Trump Change

trump-change is a character-level generative text model wrapped in a customized data science workflow written in python using tensorflow and the pyruns experiment manager. This project contains all the necessary source code and data requred to train your own models, run hyperparameter tuning experiments, freeze models off in Tensorflow's saved_model format, and access pretrained models from a python API.

Note: You do not need pyruns to run any of this code. pyruns is a simple port of Rstudio's tfruns for python that I wrote and can be found here. It helps manage your expierments by creating a micro-hermetic build of a data science project in a unique run directory, making for easy evaluations and comparisons.

To start generating with a pretrained model with an automated script, simply clone the repo and run generate.py:

git clone www.github.com/ifrit98/trump-change.git
cd trump-change
./generate.py

To have more control over knobs such as swapping out checkpoint files, setting the annealing temperature,conditioning_strings, and the number of chars to generate per tweet, use the python CLI provided in CLI.py:

./CLI.py
...
How many strings to generate?...
3

...

Would you like to update parameters? (y/n):
y

...

Note: This script will automatically load the latest checkpoint in the trump_training_checkpoints directory. If you would like to use a different checkpoint directory, specify it upon calling CLI.py:

./CLI.py --checkpoint /path/to/checkpoint_dir

The CLI uses argparse to set values automatically so you only have to set the number of tweets to generate in the CLI:

./CLI.py --help

usage: CLI.py [-h] [-w CHECKPOINT] [-n NUM_GENERATE] [-c CONDITIONING]
              [-v VOCAB_PATH] [-t TEMPERATURE]

optional arguments:
  -h, --help            show this help message and exit
  -w CHECKPOINT, --checkpoint CHECKPOINT
                        Filepath to model checkpoint.
  -n NUM_GENERATE, --num_generate NUM_GENERATE
                        Set the number of characters to generate per tweet.
  -c CONDITIONING, --conditioning CONDITIONING
                        Set the conditioning string to use as input to the
                        model.
  -v VOCAB_PATH, --vocab_path VOCAB_PATH
                        Path to vocabulary file [.npy array].
  -t TEMPERATURE, --temperature TEMPERATURE
                        Set the temperature of the annealer during prediction.

To train your own model simply run the train.py script:

./train.py

You may wish to update hyperparameter values found in the flags.yaml file of the top level.

cat flags.yaml

--- 
 # Model
 epochs: 350
 rnn_units: 412
 embedding_dim: 206
 batch_size: 108
 vocab_file: vocab.npy

 # Optimizer
 min_lr: 0.0001
 max_lr: 0.004291934
 min_delta: 0.001 # for lr_scheduler
 lr_factor: 0.5
 patience: 5
 steps_per_epoch: 130
 decay_epochs: 5
 decay_rate: 0.96

 # Dataset
 keep_emojis: False
 data_file: trump-tweets-no-retweets-latest.json
 buffer_size: 100

 # Misc
 checkpoint_dir: trump_training_checkpoints/current
 verbose: False
 encoding: ISO-8859-2
 base_dir: /home/jason/internal/trump-change
 runs_dir: /home/jason/freya/runs # This gloab runs directory may be separate from your project directory