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textgenrnn

I am supposed to get 5 lines in a file in the current folder. But that does not happen, instead, I get. Wonder what's wrong. Learn more. Asked 1 year, 7 months ago. Active 1 year, 7 months ago. Viewed times. I'm having trouble saving the textgenrnn generated text to a file. Salman Farsi Salman Farsi 1 1 silver badge 11 11 bronze badges. Active Oldest Votes. Fixed it. Sign up or log in Sign up using Google.

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textgenrnn

Stack Overflow works best with JavaScript enabled.Easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code, or quickly train on a text using a pretrained model. You can play with textgenrnn and train any text file with a GPU for free in this Colaboratory Notebook! Read this blog post or watch this video for more information! The included model can easily be trained on new texts, and can generate appropriate text even after a single pass of the input data.

The model weights are relatively small 2 MB on diskand they can easily be saved and loaded into a new textgenrnn instance. As a result, you can play with models which have been trained on hundreds of passes through the data. It's also possible to get involved in how the output unfolds, step by step. N defaults to 3. This can add a human touch to the output; it feels like you're the writer! You can view a demo of common features and model configuration options in this Jupyter Notebook.

The included pretrained-model follows a neural network architecture inspired by DeepMoji. For the default model, textgenrnn takes in an input of up to 40 characters, converts each character to a D character embedding vector, and feeds those into a cell long-short-term-memory LSTM recurrent layer.

Those outputs are then fed into another cell LSTM. That output is mapped to probabilities for up to different characters that they are the next character in the sequence, including uppercase characters, lowercase, punctuation, and emoji.

Alternatively, if context labels are provided with each text document, the model can be trained in a contextual mode, where the model learns the text given the context so the recurrent layers learn the decontextualized language. The text-only path can piggy-back off the decontextualized layers; in all, this results in much faster training and better quantitative and qualitative model performance than just training the model gien the text alone.

The model weights included with the package are trained on hundreds of thousands of text documents from Reddit submissions via BigQueryfrom a very diverse variety of subreddits. The network was also trained using the decontextual approach noted above in order to both improve training performance and mitigate authorial bias.

When fine-tuning the model on a new dataset of texts using textgenrnn, all layers are retrained. However, since the original pretrained network has a much more robust "knowledge" initially, the new textgenrnn trains faster and more accurately in the end, and can potentially learn new relationships not present in the original dataset e.

Additionally, the retraining is done with a momentum-based optimizer and a linearly decaying learning rate, both of which prevent exploding gradients and makes it much less likely that the model diverges after training for a long time. Results will vary greatly between datasets. Because the pretrained neural network is relatively small, it cannot store as much data as RNNs typically flaunted in blog posts. For best results, use a dataset with at least 2, documents.

Even then, there is currently no good heuristic for determining a "good" model. A web-based implementation using tensorflow. A mode to allow the model architecture to be used for chatbot conversations may be released as a separate project.

A larger pretrained network which can accommodate longer character sequences and a more indepth understanding of language, creating better generated sentences. Max's open-source projects are supported by his Patreon. If you found this project helpful, any monetary contributions to the Patreon are appreciated and will be put to good creative use. Andrej Karpathy for the original proposal of the char-rnn via the blog post The Unreasonable Effectiveness of Recurrent Neural Networks.

Daniel Grijalva for contributing an interactive mode.These were all made with neural networksa type of AI modeled on the network-like nature of our own brains. You train a neural network by giving it input: recipes, for example. The network strengthens some of the connections between its neurons imitation brain cells more than others as it learns.

Once the network is trained, you can ask it to generate its own output, or to give it a partial input and ask it to fill in the rest. It knows that beer can be an ingredient, and that things can be cut into cubes, but nobody has ever told it that beer is not one of those things. The outputs that look almost right, but misunderstand some fundamental rule, are often the most hilarious. I was happy to just watch these antics from afar, until Shane mentioned on Twitter that a middle school coding class had generated better ice cream names than she had.

And I thought, if kids can do this, I can do this. I started with the same toolkit Shane used for ice cream flavors: a python module called textgenrnnby Max Woolf of Buzzfeed.

The middle school class started with a list of thousands of ice cream flavors, for example. Whatever you choose, get it into a text file with one item per line. Asking nicely is an underrated coding skill. Create a folder for your new project, and write two scripts. First, one called train.

This script will get the neural net reading your input and thinking about what its rules must be. The script has a couple things you can modify:. It takes a while to train the network. This is the fun part! The script above will give you 20 fun new things to look at.

The important parts of that last line are:. Not every idea your neural network comes up with will be comedy gold. Here are some of the better Lifehacker headlines that my AI came up with:. I got these by playing with the temperature and the number of training epochs, and every time I saw something I liked I copied it into a text file of my favorites.

