seq2seq | Sequence to Sequence Learning with Keras | Machine Learning library
kandi X-RAY | seq2seq Summary
kandi X-RAY | seq2seq Summary
Sequence to Sequence Learning with Keras. Hi! You have just found Seq2Seq. Seq2Seq is a sequence to sequence learning add-on for the python deep learning library Keras. Using Seq2Seq, you can build and train sequence-to-sequence neural network models in Keras. Such models are useful for machine translation, chatbots (see [4]), parsers, or whatever that comes to your mind.
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Top functions reviewed by kandi - BETA
- Creates a Seq2 Seq2 Seq2 sequence .
- Creates an attention sequence .
- Creates a sequence of sequential sequences .
- Builds the model .
- Sets the constructor .
seq2seq Key Features
seq2seq Examples and Code Snippets
Lines of Code : 61
License : No License
CopyIteration 1
Train on 382617 samples, validate on 42513 samples
Epoch 1/10
382617/382617 [==============================] - 216s - loss: 0.8973 - acc: 0.6880 - val_loss: 0.3011 - val_acc: 0.8997
Epoch 2/10
382617/382617 [============================== Training...
0 Train loss: 0.21926558017730713 Test loss: 0.22550159692764282
1000 Train loss: 0.0022761737927794456 Test loss: 0.0024939212016761303
2000 Train loss: 0.0004760705924127251 Test loss: 0.000556636 0 0 ... 0 23 12 121 832 || 2 3432 898 7 323
0 0 ... 0 43 98 233 323 || 7 4423 833 1 232
0 0 ... 0 32 44 133 555 || 2 4534 545 6 767
---
0 0 ... 0 23 12 121 832 || 2 3432 898 7
0 0 ... 0 23 12 121 832 || 2 3432 898 7
0 0 ... 0 23 12 121 832 || 2 3432 '''
Pedagogical example realization of seq2seq recurrent neural networks, using TensorFlow and TFLearn.
More info at https://github.com/ichuang/tflearn_seq2seq
'''
from __future__ import division, print_function
import os
import sys
import tflearn
def raw_rnn(cell,
loop_fn,
parallel_iterations=None,
swap_memory=False,
scope=None):
"""Creates an `RNN` specified by RNNCell `cell` and loop function `loop_fn`.
**NOTE: This method is still in tes def _create_loss(self):
print('Creating loss... \nIt might take a couple of minutes depending on how many buckets you have.')
start = time.time()
def _seq2seq_f(encoder_inputs, decoder_inputs, do_decode):
setattr(t Community Discussions
Trending Discussions on seq2seq
QUESTION
I am working on an NLP project using Seq2Seq. I created a data frame from my dataset then created a batch iterator using data loader, see the following code:
...ANSWER
Answered 2022-Mar-17 at 20:13You can redefine __getitem__ in your Dataset to return a dictionary:
QUESTION
I am writing a seq2seq and would like to keep only three checkpoints; I thought I was implementing this with
...ANSWER
Answered 2022-Mar-08 at 07:10Hmm maybe you should try restoring your checkpoint every time you begin training again:
QUESTION
Building a seq2seq based on tfa.seq2seq, basically works like in https://www.tensorflow.org/addons/tutorials/networks_seq2seq_nmt#train_the_model. I am looking at the nature of the outputs when calling a BasicDecoder. I create an instance of decoder
ANSWER
Answered 2022-Mar-01 at 11:58Using class GreedyEmbeddingSampler(Sampler): for inference https://github.com/tensorflow/addons/blob/v0.15.0/tensorflow_addons/seq2seq/sampler.py#L559-L650
QUESTION
I am following quite closely the Seq2seq for translation tutorial here https://www.tensorflow.org/addons/tutorials/networks_seq2seq_nmt#define_the_optimizer_and_the_loss_function while testing on other data. I meet an error when instantiating the Encoder which is defined as
...ANSWER
Answered 2022-Feb-27 at 18:15This error occurs when you have a sequence which contains integer values outside the range of the defined vocabulary size. You can reproduce your error with the following example, because the vocabulary size of the Embedding layer is 106, meaning sequences can have values between 0 and 105 and I pass a random sequence with values between 0 and 200 to enforce an error:
QUESTION
I have a simple transformers script looking like this.
...ANSWER
Answered 2022-Feb-22 at 11:54Use this model instead.
QUESTION
I'm training a Seq2Seq model on Tensorflow on a ml.p3.2xlarge instance. When I tried running the code on google colab, the time per epoch was around 40 mins. However on the instance it's around 5 hours!
This is my training code
...ANSWER
Answered 2021-Aug-13 at 16:35If you're using SageMaker Notebook instance. Open a terminal and run nvidia-smi to see the GPU utilization rate. If you it's 0% then you're not using the right device. If it's more than 0% but very far from 100%, then you have a non GPU bottleneck to handle.
If you're using SageMaker training, then check the GPU usage via Cloudwatch metrics for the job.
QUESTION
I am trying to train a seq2seq model for language translation, and I am copy-pasting code from this Kaggle Notebook on Google Colab. The code is working fine with CPU and GPU, but it is giving me errors while training on a TPU. This same question has been already asked here.
Here is my code:
...ANSWER
Answered 2021-Nov-09 at 06:27Need to down-grade to Keras 1.0.2 If works then great, otherwise I will tell other solution.
QUESTION
I am trying to follow this guide to implement a seq2seq machine tranlsation model: https://www.tensorflow.org/tutorials/text/nmt_with_attention
The tutorial's Encoder has an initialize_hidden_state() function that is used to generate all 0 as initial state for the encoder. However I am a bit confused as to why this is neccessary. As far as I can tell, the only times when encoder is called (in train_step and evaluate), they were initialized with the initialize_hidden_state() function. My questions are 1.) what is the purpose of this initial state? Doesn't Keras layer automatically initialize LSTM states to begin with? And 2.) why not always just initialize the encoder with all 0 hidden states if encoder is always called with initial states generated by initialize_hidden_state()?
ANSWER
Answered 2021-May-16 at 18:34you are totally right. The code in the example is a little misleading. The LSTM cells are automatically initialized with zeros. You can just delete the initialize_hidden_state() function.
QUESTION
Is there a parameter that I can set in the config file (maybe for the trainer?) that would save the model (archive) after each epoch or after a specific number of steps? I'm using seq2seq dataloader and "composed_seq2seq" as my model. This is how my trainer looks like currently:
...ANSWER
Answered 2021-May-06 at 23:03Can you explain a little more about what you're trying to do with a model from every epoch/some number of steps? I think it already archives the model every time it gets a new best score, so I'm wondering what you want to do that can't be accomplished with that.
Edit:
It looks like AllenNLP already saves a model every epoch, but it only keeps a maximum of 2 by default. I believe you can change that by adding a checkpointer to your training config, e.g.:
QUESTION
I would like to use bert for tokenization and also indexing for a seq2seq model and this is how my config file looks like so far:
...ANSWER
Answered 2021-Apr-29 at 17:28- Please set
add_special_tokens = False. - Use
tokenizer.convert_tokens_to_string(which takes the list of subword tokens as input), wheretokenizerrefers to the tokenizer used by your DatasetReader.
Please let us know if you have further questions!
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