Test_GPT-2.py

#Importing the necessary libraries..

import json
import numpy as np
import tensorflow as tf
import model, sample, encoder


#Function to use the interaction model..

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(save_relative_paths=True)
        ckpt = tf.train.latest_checkpoint(os.path.join(models_dir, model_name))
        saver.restore(sess, ckpt)

        while True:
            raw_text = input("\nModel prompt >>> ")
            if raw_text == 'ADMIN_NIXTRATOR':
              raw_text = False
              break
            while not raw_text:
                print('\nPrompt should not be empty!')
                raw_text = input("\nModel 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)
            


'''Code Explanation
model_name:
This indicates which model we are using. In our case, we are using the GPT-2 model with 345 million parameters or weights

seed:
Integer seed for random number generators, fix seed to reproduce results

nsamples:
This represents the number of sample texts generated in our output

batch_size:
This only affects speed/memory. This must also divide nsamples

Note: To generate more than one sample, you need to change the values of both nsamples and batch_size and also have to keep them equal.

length:
It represents the number of tokens in the generated text. If the length is None, then the number of tokens is decided by model hyperparameters

temperature:
This controls 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:
This parameter controls diversity. If the value of top_k is set to 1, this means that only 1 word is considered for each step (token). If top_k is set to 40, that means 40 words are considered at each step. 0 (default) is a special setting meaning no restrictions. top_k = 40 generally is a good value

models_dir:
It represents the path to parent folder containing model subfolders (contains the folder)'''

#Using the arguements above..

interact_model('345M', None, 1, 1, 20, 1, 0, '/content/gpt-2/models')