoder Representations from Transformers or simply BERT. 2. To handle this flaw, the Transformer just allows the encoder and decoder to see the entire input sequence all at once, directly modelling these dependencies using self-attention. 3. al paper in deep learning literature. It was originally introduced for machine translation tasks, but it has spread into many other application areas. 4. \begin{program} \VAR \seq{m := 0, p := 0, |last| : ; \ACTIONS |prog|: |prog| \ACTIONEQ % \seq{|line| : ', m := 0, i := 1}; \CALL |inhere| \ENDACTION l \ACTIONEQ % i := i+1; \IF (i=(n+1)) \THEN \CALL |alldone| \ 5. After calculating the recurrent layer output, the loss loss and the perplexity perp are calculated, and the values are logged by chainer.report. This seq2seq tutorial explains Sequence to Sequence modelling with Attention. In this article, we will cover two important concepts used in the current state of the art applications in Speech Recognition and Natural Language Processing - viz Sequence to Sequence modelling and Attention models train_iter = chainer.iterators.SerialIterator(train_data, args.batchsize) 2.2.7 Create RNN and Classification Model¶ Instantiate Seq2seq model.The encoder of a seq2seq network is a RNN that outputs some value for every word from the input sentence. For every input word the encoder outputs a vector and a hidden state, and uses the hidden state for the next input word. Tn-Seq Analysis. Phylogenetic Tree. Transcriptomics. Expression Import. RNA-Seq Analysis class CalculateBleu(chainer.training.Extension): trigger = 1, 'epoch' priority = chainer.training.PRIORITY_WRITER def __init__( self, model, test_data, key, device, batch=100, max_length=100): self.model = model self.test_data = test_data self.key = key self.batch = batch self.device = device self.max_length = max_length def __call__(self, trainer): device = self.device with chainer.no_backprop_mode(): references = [] hypotheses = [] for i in range(0, len(self.test_data), self.batch): sources, targets = zip(*self.test_data[i:i + self.batch]) references.extend([[t.tolist()] for t in targets]) sources = [device.send(x) for x in sources] ys = [y.tolist() for y in self.model.translate(sources, self.max_length)] hypotheses.extend(ys) bleu = bleu_score.corpus_bleu( references, hypotheses, smoothing_function=bleu_score.SmoothingFunction().method1) chainer.report({self.key: bleu}) 2.2.6 Create Iterator¶ Here, the code below just creates iterator objects. S = con2seq(b) S = con2seq(b,TS). Description. Deep Learning Toolbox™ software arranges concurrent vectors with a matrix, and sequential vectors with a cell array (where the second index is the time step) In this article we covered the seq2seq concepts. We showed that training is different than decoding. We covered two methods for decoding: greedy and beam search. While beam search generally achieves better results, it is not perfect and still suffers from exposure bias. During training, the model is never exposed to its errors! It also suffers from Loss-Evaluation Mismatch. The model is optimized w.r.t. token-level cross entropy, while we are interested about the reconstruction of the whole sentence… lstm_out, self.hidden = self.lstm(input.view(len(input), self.batch_size, -1)). # Only take the output from the final timetep. # Can pass on the entirety of lstm_out to the next layer if it is a seq2seq prediction If only the context vector is passed betweeen the encoder and decoder, that single vector carries the burden of encoding the entire sentence. tcpdump: verbose output suppressed, use -v or -vv for full protocol decode. listening on eth1, link-type EN10MB (Ethernet), capture size 65535 bytes. 22:23:38.696918 IP 78.12.34.56 > 193.98.76.54: ESP(spi=0xcd4e97ba,seq=0x1f5), length 100 1 Below in the FAQ section of this example, they provide an example on how to use embeddings with seq2seq. I'm currently figuring out the inference step myself. I'll post here when i get it. https://blog.keras.io/a-ten-minute-introduction-to-sequence-to-sequence-learning-in-keras.htmlMAX_LENGTH = 10 eng_prefixes = ( "i am ", "i m ", "he is", "he s ", "she is", "she s ", "you are", "you re ", "we are", "we re ", "they are", "they re " ) def filterPair(p): return len(p[0].split(' ')) < MAX_LENGTH and \ len(p[1].split(' ')) < MAX_LENGTH and \ p[1].startswith(eng_prefixes) def filterPairs(pairs): return [pair for pair in pairs if filterPair(pair)] The full process for preparing the data is: Practical seq2seq. Revisiting sequence to sequence learning, with focus on implementation details. Read these papers for a deeper understanding of seq2seq - Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation, Sequence to Sequence Learning with.. Lookup | Welcome | Wiki | Register | Music | Plot 2 | Demos | Index | Browse | More | WebCam Contribute new seq. or comment | Format | Style Sheet | Transforms | Superseeker | Recent The OEIS Community | Maintained by The OEIS Foundation Inc The input is $${\bf y}_{j-1}$$ : the one-hot vector which represents the $$(j-1)$$-th word generated by the decoder output layer 标签：Seq2Seq. 