I also experimented with the word-by-word version of the algorithm; the scripts above use the default character-by-character model. My final list of headlines includes results from both. Sometimes it might get so good at guessing the rules of your data set that it just recreates the same things you fed it—the AI version of plagiarism. Botnik studios pairs people with machines by training predictive-text keyboards. She photoshopped out the sheep, and realized the AI was just looking for white blobs in grass.

If she colored the sheep orange, the AI thought they were flowers.Want to generate text with little trouble, and without building and tuning a neural network yourself? Let's check out a project which allows you to "easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code. Generating text is one of those projects that seems like a lot of fun to machine learning and NLP beginners, but one which is also pretty daunting.

Or, at least it was for me. Thankfully, there are all sorts of great materials online for learning how RNNs can be used for generating text, ranging from the theoretical to the technically in-depth to those decidedly focused on the practical. There are also some very good posts which cover it all and are now considered canon in this space.

All of these materials share one thing in particular: at some point along the way, you have to build and tune an RNN to do the work. While this is a obviously a worthwhile undertaking, especially for the sake of learning, what if you are OK with a much higher level of abstraction, whatever your reason may be?

What if you are a data scientist that requires a building block in the form of an RNN text generator to plug into your project? Or, what if, as a newcomer, you simply want to get your hands a bit -- but not too -- dirty, as a means of testing the water or as motivation to dig down further? In that vein, let's take a look at textgenrnna project which allows you to "easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code.

The network architecture uses attention-weighting and skip-embedding for accelerated training and improved quality, and allows for the tuning of a number of hyperparameters, such as RNN size, RNN layers, and the inclusion of bidirectional RNNs. You can read more about textgenrnn and its features and architecture at its Github repo or in this introductory blog post. Since the "Hello, World! I grabbed a selection of Donald Trump's tweets -- Jan 1, - Jun 11, yesterday, at time of writingwhich clearly includes tweets from both before and after his inauguration as President of the United States -- from Trump Twitter Archivea site which makes querying and downloading tweets from the President painless.

I chose only to grab the text from the tweets in that date range, since I don't care about any of the metadata, and saved it to a text file I appropriately called trump-tweets. Let's see how uncomplicated it is to generate text with textgenrnn. The following 4 lines are all we need to import the library, create a text generation object, train the model on the trump-tweets.

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Want to play with temperature the textgenrnn default is 0. Let's try it out:. Well, that's less convincing. How about something more conservative, which the model is more confident of:.

Of course, this isn't perfect. There are all sorts of other things we could have tried, and the good news is that, if you don't want to implement your own solution, textgenrnn can be used to perform many of these things again, see the Github repo :.

Kind of fun. I'm interested in seeing how a default textgenrnn model performs out-of-the-box against a custom, well-tuned model.

Maybe something for next time. By subscribing you accept KDnuggets Privacy Policy.

Subscribe to KDnuggets News. By Matthew MayoKDnuggets. My FoxNews will be self finally complaining about me that so he is a great day and companies and is starting to report the president in safety and more than any mention of the bail of the underaches to the construction and freedom and efforts the politicians and expensive meetings should have bee The world will be interviewed on foxandfriends at pm.

A the best and people in the biggest! Thank you!Released: Feb 2, Easily train your own text-generating neural network of any size and complexity. View statistics for this project via Libraries. Tags deep learning, tensorflow, keras, text generation. Easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code, or quickly train on a text using a pretrained model. Feb 2, Jan 9, Oct 26, Aug 8, Aug 4, Jun 6, May 7, May 5, May 4, Apr 30, Apr 16, Aug 6, Download the file for your platform.

If you're not sure which to choose, learn more about installing packages. Warning Some features may not work without JavaScript. Please try enabling it if you encounter problems. Search PyPI Search. Latest version Released: Feb 2, Navigation Project description Release history Download files.

Project links Homepage. Maintainers minimaxir. Project description Project details Release history Download files Project description Easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code, or quickly train on a text using a pretrained model.

A modern neural network architecture which utilizes new techniques as attention-weighting and skip-embedding to accelerate training and improve model quality. Able to train on and generate text at either the character-level or word-level. Able to train on any generic input text file, including large files.

Able to train the model using contextual labels, allowing it to learn faster and produce better results in some cases. Able to generate text interactively for customized stories. Project details Project links Homepage. Download files Download the file for your platform.Homepage PyPI Python. Easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code, or quickly train on a text using a pretrained model.

You can play with textgenrnn and train any text file with a GPU for free in this Colaboratory Notebook! Read this blog post or watch this video for more information!

