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def interact_model( | |
model_name, | |
seed, | |
nsamples, | |
batch_size, | |
length, | |
temperature, | |
top_k, | |
models_dir | |
): | |
models_dir = os.path.expanduser(os.path.expandvars(models_dir)) | |
if batch_size is None: | |
batch_size = 1 | |
assert nsamples % batch_size == 0 | |
enc = encoder.get_encoder(model_name, models_dir) | |
hparams = model.default_hparams() | |
with open(os.path.join(models_dir, model_name, 'hparams.json')) as f: | |
hparams.override_from_dict(json.load(f)) | |
if length is None: | |
length = hparams.n_ctx // 2 | |
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: | |
context = tf.placeholder(tf.int32, [batch_size, None]) | |
np.random.seed(seed) | |
tf.set_random_seed(seed) | |
output = sample.sample_sequence( | |
hparams=hparams, length=length, | |
context=context, | |
batch_size=batch_size, | |
temperature=temperature, top_k=top_k | |
) | |
saver = tf.train.Saver() | |
ckpt = tf.train.latest_checkpoint(os.path.join(models_dir, model_name)) | |
saver.restore(sess, ckpt) | |
while True: | |
raw_text = input("Model prompt >>> ") | |
while not raw_text: | |
print('Prompt should not be empty!') | |
raw_text = input("Model prompt >>> ") | |
context_tokens = enc.encode(raw_text) | |
generated = 0 | |
for _ in range(nsamples // batch_size): | |
out = sess.run(output, feed_dict={ | |
context: [context_tokens for _ in range(batch_size)] | |
})[:, len(context_tokens):] | |
for i in range(batch_size): | |
generated += 1 | |
text = enc.decode(out[i]) | |
print("=" * 40 + " SAMPLE " + str(generated) + " " + "=" * 40) | |
print(text) | |
print("=" * 80) |
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