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https://github.com/batzner/tensorlm
Wrapper library for text generation / language models at char and word level with RNN in TensorFlow
https://github.com/batzner/tensorlm
char-lm char-rnn language-model nlp tensorflow tensorflow-library
Last synced: 14 days ago
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Wrapper library for text generation / language models at char and word level with RNN in TensorFlow
- Host: GitHub
- URL: https://github.com/batzner/tensorlm
- Owner: batzner
- License: mit
- Created: 2017-08-20T12:54:14.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2022-06-21T21:07:11.000Z (over 2 years ago)
- Last Synced: 2024-04-24T01:02:40.094Z (7 months ago)
- Topics: char-lm, char-rnn, language-model, nlp, tensorflow, tensorflow-library
- Language: Python
- Homepage:
- Size: 79.1 KB
- Stars: 61
- Watchers: 4
- Forks: 30
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
Awesome Lists containing this project
- awesome-datascience - tensorlm
README
# tensorlm
Generate Shakespeare poems with 4 lines of code.
## Installation
`tensorlm` is written in / for Python 3.4+ and TensorFlow 1.1+
pip3 install tensorlm
## Basic Usage
Use the `CharLM` or `WordLM` class:
```python
import tensorflow as tf
from tensorlm import CharLM
with tf.Session() as session:
# Create a new model. You can also use WordLM
model = CharLM(session, "datasets/sherlock/tinytrain.txt", max_vocab_size=96,
neurons_per_layer=100, num_layers=3, num_timesteps=15)
# Train it
model.train(session, max_epochs=10, max_steps=500)
# Let it generate a text
generated = model.sample(session, "The ", num_steps=100)
print("The " + generated)
```
This should output something like:
The ee e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e
## Command Line Usage
**Train:**
`python3 -m tensorlm.cli --train=True --level=char --train_text_path=datasets/sherlock/tinytrain.txt --max_vocab_size=96 --neurons_per_layer=100 --num_layers=2 --batch_size=10 --num_timesteps=15 --save_dir=out/model --max_epochs=300 --save_interval_hours=0.5`
**Sample:**
`python3 -m tensorlm.cli --sample=True --level=char --neurons_per_layer=400 --num_layers=3 --num_timesteps=160 --save_dir=out/model`
**Evaluate:**
`python3 -m tensorlm.cli --evaluate=True --level=char --evaluate_text_path=datasets/sherlock/tinyvalid.txt --neurons_per_layer=400 --num_layers=3 --batch_size=10 --num_timesteps=160 --save_dir=out/model`
See `python3 -m tensorlm.cli --help` for all options.
## Advanced Usage
### Custom Input Data
The inputs and targets don't have to be text. `GeneratingLSTM` only expects token ids, so you can use any data type for the sequences, as long as you can encode the data to integer ids.
```python
# We use integer ids from 0 to 19, so the vocab size is 20. The range of ids must always start
# at zero.
batch_inputs = np.array([[1, 2, 3, 4], [15, 16, 17, 18]]) # 2 batches, 4 time steps each
batch_targets = np.array([[2, 3, 4, 5], [16, 17, 18, 19]])
# Create the model in a TensorFlow graph
model = GeneratingLSTM(vocab_size=20, neurons_per_layer=10, num_layers=2, max_batch_size=2)
# Initialize all defined TF Variables
session.run(tf.global_variables_initializer())
for _ in range(5000):
model.train_step(session, batch_inputs, batch_targets)
sampled = model.sample_ids(session, [15], num_steps=3)
print("Sampled: " + str(sampled))
```
This should output something like:
Sampled: [16, 18, 19]
### Custom Training, Dropout etc.
Use the `GeneratingLSTM` class directly. This class is agnostic to the dataset type. It expects integer ids and returns integer ids.
```python
import tensorflow as tf
from tensorlm import Vocabulary, Dataset, GeneratingLSTM
BATCH_SIZE = 20
NUM_TIMESTEPS = 15
with tf.Session() as session:
# Generate a token -> id vocabulary based on the text
vocab = Vocabulary.create_from_text("datasets/sherlock/tinytrain.txt", max_vocab_size=96,
level="char")
# Obtain input and target batches from the text file
dataset = Dataset("datasets/sherlock/tinytrain.txt", vocab, BATCH_SIZE, NUM_TIMESTEPS)
# Create the model in a TensorFlow graph
model = GeneratingLSTM(vocab_size=vocab.get_size(), neurons_per_layer=100, num_layers=2,
max_batch_size=BATCH_SIZE, output_keep_prob=0.5)
# Initialize all defined TF Variables
session.run(tf.global_variables_initializer())
# Do the training
epoch = 1
step = 1
for epoch in range(20):
for inputs, targets in dataset:
loss = model.train_step(session, inputs, targets)
if step % 100 == 0:
# Evaluate from time to time
dev_dataset = Dataset("datasets/sherlock/tinyvalid.txt", vocab,
batch_size=BATCH_SIZE, num_timesteps=NUM_TIMESTEPS)
dev_loss = model.evaluate(session, dev_dataset)
print("Epoch: %d, Step: %d, Train Loss: %f, Dev Loss: %f" % (
epoch, step, loss, dev_loss))
# Sample from the model from time to time
print("Sampled: \"The " + model.sample_text(session, vocab, "The ") + "\"")
step += 1
```
This should output something like:
Epoch: 3, Step: 100, Train Loss: 3.824941, Dev Loss: 3.778008
Sampled: "The "
Epoch: 7, Step: 200, Train Loss: 2.832825, Dev Loss: 2.896187
Sampled: "The "
Epoch: 11, Step: 300, Train Loss: 2.778579, Dev Loss: 2.830176
Sampled: "The eee "
Epoch: 15, Step: 400, Train Loss: 2.655153, Dev Loss: 2.684828
Sampled: "The ee e e e e e e e e e e e e e e e e e e e e e e e e e e e "
Epoch: 19, Step: 500, Train Loss: 2.444502, Dev Loss: 2.479753
Sampled: "The an an an on on on on on on on on on on on on on on on on on on on on on o"