Hi, thanks for the tutorial.
This is more of a question so sorry if it's not in the right place.
I'm training a language model using the queue and TFRecord method that you've described in the tutorials. I am able to feed in the batched / padded data into the TensorFlow graph and train the model.
...
# queue for the data
batched_data = tf.train.batch(
tensors=[y],
batch_size=5,
dynamic_pad=True,
name="y_batch"
)
...
# specify the model
embedding = tf.Variable(shape=(vocab_size, hidden_units), dtype=tf.float32)
embedded_input = tf.nn.embedding_lookup(
embedding, batched_data, name="embedded_input")
...
# run the model
sess.run([train_op], feed_dict={})
...
I'd like to check the language model performance by generating words from the trained model. I would run the model however many steps to complete the sentence.
This requires me to feed in new data that's not part of the training data queue.