Neural-Machine-Translation | Several basic neural machine translation models | Translation library
kandi X-RAY | Neural-Machine-Translation Summary
kandi X-RAY | Neural-Machine-Translation Summary
This repository implemented various kinds of Neural Machine Translation models with Pytorch and TensorFlow. **There are 10k training data and 0.1k validation&test data in the file: ./data/. (19 Nov. 2018) // Adding basic seq2seq model (./seq2seq/seq2seq). (21 Nov. 2018) // Adding seq2seq + attn model (./seq2seq/seq2seq_att). (26 Nov. 2018) // Adding transformer-simple model (./transformer/transformer-pt-v1). (26 Nov. 2018) // Adding transformer-simple tf-version (./transformer/transformer-tf-v1). (01 Dev. 2018) // Adding GAN-NMT (./Adversarial-NMT). (19 Jun. 2019) // Adding transformer model (./transformer/transformer-pt-v2).
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Top functions reviewed by kandi - BETA
- Translate batch
- Computes the tensor product of the network
- Get the hypothesis for a given node
- Advance the beam
- Train the model
- Load de - vocab
- Get a random batch of indices
- Multihead attention layer
- Normalize inputs
- Load data from training data
- Read instance from file
- Forward layer to src_seq
- Generate vocabulary
- Compute embedding
- Preprocess two texts
- Calculate the alignments
- Compute the LSTM
- Collate pairwise pairwise pairs
- Compute the attention layer
- Prepare data loader
- Forward the decoder
- Build vocabulary index
- Feed forward layer
- Train Iters
- Forward forward attention
- Evaluate inference
Neural-Machine-Translation Key Features
Neural-Machine-Translation Examples and Code Snippets
Community Discussions
Trending Discussions on Neural-Machine-Translation
QUESTION
I get always 100% training and validation accuracies. Here's how it looks:
...ANSWER
Answered 2020-Jun-10 at 12:39You initialize decoder_targets_one_hot as vectors of zeros, but do not set the index of true class as 1 anywhere. So, basically the target vectors are not one-hot vectors. The model tries to learn same target for all inputs, i.e. the vector of zeros.
QUESTION
I have a for loop with excerpts of try-except blocks referring to https://machinetalk.org/2019/03/29/neural-machine-translation-with-attention-mechanism/?unapproved=67&moderation-hash=ea8e5dcb97c8236f68291788fbd746a7#comment-67:-
ANSWER
Answered 2019-Jul-01 at 07:47It may be a problem with nested loops, as covered by this answer. They suggest using return, but then your loop would need to be written as a function. If that doesn't appeal you could try using various levels of break statements as shown in some of the answers. Using the for, else construction (explained here), I think your code would look like the following
QUESTION
I am building a toy encoder-decoder model for machine translation by using Tensorflow.
I use Tensorflow 1.8.0 cpu version. FastText pretrained word vector of 300 dimension is used in the embedding layer. Then the batch of training data goes through encoder and decoder with attention mechanism. In training stage decoder uses the TrainHelper and in inference stage GreedyEmbeddingHelper is used.
I already ran the model successfully by using a bidirectional LSTM encoder. However when I try to further improve my model by using multilayer LSTM, the bug arises. The code to build the training stage model is below:
...ANSWER
Answered 2018-Jun-16 at 10:52Use the following method to define a list of cell instances,
QUESTION
For some self-studying, I'm trying to implement simple a sequence-to-sequence model using Keras. While I get the basic idea and there are several tutorials available online, I still struggle with some basic concepts when looking these tutorials:
- Keras Tutorial: I've tried to adopt this tutorial. Unfortunately, it is for character sequences, but I'm aiming for word sequences. There's is a block to explain the required for word sequences, but this is currently throwing "wrong dimension" errors -- but that's OK, probably some data preparation errors from my side. But more importantly, in this tutorial, I can clearly see the 2 types of input and 1 type of output:
encoder_input_data,decoder_input_data,decoder_target_data - MachineLearningMastery Tutorial: Here the network model looks very different, completely sequential with 1 input and 1 output. From what I can tell, here the decoder gets just the output of the encoder.
Is it correct to say that these are indeed two different approaches towards Seq2Seq? Which one is maybe better and why? Or do I read the 2nd tutorial wrongly? I already got an understanding in sequence classification and sequences labeling, but with sequence-to-sequence it hasn't properly clicked yet.
...ANSWER
Answered 2018-Feb-11 at 03:12Yes, those two are different approaches and there are other variations as well. MachineLearningMastery simplifies things a bit to make it accessible. I believe Keras method might perform better and is what you will need if you want to advance to seq2seq with attention which is almost always the case.
MachineLearningMastery has a hacky workaround that allows it to work without handing in decoder inputs. It simply repeats the last hidden state and passes that as the input at each timestep. This is not a flexible solution.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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Install Neural-Machine-Translation
You can use Neural-Machine-Translation like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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