我们知道，Seq2Seq 现在已经成为了机器翻译、对话聊天、文本摘要等工作的重要模型，真正提出 Seq2Seq 的文章是《Sequence to Sequence Learning with Neural Networks》，但本篇《Learning Phrase.. RNA‐Seq Applica6ons - Annota6on: Alterna6ve Splicing Events. Ozsolak, F. and Milos, P. RNA sequencing: advances, challenges and opportuni.es Trapnell, C. et al Transcript assembly and quan.ca.on by RNA‐Seq reveals unannotated transcripts and isoform switching during cell dieren.a.on.. def indexesFromSentence(lang, sentence): return [lang.word2index[word] for word in sentence.split(' ')] def tensorFromSentence(lang, sentence): indexes = indexesFromSentence(lang, sentence) indexes.append(EOS_token) return torch.tensor(indexes, dtype=torch.long, device=device).view(-1, 1) def tensorsFromPair(pair): input_tensor = tensorFromSentence(input_lang, pair[0]) target_tensor = tensorFromSentence(output_lang, pair[1]) return (input_tensor, target_tensor) Training the Model¶ To train we run the input sentence through the encoder, and keep track of every output and the latest hidden state. Then the decoder is given the <SOS> token as its first input, and the last hidden state of the encoder as its first hidden state.hidden_size = 256 encoder1 = EncoderRNN(input_lang.n_words, hidden_size).to(device) attn_decoder1 = AttnDecoderRNN(hidden_size, output_lang.n_words, dropout_p=0.1).to(device) trainIters(encoder1, attn_decoder1, 75000, print_every=5000) Out: Character-based Seq2Seq model. Recurrent Neural Networks are a special type of Neural Networks. An overview of different types of neural networks can be found here. Seq2Seq. As you can see, 'HEY' is the input. The processing is based on the sequence The sequence to sequence (seq2seq) model[1][2] is a learning model that converts an input sequence into an output sequence. In this context, the sequence is a list of symbols, corresponding to the words in a sentence. The seq2seq model has achieved great success in fields such as machine translation, dialogue systems, question answering, and text summarization. All of these tasks can be regarded as the task to learn a model that converts an input sequence into an output sequence. Attention allows the decoder network to “focus” on a different part of the encoder’s outputs for every step of the decoder’s own outputs. First we calculate a set of attention weights. These will be multiplied by the encoder output vectors to create a weighted combination. The result (called attn_applied in the code) should contain information about that specific part of the input sequence, and thus help the decoder choose the right output words.First, we represent the process which generating $$\bf z$$ from $$\bf X$$ by the function $$\Lambda$$: CEL-Seq provides its first single-cell, on-chip barcoding method, and we detected gene expression changes accompanying the progression through the cell cycle in mouse fibroblast cells. The pipeline consists of the following steps: (1) demultiplexing.. 64 bytes from 192.168.100.102: icmp_seq=1 ttl=127 time=1198 ms 64 bytes from 192.168.100.102: icmp_seq=2 ttl=127 time=184 ms 64 bytes from 192.168.100.102: icmp_seq=3 ttl=127 time=955 ms 64 bytes from 192.168.100.102: icmp_seq=4 ttl=127 time=6.12 ms ^C An important point we should keep in mind that the decoder predicts the sentences based on all the words before the current word. This can be demonstrated easily by the example taken above i.e. “I like cats more than dogs” has to be mapped to “私は犬よりも猫が好き” by the network. Here we train our model to predict that “犬” comes after “私は” when we feed the source sentence as “I like cats more than dogs”. Towards our left is the encoder, and towards our right is the decoder. The inputs are given as embeddings of the input sequence, and the initial inputs to the decoder are the embeddings of the outputs up to that point.In other words, the information of $$\bf X$$ is conveyed by $$\bf z$$, and $$P_{\theta}({\bf y}_j|{\bf Y}_{<j}, {\bf X})$$ is actually calculated by $$P_{\theta}({\bf y}_j|{\bf Y}_{<j}, {\bf z})$$.Multi-head attention network cannot automatically make use of the word’s position, unlike RNNs. Also, without positional encoding the output of multi-head attention block for “I like cats more than dogs” and “I like dogs more than cats” would be more or less the same. ### What loss function should I use to score a seq2seq RNN model 1. