tf-seq2seq | Sequence to sequence learning using TensorFlow | Translation library
kandi X-RAY | tf-seq2seq Summary
kandi X-RAY | tf-seq2seq Summary
Sequence to sequence learning using TensorFlow.
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Top functions reviewed by kandi - BETA
- Builds the model
- Build a single cell
- Build the decoder cell
- Builds the decoder and attention layer
- Segment a sentence
- Return a set of pair pairs from a word
- Encode a string using bpe_codes
- Train the model
- Check that the inputs are valid
- Load the inverse dictionary
- Load a dictionary from file
- Calculate the F1 score
- Prune stats based on a given threshold
- Runs the prediction
- Extract n - grams from a string
- Calculate pairwise pairs
- Replaces the pair of pairs in the vocabulary
- Evaluate the model
- Create argument parser
- Calculates the correct and total number of comparisons
- Get the vocabulary
- Calculate statistics for a given pair
tf-seq2seq Key Features
tf-seq2seq Examples and Code Snippets
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 tf-seq2seq
QUESTION
Although not new to Machine Learning, I am still relatively new to Neural Networks, more specifically how to implement them (In Keras/Python). Feedforwards and Convolutional architectures are fairly straightforward, but I am having trouble with RNNs.
My X
data consists of variable length sequences, each data-point in that sequence having 26 features. My y
data, although of variable length, each pair of X
and y
have the same length, e.g:
ANSWER
Answered 2018-Aug-26 at 11:59The problem is that the decoder_gru
layer does not return its state, therefore you should not use _
as the return value for the state (i.e. just remove , _
):
QUESTION
I am using the following implementation of the Seq2Seq
model. Now, if I want to pass some inputs and get the corresponding values of encoder's hidden state (self.encoder_last_state), how can I do it?
https://github.com/JayParks/tf-seq2seq/blob/master/seq2seq_model.py
...ANSWER
Answered 2017-Oct-08 at 19:39You need to first assemble input_feed
, similar to the predict routine. Once you have that, just execute sess.run over the required hidden layer.
To assmeble the input_feed:
QUESTION
I'm going to train a seq2seq model using tf-seq2seq package by 1080 ti (11GB) GPU. I always get the following error using different network's size (even nmt_small):
...ANSWER
Answered 2017-Apr-18 at 09:36you should notice some tips:
1- use memory growth, from tensorflow document: "in some cases it is desirable for the process to only allocate a subset of the available memory, or to only grow the memory usage as is needed by the process. TensorFlow provides two Config options on the Session to control this."
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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No vulnerabilities reported
Install tf-seq2seq
You can use tf-seq2seq 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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