In this project, we’ll generate our own Simpsons TV scripts using RNNs. We’ll be using part of the Simpsons dataset of scripts from 27 seasons. The Neural Network we’ll build will generate a new TV script for a scene at Moe’s Tavern.

Get the Data

we’ll be using a subset of the original dataset. It consists of only the scenes in Moe’s Tavern. This doesn’t include other versions of the tavern, like “Moe’s Cavern”, “Flaming Moe’s”, “Uncle Moe’s Family Feed-Bag”, etc..

import helper

data_dir = './data/simpsons/moes_tavern_lines.txt'
text = helper.load_data(data_dir)
# Ignore notice, since we don't use it for analysing the data
text = text[81:]

Explore the Data

Play around with view_sentence_range to view different parts of the data.

view_sentence_range = (0, 10)
import numpy as np

print('Dataset Stats')
print('Roughly the number of unique words: {}'.format(len({word: None for word in text.split()})))
scenes = text.split('\n\n')
print('Number of scenes: {}'.format(len(scenes)))
sentence_count_scene = [scene.count('\n') for scene in scenes]
print('Average number of sentences in each scene: {}'.format(np.average(sentence_count_scene)))

sentences = [sentence for scene in scenes for sentence in scene.split('\n')]
print('Number of lines: {}'.format(len(sentences)))
word_count_sentence = [len(sentence.split()) for sentence in sentences]
print('Average number of words in each line: {}'.format(np.average(word_count_sentence)))

print()
print('The sentences {} to {}:'.format(*view_sentence_range))
print('\n'.join(text.split('\n')[view_sentence_range[0]:view_sentence_range[1]]))

Dataset Stats
Roughly the number of unique words: 11492
Number of scenes: 262
Average number of sentences in each scene: 15.248091603053435
Number of lines: 4257
Average number of words in each line: 11.50434578341555

The sentences 0 to 10:
Moe_Szyslak: (INTO PHONE) Moe's Tavern. Where the elite meet to drink.
Bart_Simpson: Eh, yeah, hello, is Mike there? Last name, Rotch.
Moe_Szyslak: (INTO PHONE) Hold on, I'll check. (TO BARFLIES) Mike Rotch. Mike Rotch. Hey, has anybody seen Mike Rotch, lately?
Moe_Szyslak: (INTO PHONE) Listen you little puke. One of these days I'm gonna catch you, and I'm gonna carve my name on your back with an ice pick.
Moe_Szyslak: What's the matter Homer? You're not your normal effervescent self.
Homer_Simpson: I got my problems, Moe. Give me another one.
Moe_Szyslak: Homer, hey, you should not drink to forget your problems.
Barney_Gumble: Yeah, you should only drink to enhance your social skills.

Implement Preprocessing Functions

The first thing to do to any dataset is preprocessing. Implement the following preprocessing functions below: - Lookup Table - Tokenize Punctuation

Lookup Table

To create a word embedding, we first need to transform the words to ids. In this function, create two dictionaries: - Dictionary to go from the words to an id, we’ll call vocab_to_int - Dictionary to go from the id to word, we’ll call int_to_vocab

Return these dictionaries in the following tuple (vocab_to_int, int_to_vocab)

import numpy as np
import problem_unittests as tests

def create_lookup_tables(text):
    """
    Create lookup tables for vocabulary
    :param text: The text of tv scripts split into words
    :return: A tuple of dicts (vocab_to_int, int_to_vocab)
    """
    chars = sorted(list(set(text)))
    vocab_to_int = dict((c, i) for i, c in enumerate(chars))
    int_to_vocab = dict((i, c) for i, c in enumerate(chars))

    return vocab_to_int, int_to_vocab

Tokenize Punctuation

We’ll be splitting the script into a word array using spaces as delimiters. However, punctuations like periods and exclamation marks make it hard for the neural network to distinguish between the word “bye” and “bye!”.