The included model can easily be trained on new texts, and can generate appropriate text even after a single pass of the input data. The model weights are relatively small 2 MB on diskand they can easily be saved and loaded into a new textgenrnn instance. As a result, you can play with models which have been trained on hundreds of passes through the data.

It's also possible to get involved in how the output unfolds, step by step. N defaults to 3. This can add a human touch to the output; it feels like you're the writer! You can view a demo of common features and model configuration options in this Jupyter Notebook. The included pretrained-model follows a neural network architecture inspired by DeepMoji. For the default model, textgenrnn takes in an input of up to 40 characters, converts each character to a D character embedding vector, and feeds those into a cell long-short-term-memory LSTM recurrent layer.

Those outputs are then fed into another cell LSTM. That output is mapped to probabilities for up to different characters that they are the next character in the sequence, including uppercase characters, lowercase, punctuation, and emoji.

Alternatively, if context labels are provided with each text document, the model can be trained in a contextual mode, where the model learns the text given the context so the recurrent layers learn the decontextualized language.

The text-only path can piggy-back off the decontextualized layers; in all, this results in much faster training and better quantitative and qualitative model performance than just training the model gien the text alone. The model weights included with the package are trained on hundreds of thousands of text documents from Reddit submissions via BigQueryfrom a very diverse variety of subreddits. The network was also trained using the decontextual approach noted above in order to both improve training performance and mitigate authorial bias.

When fine-tuning the model on a new dataset of texts using textgenrnn, all layers are retrained. However, since the original pretrained network has a much more robust "knowledge" initially, the new textgenrnn trains faster and more accurately in the end, and can potentially learn new relationships not present in the original dataset e. Additionally, the retraining is done with a momentum-based optimizer and a linearly decaying learning rate, both of which prevent exploding gradients and makes it much less likely that the model diverges after training for a long time.

Results will vary greatly between datasets. Because the pretrained neural network is relatively small, it cannot store as much data as RNNs typically flaunted in blog posts. For best results, use a dataset with at least 2, documents. Even then, there is currently no good heuristic for determining a "good" model. A web-based implementation using tensorflow.

A mode to allow the model architecture to be used for chatbot conversations may be released as a separate project. A larger pretrained network which can accommodate longer character sequences and a more indepth understanding of language, creating better generated sentences. Max's open-source projects are supported by his Patreon. If you found this project helpful, any monetary contributions to the Patreon are appreciated and will be put to good creative use.

Andrej Karpathy for the original proposal of the char-rnn via the blog post The Unreasonable Effectiveness of Recurrent Neural Networks.

textgenrnn 2.0.0

Daniel Grijalva for contributing an interactive mode. Something wrong with this page? Make a suggestion.One of the more interesting applications of the neural network revolution is text generation. Many internet tutorials for text-generation neural networks simply copy an existing char-rnn implementation while changing the input dataset.

Thanks to frameworks like TensorFlow and KerasI built textgenrnna Python package which abstracts the process of creating and training such char-rnns to a few lines of codewith numerous model architecture and training improvements such as character embeddingsattention-weighted averaging, and a decaying learning rate.

A neat benefit of textgenrnn is that it can be easily used to train neural networks on a GPU very quickly, for free using Google Colaboratory. Colaboratory is a notebook environment similar to Jupyter Notebooks used in other data science projects. For free. But you can install more packages directly in the notebook. Like textgenrnn! When training a new model, textgenrnn allows you to specify the size and complexity of the neural network with a wide variety of parameters:.

Run the next cell, which prompts you to upload a file. Any text file should workeven large text files! The next cell initializes an instance of textgenrnn and begins training a custom new text-generating neural network! I typically like generating text at a temperature of 0.

In all, for this example dataset and model architecture, training on a GPU took minutes an epoch, while on a modern CPU, training took 1 hour and 24 minutes an epoch, a 14x speedup on the GPU! After training is complete, running the next cell will download three files: a weights file, a vocabulary file, and a config file that are all needed to regenerate your model elsewhere.

For example, on your own personal computer. You can generate samples like during training using textgen. Training has a few helpful options as well. You can set 'validation': True to run the model on the unused data after each epoch to see if the model is overfitting. Reddithowever, has millions of submission titles which would be great to train for a model.

textgenrnn

I wrote a helper script to automatically download the top n Reddit submissions from a given subreddit over a given period of time. If you choose subreddits with similar linguistic styles in their titles, the subreddits will even blend together! Another advantage of the Colaboratory notebook is that you can quickly adjust model parameters, upload a new file, and immediately start training it. Rerun the config cell to update parameters, upload the Reddit data file, and rerun training.

And the results are very funny:. Let me know if you make any interesting neural networks with textgenrnn and this Notebook!


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