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. 2. About Seq2Seq with Attention and Beam Search Seq2Seq for LaTeX generation - part I Nov 8, 2017 3. This post is the first in a series about im2latex: its goal is to cover the concepts of Sequence-to-Sequence models with Attention and Beam search. If you're already familiar with Seq2Seq and want to go straight to the Tensorflow code. > Go to part II. Introduction 4. This process works fine for shorter sentences, but as the length of the sentence increases the performance slumps. It is, therefore, difficult for a recurrent neural network to memorize a rather long sentence. The attention mechanism helps us overcome this difficulty. Let’s see how. 5. src_seq - Pointer to the approximated Freeman chain that can refer to other chains. storage - Storage location for the resulting polylines. method - Approximation method (see the description of the function FindContours() ) 6. In CNV-seq the mapping is performed by sequence alignment. 4. In microarray the light intensities reflect the number of hybridized fragments. A major assumption in CNV-seq is that shotgun sampling of DNA fragments is random, and therefore the CNV calls made by CNV-seq are not due to different.. ### Sequence Modeling With Neural Networks (Part 1): Language - Indic 1. The network now attends over the preceding decoder states and does a similar task as decoder hidden state which we see in trivial Machine Translation architectures. 2. ... # source : We knew the Government had tried many things , like launching <UNK> with <UNK> or organising speed dating evenings . # result : Nous savions que le gouvernement avait <UNK> plusieurs fois , comme le <UNK> <UNK> , le <UNK> ou le <UNK> <UNK> . # expect : Nous savions que le gouvernement avait tenté plusieurs choses comme lancer des parfums aux <UNK> ou organiser des soirées de <UNK> ... 3. Reference¶ [1] Sequence to Sequence Learning with Neural Networks 3. Draft saved Draft discarded Sign up or log in Sign up using Google Sign up using Facebook Sign up using Email and Password Submit Post as a guest Name Email Required, but never shown 4. Even after having trained the model, it can happen that the model makes a small error (and gives a small advantage to vas over comment for the first step of the decoding). This would mess up the entire decoding… 5. Next, let’s think about the conditional probability $$P({\bf Y}|{\bf X})$$ generating the output sequence $$\bf Y$$ when the input sequence $$\bf X$$ is given. The purpose of seq2seq model is modeling the probability $$P({\bf Y}|{\bf X})$$. However, the seq2seq model does not model the probability $$P({\bf Y}|{\bf X})$$ directly. Actually, it models the probability $$P({\bf y}_j|{\bf Y}_{<j}, {\bf X})$$, which is the probability of generating the $$j$$-th element of the output sequence $${\bf y}_j$$ given the $${\bf Y}_{<j}$$ and $${\bf X}$$. $${\bf Y}_{<j}$$ means the output sequence from $$1$$ to $$j-1$$, or $$({\bf y}_j)_{j=1}^{j-1}$$. In this notation, you can write the model $$P_{\theta}({\bf Y}|{\bf X})$$ with the product of $$P_{\theta}({\bf y}_j|{\bf Y}_{<j}, {\bf X})$$: 6. Le MNase-Seq est une technique utilisée en biologie moléculaire pour identifier les régions de la chromatine occupées par des nucléosomes à l'échelle du génome. Cette technique repose sur le séquençage à haut débit de fragments issus de la digestion par MNase What is NLP? NLP or Natural Language Processing is one of the popular branches of Artificial Intelligence that helps computers understands, manipulate or respond to a human in their natural language. NLP is the engine behind Google Translate that helps us understand other languages The official Chainer repository includes a neural machine translation example using the seq2seq model. We will now provide an overview of the example and explain its implementation in detail. chainer/examples/seq2seqThe previous model has been refined over the past few years and greatly benefited from what is known as attention. Attention is a mechanism that forces the model to learn to focus (=to attend) on specific parts of the input sequence when decoding, instead of relying only on the hidden vector of the decoder’s LSTM. One way of performing attention is explained by Bahdanau et al.. We slightly modify the reccurrence formula that we defined above by adding a new vector c_t $to the input of the LSTMThere indeed are 2 main ways of performing decoding at testing