Implement the function token_lookup to return a dict that will be used to tokenize symbols like “!” into “||Exclamation_Mark||“. Create a dictionary for the following symbols where the symbol is the key and value is the token: - Period ( . ) - Comma ( , ) - Quotation Mark ( “ ) - Semicolon ( ; ) - Exclamation mark ( ! ) - Question mark ( ? ) - Left Parentheses ( ( ) - Right Parentheses ( ) ) - Dash ( – ) - Return ( \n )

This dictionary will be used to token the symbols and add the delimiter (space) around it. This separates the symbols as it’s own word, making it easier for the neural network to predict on the next word. Make sure we don’t use a token that could be confused as a word. Instead of using the token “dash”, try using something like “||dash||”.

def token_lookup():
    """
    Generate a dict to turn punctuation into a token.
    :return: Tokenize dictionary where the key is the punctuation and the value is the token
    """
    values = ['||Period||','||Comma||','||Quotation_Mark||','||Semicolon||','||Exclamation_mark||','||Question_mark||','||Left_Parentheses||','||Right_Parentheses||','||Dash||','||Return||']
    keys = ['.', ',', '"', ';', '!', '?', '(', ')', '--','\n']
    return (dict(zip(keys,values)))

Preprocess all the data and save it

Running the code cell below will preprocess all the data and save it to file.

# Preprocess Training, Validation, and Testing Data
helper.preprocess_and_save_data(data_dir, token_lookup, create_lookup_tables)

Check Point

This is our first checkpoint. If we ever decide to come back to this or have to restart, we can start from here. The preprocessed data has been saved to disk.

import helper
import numpy as np
import problem_unittests as tests

int_text, vocab_to_int, int_to_vocab, token_dict = helper.load_preprocess()

Build the Neural Network

We’ll build the components necessary to build a RNN by implementing the following functions below: - get_inputs - get_init_cell - get_embed - build_rnn - build_nn - get_batches

Check the Version of TensorFlow and Access to GPU

from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer'
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))

TensorFlow Version: 1.0.0
Default GPU Device: /gpu:0

Input

Implement the get_inputs() function to create TF Placeholders for the Neural Network. It should create the following placeholders: - Input text placeholder named “input” using the TF Placeholder name parameter. - Targets placeholder - Learning Rate placeholder

Return the placeholders in the following tuple (Input, Targets, LearningRate)

def get_inputs():
    """
    Create TF Placeholders for input, targets, and learning rate.
    :return: Tuple (input, targets, learning rate)
    """
    input = tf.placeholder(tf.int32,[None, None], name='input')
    targets = tf.placeholder(tf.int32,[None, None], name='targets')
    learning_rate = tf.placeholder(tf.float32,name='learning_rate')

    return input, targets, learning_rate

Build RNN Cell and Initialize

Stack one or more BasicLSTMCells in a MultiRNNCell. - The Rnn size should be set using rnn_size - Initalize Cell State using the MultiRNNCell’s zero_state() function - Apply the name “initial_state” to the initial state using tf.identity()

Return the cell and initial state in the following tuple (Cell, InitialState)

def get_init_cell(batch_size, rnn_size):
    """
    Create an RNN Cell and initialize it.
    :param batch_size: Size of batches
    :param rnn_size: Size of RNNs
    :return: Tuple (cell, initialize state)
    """
    lstm = tf.contrib.rnn.BasicLSTMCell(rnn_size)
    cell = tf.contrib.rnn.MultiRNNCell([lstm])
    initial_state = cell.zero_state(batch_size, tf.float32)
    initial_state =  tf.identity(initial_state, name='initial_state')

    return cell, initial_state

Word Embedding

Apply embedding to input_data using TensorFlow. Return the embedded sequence.

def get_embed(input_data, vocab_size, embed_dim):
    """
    Create embedding for <input_data>.
    :param input_data: TF placeholder for text input.
    :param vocab_size: Number of words in vocabulary.
    :param embed_dim: Number of embedding dimensions
    :return: Embedded input.
    """
    embeddings = tf.Variable(tf.random_uniform([vocab_size, embed_dim], -1.0, 1.0))
    embedded_input = tf.nn.embedding_lookup(embeddings, input_data)

    return embedded_input

Build RNN

We created a RNN Cell in the get_init_cell() function. Time to use the cell to create a RNN. - Build the RNN using the tf.nn.dynamic_rnn() - Apply the name “final_state” to the final state using tf.identity()

Return the outputs and final_state state in the following tuple (Outputs, FinalState)

def build_rnn(cell, inputs):
    """
    Create a RNN using a RNN Cell
    :param cell: RNN Cell
    :param inputs: Input text data
    :return: Tuple (Outputs, Final State)
    """
    outputs, final_state = tf.nn.dynamic_rnn(cell, inputs, dtype = tf.float32)
    final_state = tf.identity(final_state, name="final_state")

    return outputs, final_state

Build the Neural Network

Apply the functions we implemented above to: - Apply embedding to input_data using our get_embed(input_data, vocab_size, embed_dim) function. - Build RNN using cell and our build_rnn(cell, inputs) function. - Apply a fully connected layer with a linear activation and vocab_size as the number of outputs.

