neural-language-model | neural language models , in particular Collobert | Machine Learning library
kandi X-RAY | neural-language-model Summary
kandi X-RAY | neural-language-model Summary
Approach based upon language model in Bengio et al ICML 09 "Curriculum Learning". You will need my common python library: and my textSNE wrapper for t-SNE: You will need Murmur for hashing. easy_install Murmur. To train a monolingual language model, probably you should run: [edit hyperparameters.language-model.yaml] ./build-vocabulary.py ./train.py. To train word-to-word multilingual model, probably you should run: cd scripts; ln -s hyperparameters.language-model.sample.yaml s hyperparameters.language-model.yaml. TODO: * sqrt scaling of SGD updates * Use normalization of embeddings? * How do we initialize embeddings? * Use tanh, not softsign? * When doing SGD on embeddings, use sqrt scaling of embedding size?.
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Top functions reviewed by kandi - BETA
- Train the model
- Print the pre - hidden posterior probabilities
- Embed a sequence
- Compute the verbose prediction
- Validate a translation model
- Validate the error and noise score
- Convert the given ebatch into a list of aligned sequences
- Return the language of the given id
- Return the wordmap
- Corrupts an example
- Return a list of weighted weights
- Get the targetmap
- Returns the targetmap filename
- Validate the given sequence
- Predict the prediction
- Write a new targetmap to the targetmap
- Return the name of the wordmap file
- Returns the directory of the language - model
- Corrupts the given model
- Get the index of the language model
- Get examples from a file
- Convert an ebatch to a two - dimensional list
- Save the model to disk
- Loads a new model from disk
- Return word form
neural-language-model Key Features
neural-language-model Examples and Code Snippets
Community Discussions
Trending Discussions on neural-language-model
QUESTION
As part of my thesis, I am trying to build a recurrent Neural Network Language Model.
From theory, I know that the input layer should be a one-hot vector layer with a number of neurons equal to the number of words of our Vocabulary, followed by an Embedding layer, which, in Keras, it apparently translates to a single Embedding layer in a Sequential model. I also know that the output layer should also be the size of our vocabulary so that each output value maps 1-1 to each vocabulary word.
However, in both the Keras documentation for the Embedding layer (https://keras.io/layers/embeddings/) and in this article (https://machinelearningmastery.com/how-to-develop-a-word-level-neural-language-model-in-keras/#comment-533252), the vocabulary size is arbitrarily augmented by one for both the input and the output layers! Jason gives an explenation that this is due to the implementation of the Embedding layer in Keras but that doesn't explain why we would also use +1 neuron in the output layer. I am at the point of wanting to order the possible next words based on their probabilities and I have one probability too many that I do not know to which word to map it too.
Does anyone know what is the correct way of acheiving the desired result? Did Jason just forget to subtrack one from the output layer and the Embedding layer just needs a +1 for implementation reasons (I mean it's stated in the official API)?
Any help on the subject would be appreciated (why is Keras API documentation so laconic?).
Edit:
This post Keras embedding layer masking. Why does input_dim need to be |vocabulary| + 2? made me think that Jason does in fact have it wrong and that the size of the Vocabulary should not be incremented by one when our word indices are: 0, 1, ..., n-1.
However, when using Keras's Tokenizer our word indices are: 1, 2, ..., n. In this case, the correct approach is to:
Set
mask_zero=True, to treat 0 differently, as there is never a 0 (integer) index input in the Embedding layer and keep the vocabulary size the same as the number of vocabulary words (n)?Set
mask_zero=Truebut augment the vocabulary size by one?Not set
mask_zero=Trueand keep the vocabulary size the same as the number of vocabulary words?
ANSWER
Answered 2020-May-04 at 17:46the reason why we add +1 leads to the possibility that we can encounter a chance to see an unseen word(out of our vocabulary) during testing or in production, it is common to consider a generic term for those UNKNOWN and that is why we add a OOV word in front which resembles all out of vocabulary words.
Check this issue on github which explains it in detail:
https://github.com/keras-team/keras/issues/3110#issuecomment-345153450
QUESTION
I've seen the multitude of questions about this particular error. I believe my question is different enough to warrant its own post.
My objective: I am building an RNN that generates news headlines. It will predict the next word based on the words that came before it. This code is from an example and I am trying to adapt it to work for my situation. I am trying to slice the array into an X and y.
The issue:
I understand that the error appears because the array is being indexed as if it were a 2d array, but it is actually a 1d array. Before converting sequences to an array, it is a list of lists, but not all of the nested lists are the same length so numPy converts it to a 1d array.
My question(s): Is there a simple or elegant way to pad sequences so that all of the lists are the same length? Can I do this using spaces to keep the same meaning in the shorter headlines? Why do I need to change the list of lists to an array at all? As I said before, this is from an example and I am trying to understand what they did and why.
ANSWER
Answered 2019-Dec-02 at 01:48Problem is that this tutorial has few parts on one page and every part has own "Complete Example"
First "Complete Example" reads text from republic_clean.txt, clear it and save it in republic_sequences.txt - it creates sequences with the same number of words.
Second "Complete Example" reads text from republic_sequences.txt and use it with
QUESTION
I have trouble running the code from this this neural language model tutorial. It seems that I cannot import the relevant packages from keras.models although I have installed keras and tensorflow.
I also tried to run the import command in the Windows console. There the error message says something about "the CPU supports instructions that this TensorFlow binary was not compiled to use".
d) Error message in windows console
Background info: I am using Spyer 3.2.3 and have installed python 3.6.0.
Could you please help me to find out what the issue is?
Thank you, very much appreciated!
...ANSWER
Answered 2017-Nov-11 at 18:10Dense is not a model. Dense is a layer, and it's in keras.layers:
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Install neural-language-model
You can use neural-language-model 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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