time (translating a sentence for which we don’t have a translation). The first of these methods is the one covered at the beginning of the article: greedy decoding. It is the most natural way and it consists in feeding to the next step the most likely word predicted at the previous step. CREATE SEQUENCE Order_seq START WITH 1 INCREMENT BY 1 NOMAXVALUE NOCYCLE CACHE 20 This CREATE SEQUENCE statement creates the seq2 sequence so that 50 values of the sequence are stored in the SEQUENCE cache This type of seq2seq model has shown impressive performance in various other tasks such as speech recognition, machine translation, question answering, Neural Machine Translation (NMT), and image caption generation. The following diagram helps you visualize the seq2seq mode In this article, we’re going to dive deep into the mighty Transformer, dissecting its architecture, and comparing it with the traditional LSTM approaches, to see how it outperforms such models. import seq2seq from seq2seq.models import SimpleSeq2Seq. (4)Peeky Seq2seq model. the decoder gets a 'peek' at the context vector at every timestep. 打开peek=True，类似于上述的模式三 We built tf-seq2seq with the following goals in mind: General Purpose: We initially built this framework for Machine Translation, but have since used it for a variety of other tasks, including Summarization, Conversational Modeling, and Image Captioning. As long as your problem can be phrased as.. output_words, attentions = evaluate( encoder1, attn_decoder1, "je suis trop froid .") plt.matshow(attentions.numpy()) For a better viewing experience we will do the extra work of adding axes and labels:If we use the predicted token as input to the next step during training (as explained above), errors would accumulate and the model would rarely be exposed to the correct distribution of inputs, making training slow or impossible. To speedup things, one trick is to feed the actual output sequence (<sos> comment vas tu) into the decoder’s LSTM and predict the next token at every position (comment vas tu <eos>). # These two do the same thing; all data points outside the graphing range are # dropped, resulting in a misleading box plot bp + ylim(5, 7.5) Specify tick marks directly bp + coord_cartesian(ylim=c(5, 7.5)) + scale_y_continuous(breaks=seq(0, 10, 0.25)) # Ticks from 0-10, every .25 For pair-end RNA-seq, there are two different ways to strand reads (such as Illumina ScriptSeq protocol) For example, differential expression can be easily checked by comparing two RNA-seq tracks using genome browser. However, one must make sure that all samples are comparable before.. def __init__(self, n_layers, n_source_vocab, n_target_vocab, n_units): super(Seq2seq, self).__init__() with self.init_scope(): self.embed_x = L.EmbedID(n_source_vocab, n_units) self.embed_y = L.EmbedID(n_target_vocab, n_units) self.encoder = L.NStepLSTM(n_layers, n_units, n_units, 0.1) self.decoder = L.NStepLSTM(n_layers, n_units, n_units, 0.1) self.W = L.Linear(n_units, n_target_vocab) self.n_layers = n_layers self.n_units = n_units When we instantiate this class for making a model, we give the number of stacked lstms to n_layers, the vocabulary size of the source language to n_source_vocab, the vocabulary size of the target language to n_target_vocab, and the size of hidden vectors to n_units. In the vanilla Seq2Seq model, the encoder representation is just a vector, whose length is the same as the hidden size of the RNN. AllenNLP provides a very convenient Seq2SeqEncoder abstraction. You can initialize this by passing PyTorch's RNN modules, as i Now, let us think of how a human being, that understands multiple languages, translates them. He takes a sentence, breaks it into parts, and then finally translates the parts rather than memorizing the whole sentence and then translating it. Small RNA-Seq Library Prep Kit for Illumina. Expression Profiling Library Prep Kits. QuantSeq-Flex Targeted RNA-Seq Library Prep Kit V2 with First Strand Synthesis Module (Cat. No. 033) using oligodT or target-specific first strand synthesis priming and random primed second strand synthesis Now that we have a vector$ e $that captures the meaning of the input sequence, we’ll use it to generate the target sequence word by word. Feed to another LSTM cell:$ e $as hidden state and a special start of sentence vector$ w_{sos} $as input. The LSTM computes the next hidden state$ h_0 \in \mathbb{R}^h $. Then, we apply some function$ g : \mathbb{R}^h \mapsto \mathbb{R}^V $so that$ s_0 := g(h_0) \in \mathbb{R}^V \$ is a vector of the same size as the vocabulary.

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