Return the logits and final state in the following tuple (Logits, FinalState)

def build_nn(cell, rnn_size, input_data, vocab_size, embed_dim):
    """
    Build part of the neural network
    :param cell: RNN cell
    :param rnn_size: Size of rnns
    :param input_data: Input data
    :param vocab_size: Vocabulary size
    :param embed_dim: Number of embedding dimensions
    :return: Tuple (Logits, FinalState)
    """
    input_data = get_embed(input_data,vocab_size,rnn_size)
    outputs,final_state = build_rnn(cell,input_data)
    logits = tf.contrib.layers.fully_connected(outputs,vocab_size,activation_fn = None)
    return logits, final_state

Batches

Implement get_batches to create batches of input and targets using int_text. The batches should be a Numpy array with the shape (number of batches, 2, batch size, sequence length). Each batch contains two elements: - The first element is a single batch of input with the shape [batch size, sequence length] - The second element is a single batch of targets with the shape [batch size, sequence length]

If we can’t fill the last batch with enough data, drop the last batch.

For exmple, get_batches([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20], 3, 2) would return a Numpy array of the following:

[
  # First Batch
  [
    # Batch of Input
    [[ 1  2], [ 7  8], [13 14]]
    # Batch of targets
    [[ 2  3], [ 8  9], [14 15]]
  ]

  # Second Batch
  [
    # Batch of Input
    [[ 3  4], [ 9 10], [15 16]]
    # Batch of targets
    [[ 4  5], [10 11], [16 17]]
  ]

  # Third Batch
  [
    # Batch of Input
    [[ 5  6], [11 12], [17 18]]
    # Batch of targets
    [[ 6  7], [12 13], [18  1]]
  ]
]

Notice that the last target value in the last batch is the first input value of the first batch. In this case, 1. This is a common technique used when creating sequence batches, although it is rather unintuitive.

def get_batches(int_text, batch_size, seq_length):
    """
    Return batches of input and target
    :param int_text: Text with the words replaced by their ids
    :param batch_size: The size of batch
    :param seq_length: The length of sequence
    :return: Batches as a Numpy array
    """
    n_batches = int(len(int_text) / (batch_size * seq_length))
    input_data = np.array(int_text[: n_batches * batch_size * seq_length])
    target_data = np.array(int_text[1: n_batches * batch_size * seq_length + 1])
    target_data[-1] = input_data[0]

    input_batches = np.split(input_data.reshape(batch_size, -1), n_batches, 1)
    target_batches = np.split(target_data.reshape(batch_size, -1), n_batches, 1)

    return np.array(list(zip(input_batches, target_batches)))

Neural Network Training

Hyperparameters

Tune the following parameters:

• Set num_epochs to the number of epochs.
• Set batch_size to the batch size.
• Set rnn_size to the size of the RNNs.
• Set embed_dim to the size of the embedding.
• Set seq_length to the length of sequence.
• Set learning_rate to the learning rate.
• Set show_every_n_batches to the number of batches the neural network should print progress.

# Number of Epochs
num_epochs = 100
# Batch Size
batch_size = 128
# RNN Size
rnn_size = 256
# Embedding Dimension Size
embed_dim = 300
# Sequence Length
seq_length = 16
# Learning Rate
learning_rate = 0.01
# Show stats for every n number of batches
show_every_n_batches = 33

save_dir = './save'

Build the Graph

Build the graph using the neural network we implemented.

from tensorflow.contrib import seq2seq

train_graph = tf.Graph()
with train_graph.as_default():
    vocab_size = len(int_to_vocab)
    input_text, targets, lr = get_inputs()
    input_data_shape = tf.shape(input_text)
    cell, initial_state = get_init_cell(input_data_shape[0], rnn_size)
    logits, final_state = build_nn(cell, rnn_size, input_text, vocab_size, embed_dim)

