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R11149/test_projects/gpt2/gpt-2-nshepperd_copy/src/generate_unconditional_samples.py
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generate_unconditional_samples.py
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#!/usr/bin/env python3
import fire
import json
import os
import numpy as np
import tensorflow as tf
import model, sample, encoder
def sample_model(
model_name='117M',
seed=None,
nsamples=0,
batch_size=1,
length=None,
temperature=1,
top_k=0,
top_p=0.0
):
"""
Run the sample_model
:model_name=117M : String, which model to use
:seed=None : Integer seed for random number generators, fix seed to
reproduce results
:nsamples=0 : Number of samples to return, if 0, continues to
generate samples indefinately.
:batch_size=1 : Number of batches (only affects speed/memory).
:length=None : Number of tokens in generated text, if None (default), is
determined by model hyperparameters
:temperature=1 : Float value controlling randomness in boltzmann
distribution. Lower temperature results in less random completions. As the
temperature approaches zero, the model will become deterministic and
repetitive. Higher temperature results in more random completions.
:top_k=0 : Integer value controlling diversity. 1 means only 1 word is
considered for each step (token), resulting in deterministic completions,
while 40 means 40 words are considered at each step. 0 (default) is a
special setting meaning no restrictions. 40 generally is a good value.
:top_p=0.0 : Float value controlling diversity. Implements nucleus sampling,
overriding top_k if set to a value > 0. A good setting is 0.9.
"""
enc = encoder.get_encoder(model_name)
hparams = model.default_hparams()
with open(os.path.join('models', model_name, 'hparams.json')) as f:
hparams.override_from_dict(json.load(f))
if length is None:
length = hparams.n_ctx
elif length > hparams.n_ctx:
raise ValueError("Can't get samples longer than window size: %s" % hparams.n_ctx)
with tf.Session(graph=tf.Graph()) as sess:
np.random.seed(seed)
tf.set_random_seed(seed)
output = sample.sample_sequence(
hparams=hparams, length=length,
start_token=enc.encoder['<|endoftext|>'],
batch_size=batch_size,
temperature=temperature, top_k=top_k, top_p=top_p
)[:, 1:]
saver = tf.train.Saver()
ckpt = tf.train.latest_checkpoint(os.path.join('models', model_name))
saver.restore(sess, ckpt)
generated = 0
while nsamples == 0 or generated < nsamples:
out = sess.run(output)
for i in range(batch_size):
generated += batch_size
text = enc.decode(out[i])
print("=" * 40 + " SAMPLE " + str(generated) + " " + "=" * 40)
print(text)
if __name__ == '__main__':
fire.Fire(sample_model)
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