generate_rec.py

def validate_csv(text, field_count=4):
    # Quick validation check to make sure input is sane 
    return len(text.split(", ")) == field_count
    
def generate_rec(model, start_string, num_chars, temperature, expected_field_count):
    invalid_record_count = 0
    valid_record_count = 0
    
    # Convert our start string to numbers (vectorizing)
    input_eval = [char2idx[s] for s in start_string]
    input_eval = tf.expand_dims(input_eval, 0)

    # Empty string to store our results
    text_generated = ""

    # When generating text, use batch size of 1
    model.reset_states()
    for i in range(num_chars):
        predictions = model(input_eval)
        # remove the batch dimension
        predictions = tf.squeeze(predictions, 0)

        # using a categorical distribution to predict the word returned by the model
        predictions = predictions / temperature
        predicted_id = tf.random.categorical(predictions, num_samples=1)[-1,0].numpy()

        # We pass the predicted word as the next input to the model
        # along with the previous hidden state
        input_eval = tf.expand_dims([predicted_id], 0)

        text_generated += idx2char[predicted_id]
        if idx2char[predicted_id] == '\n':
            if validate_csv(text_generated, expected_field_count):
                print(text_generated.strip())
                valid_record_count += 1
            else:
                invalid_record_count += 1
            text_generated = ""

    return {'valid': valid_record_count, 'invalid': invalid_record_count}