    # Probabilities for generating words
    probs = tf.nn.softmax(logits, name='probs')

    # Loss function
    cost = seq2seq.sequence_loss(
        logits,
        targets,
        tf.ones([input_data_shape[0], input_data_shape[1]]))

    # Optimizer
    optimizer = tf.train.AdamOptimizer(lr)

    # Gradient Clipping
    gradients = optimizer.compute_gradients(cost)
    capped_gradients = [(tf.clip_by_value(grad, -1., 1.), var) for grad, var in gradients if grad is not None]
    train_op = optimizer.apply_gradients(capped_gradients)

Train

Train the neural network on the preprocessed data. If you have a hard time getting a good loss, check the forms to see if anyone is having the same problem.

batches = get_batches(int_text, batch_size, seq_length)

with tf.Session(graph=train_graph) as sess:
    sess.run(tf.global_variables_initializer())

    for epoch_i in range(num_epochs):
        state = sess.run(initial_state, {input_text: batches[0][0]})

        for batch_i, (x, y) in enumerate(batches):
            feed = {
                input_text: x,
                targets: y,
                initial_state: state,
                lr: learning_rate}
            train_loss, state, _ = sess.run([cost, final_state, train_op], feed)

            # Show every <show_every_n_batches> batches
            if (epoch_i * len(batches) + batch_i) % show_every_n_batches == 0:
                print('Epoch {:>3} Batch {:>4}/{}   train_loss = {:.3f}'.format(
                    epoch_i,
                    batch_i,
                    len(batches),
                    train_loss))

    # Save Model
    saver = tf.train.Saver()
    saver.save(sess, save_dir)
    print('Model Trained and Saved')

Epoch   0 Batch    0/33   train_loss = 8.821
Epoch   1 Batch    0/33   train_loss = 5.051
Epoch   2 Batch    0/33   train_loss = 4.414
Epoch   3 Batch    0/33   train_loss = 4.063
Epoch   4 Batch    0/33   train_loss = 3.731
Epoch   5 Batch    0/33   train_loss = 3.471
Epoch   6 Batch    0/33   train_loss = 3.155
Epoch   7 Batch    0/33   train_loss = 2.910
Epoch   8 Batch    0/33   train_loss = 2.686
Epoch   9 Batch    0/33   train_loss = 2.484
Epoch  10 Batch    0/33   train_loss = 2.289
Epoch  11 Batch    0/33   train_loss = 2.135
Epoch  12 Batch    0/33   train_loss = 2.004
Epoch  13 Batch    0/33   train_loss = 1.858
Epoch  14 Batch    0/33   train_loss = 1.738
Epoch  15 Batch    0/33   train_loss = 1.629
Epoch  16 Batch    0/33   train_loss = 1.502
Epoch  17 Batch    0/33   train_loss = 1.397
Epoch  18 Batch    0/33   train_loss = 1.300
Epoch  19 Batch    0/33   train_loss = 1.245
Epoch  20 Batch    0/33   train_loss = 1.158
Epoch  21 Batch    0/33   train_loss = 1.085
Epoch  22 Batch    0/33   train_loss = 1.028
Epoch  23 Batch    0/33   train_loss = 0.967
Epoch  24 Batch    0/33   train_loss = 0.899
Epoch  25 Batch    0/33   train_loss = 0.838
Epoch  26 Batch    0/33   train_loss = 0.804
Epoch  27 Batch    0/33   train_loss = 0.771
Epoch  28 Batch    0/33   train_loss = 0.707
Epoch  29 Batch    0/33   train_loss = 0.665
Epoch  30 Batch    0/33   train_loss = 0.630
Epoch  31 Batch    0/33   train_loss = 0.587
Epoch  32 Batch    0/33   train_loss = 0.568
Epoch  33 Batch    0/33   train_loss = 0.545
Epoch  34 Batch    0/33   train_loss = 0.510
Epoch  35 Batch    0/33   train_loss = 0.479
Epoch  36 Batch    0/33   train_loss = 0.471
Epoch  37 Batch    0/33   train_loss = 0.446
Epoch  38 Batch    0/33   train_loss = 0.425
Epoch  39 Batch    0/33   train_loss = 0.412
Epoch  40 Batch    0/33   train_loss = 0.392
Epoch  41 Batch    0/33   train_loss = 0.391
Epoch  42 Batch    0/33   train_loss = 0.373
Epoch  43 Batch    0/33   train_loss = 0.366
Epoch  44 Batch    0/33   train_loss = 0.343
Epoch  45 Batch    0/33   train_loss = 0.319
Epoch  46 Batch    0/33   train_loss = 0.313
Epoch  47 Batch    0/33   train_loss = 0.294
Epoch  48 Batch    0/33   train_loss = 0.288
Epoch  49 Batch    0/33   train_loss = 0.270
Epoch  50 Batch    0/33   train_loss = 0.262
Epoch  51 Batch    0/33   train_loss = 0.251
Epoch  52 Batch    0/33   train_loss = 0.244
Epoch  53 Batch    0/33   train_loss = 0.237
Epoch  54 Batch    0/33   train_loss = 0.234
Epoch  55 Batch    0/33   train_loss = 0.230
Epoch  56 Batch    0/33   train_loss = 0.225
Epoch  57 Batch    0/33   train_loss = 0.224
Epoch  58 Batch    0/33   train_loss = 0.223
Epoch  59 Batch    0/33   train_loss = 0.222
Epoch  60 Batch    0/33   train_loss = 0.219
Epoch  61 Batch    0/33   train_loss = 0.219
Epoch  62 Batch    0/33   train_loss = 0.217
Epoch  63 Batch    0/33   train_loss = 0.219
Epoch  64 Batch    0/33   train_loss = 0.217
Epoch  65 Batch    0/33   train_loss = 0.218
Epoch  66 Batch    0/33   train_loss = 0.214
Epoch  67 Batch    0/33   train_loss = 0.215
Epoch  68 Batch    0/33   train_loss = 0.214
Epoch  69 Batch    0/33   train_loss = 0.215
Epoch  70 Batch    0/33   train_loss = 0.215
Epoch  71 Batch    0/33   train_loss = 0.218
Epoch  72 Batch    0/33   train_loss = 0.214
Epoch  73 Batch    0/33   train_loss = 0.214
Epoch  74 Batch    0/33   train_loss = 0.214
Epoch  75 Batch    0/33   train_loss = 0.215
Epoch  76 Batch    0/33   train_loss = 0.213
Epoch  77 Batch    0/33   train_loss = 0.214
Epoch  78 Batch    0/33   train_loss = 0.212
Epoch  79 Batch    0/33   train_loss = 0.213
Epoch  80 Batch    0/33   train_loss = 0.212
Epoch  81 Batch    0/33   train_loss = 0.214
Epoch  82 Batch    0/33   train_loss = 0.212
Epoch  83 Batch    0/33   train_loss = 0.213
Epoch  84 Batch    0/33   train_loss = 0.211
Epoch  85 Batch    0/33   train_loss = 0.213
Epoch  86 Batch    0/33   train_loss = 0.214
Epoch  87 Batch    0/33   train_loss = 0.213
Epoch  88 Batch    0/33   train_loss = 0.213
Epoch  89 Batch    0/33   train_loss = 0.213
Epoch  90 Batch    0/33   train_loss = 0.213
Epoch  91 Batch    0/33   train_loss = 0.212
Epoch  92 Batch    0/33   train_loss = 0.211
Epoch  93 Batch    0/33   train_loss = 0.214
Epoch  94 Batch    0/33   train_loss = 0.211
Epoch  95 Batch    0/33   train_loss = 0.212
Epoch  96 Batch    0/33   train_loss = 0.211
Epoch  97 Batch    0/33   train_loss = 0.212
Epoch  98 Batch    0/33   train_loss = 0.211
Epoch  99 Batch    0/33   train_loss = 0.212
Model Trained and Saved

Save Parameters

Save seq_length and save_dir for generating a new TV script.

# Save parameters for checkpoint
helper.save_params((seq_length, save_dir))

Checkpoint

import tensorflow as tf
import numpy as np
import helper
import problem_unittests as tests

_, vocab_to_int, int_to_vocab, token_dict = helper.load_preprocess()
seq_length, load_dir = helper.load_params()

Implement Generate Functions

Get Tensors

Get tensors from loaded_graph using the function get_tensor_by_name(). Get the tensors using the following names: - “input:0” - “initial_state:0” - “final_state:0” - “probs:0”

Return the tensors in the following tuple (InputTensor, InitialStateTensor, FinalStateTensor, ProbsTensor)

def get_tensors(loaded_graph):
    """
    Get input, initial state, final state, and probabilities tensor from <loaded_graph>
    :param loaded_graph: TensorFlow graph loaded from file
    :return: Tuple (InputTensor, InitialStateTensor, FinalStateTensor, ProbsTensor)
    """
    inputs = loaded_graph.get_tensor_by_name("input:0")
    initial_state = loaded_graph.get_tensor_by_name("initial_state:0")
    final_state = loaded_graph.get_tensor_by_name("final_state:0")
    probs = loaded_graph.get_tensor_by_name("probs:0")

    return inputs, initial_state, final_state, probs

Choose Word

Implement the pick_word() function to select the next word using probabilities.

def pick_word(probabilities, int_to_vocab):
    """
    Pick the next word in the generated text
    :param probabilities: Probabilites of the next word
    :param int_to_vocab: Dictionary of word ids as the keys and words as the values
    :return: String of the predicted word
    """
    return np.random.choice(list(int_to_vocab.values()), 1, p=probabilities)[0]

Generate TV Script

This will generate the TV script for us. Set gen_length to the length of TV script we want to generate.

gen_length = 200
# homer_simpson, moe_szyslak, or Barney_Gumble
prime_word = 'moe_szyslak'

loaded_graph = tf.Graph()
with tf.Session(graph=loaded_graph) as sess:
    # Load saved model
    loader = tf.train.import_meta_graph(load_dir + '.meta')
    loader.restore(sess, load_dir)

    # Get Tensors from loaded model
    input_text, initial_state, final_state, probs = get_tensors(loaded_graph)

    # Sentences generation setup
    gen_sentences = [prime_word + ':']
    prev_state = sess.run(initial_state, {input_text: np.array([[1]])})

    # Generate sentences
    for n in range(gen_length):
        # Dynamic Input
        dyn_input = [[vocab_to_int[word] for word in gen_sentences[-seq_length:]]]
        dyn_seq_length = len(dyn_input[0])

        # Get Prediction
        probabilities, prev_state = sess.run(
            [probs, final_state],
            {input_text: dyn_input, initial_state: prev_state})

        pred_word = pick_word(probabilities[dyn_seq_length-1], int_to_vocab)

        gen_sentences.append(pred_word)

    # Remove tokens
    tv_script = ' '.join(gen_sentences)
    for key, token in token_dict.items():
        ending = ' ' if key in ['\n', '(', '"'] else ''
        tv_script = tv_script.replace(' ' + token.lower(), key)
    tv_script = tv_script.replace('\n ', '\n')
    tv_script = tv_script.replace('( ', '(')

    print(tv_script)

moe_szyslak: before you do, i just gotta warn you, marge.(sings) my adeleine....
crowd:(chanting) barney gumbel" back brace has no turning the eyes.(chuckles) really need cash, homer. nobody loves you...
c. _montgomery_burns: that was no accident. let's get out of here.
moe_szyslak:(counting radishes) now where you wanna know, we make this the life?
homer_simpson: oh, thank god, the gimmick of.(to barflies) shut up, moe.
moe_szyslak: come on, you greedy old reptile!
c. _montgomery_burns:(shaken) why not my friends! homer didn't buy!
seymour_skinner:(holding two-thirds-empty beer) two of us have drink a new lease on" alcohol half it.
homer_simpson: i'm gonna treat marge to a romantic dinner.
moe_szyslak: ooh, taking advantage, here.(warmly) one" flaming homer" the sign, barney and easygoing.
lenny_leonard: i was just why not in here no more, homer

The TV Script is Nonsensical

It’s ok if the TV script doesn’t make any sense. We trained on less than a megabyte of text. In order to get good results, you’ll have to use a smaller vocabulary or get more data. Luckly there’s more data! As we mentioned in the begging of this project, this is a subset of another dataset. We didn’t train on all the data, because that would take too long. However, you are free to train your neural network on all the data.