TV Script Generation

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

Get the Data

The data is already provided for you. You'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..

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
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)

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
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, you 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)
    """
    # TODO: Implement Function
    vocab = set(text)
    vocab_to_int = {word: i for i, word in enumerate(vocab)}
    int_to_vocab = {i: word for i, word in enumerate(vocab)}
    return vocab_to_int, int_to_vocab


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_create_lookup_tables(create_lookup_tables)

Tests Passed

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 you 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
    """
    # TODO: Implement Function
    # from string import punctuation
    return { '.': '||Period||',
             ',': '||Comma||',
             '"': '||Quotation_Mark||',
             ';': '||Semicolon||',
             '!': '||Exclamation_Mark||',
             '?': '||Question_Mark||',
             '(': '||Left_Parentheses||',
             ')': '||Right_Parentheses||',
             '--': '||Dash||',
             '\n': '||Return||'
             # '#': '||Hash||',
             # '$': '||Dollar||',
             # '%': '||Percent||',
             # '&': '||Ampersand||',
             # "'": '||Single_Quotation_Mark||',
             # '*': '||Asterisk||',
             # '+': '||Plus||',
             # '/': '||Slash||',
             # '\\': '||Backslash||',
             # ':': '||Colon||',
             # '[': '||Left_Bracket||',
             # ']': '||Right_Bracket||',
             # '{': '||Left_Curly_Braces||',
             # '{': '||Right_Curly_Braces||',
             # '<': '||Left_Arrow_Bracket||',
             # '>': '||Right_Arrow_Bracket||',
             # '=': '||Equal||',
             # '@': '||At||',
             # '^': '||Caret||',
             # '`': '||Accent_Mark||',
             # '~': '||Tilda||',
           }

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_tokenize(token_lookup)

Tests Passed

Preprocess all the data and save it

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

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
# Preprocess Training, Validation, and Testing Data
helper.preprocess_and_save_data(data_dir, token_lookup, create_lookup_tables)

Check Point

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

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
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

You'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

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
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: 1.1.0
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)
    """
    # TODO: Implement Function
    inputs = tf.placeholder(tf.int32, [None, None], 'input')
    targets = tf.placeholder(tf.int32, [None, None], 'targets')
    learning_rate = tf.placeholder(tf.float32, name='learning_rate')
    return inputs, targets, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_get_inputs(get_inputs)

Tests Passed

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)
    """
    # TODO: Implement Function
    def build_cell(lstm_size):
        lstm = tf.contrib.rnn.BasicLSTMCell(lstm_size)
        return lstm
    
    lstm_layers = 2
    cell = tf.contrib.rnn.MultiRNNCell([build_cell(rnn_size) for _ in range(lstm_layers)])
#     lstm = tf.contrib.rnn.BasicLSTMCell(rnn_size)
#     cell = tf.contrib.rnn.MultiRNNCell([lstm] * lstm_layers)
    initial_state = tf.identity(cell.zero_state(batch_size, tf.float32), name='initial_state')
    return cell, initial_state


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_get_init_cell(get_init_cell)

Tests Passed

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.
    """
    # TODO: Implement Function
    embedding = tf.Variable(tf.random_normal((vocab_size, embed_dim)))
    return tf.nn.embedding_lookup(embedding, input_data)


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_get_embed(get_embed)

Tests Passed

Build RNN

You 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)
    """
    # TODO: Implement Function
    outputs, final_state = tf.nn.dynamic_rnn(cell, inputs, dtype=tf.float32)
    return outputs, tf.identity(final_state, name='final_state')


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_build_rnn(build_rnn)

Tests Passed

Build the Neural Network

Apply the functions you implemented above to:

Apply embedding to input_data using your get_embed(input_data, vocab_size, embed_dim) function.
Build RNN using cell and your 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)
    """
    # TODO: Implement Function
    embed = get_embed(input_data, vocab_size, embed_dim)
    outputs, final_state = build_rnn(cell, embed)
    logits = tf.contrib.layers.fully_connected(outputs, vocab_size, activation_fn=None)
    return logits, final_state


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_build_nn(build_nn)

Tests Passed

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 you 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
    """
    # TODO: Implement Function
    
    n_batches = len(int_text) // (batch_size * seq_length)
    batches = np.zeros((n_batches, 2, batch_size, seq_length))
    int_text = int_text[:n_batches * batch_size * seq_length]
    for b in range(n_batches):
        for j in range(batch_size):
            # input minibatch
            start = (b+j*n_batches)*seq_length
            batches[b][0][j] = int_text[start:start+seq_length]
            # target minibatch
            end = start+seq_length+1
            if end > len(int_text):
                batches[b][1][j] = int_text[start+1:] + int_text[:end%len(int_text)]
            else:
                batches[b][1][j] = int_text[start+1:end]
    
    return batches


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_get_batches(get_batches)

Tests Passed

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 = 320
# Embedding Dimension Size
embed_dim = 320
# Sequence Length
seq_length = 32
# Learning Rate
learning_rate = 0.005
# Show stats for every n number of batches
show_every_n_batches = 64

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
save_dir = './save'

Build the Graph

Build the graph using the neural network you implemented.

"""
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"""
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.

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
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    0/16   train_loss = 8.823
Epoch   4 Batch    0/16   train_loss = 5.789
Epoch   8 Batch    0/16   train_loss = 5.100
Epoch  12 Batch    0/16   train_loss = 4.633
Epoch  16 Batch    0/16   train_loss = 4.139
Epoch  20 Batch    0/16   train_loss = 3.703
Epoch  24 Batch    0/16   train_loss = 3.185
Epoch  28 Batch    0/16   train_loss = 2.678
Epoch  32 Batch    0/16   train_loss = 2.243
Epoch  36 Batch    0/16   train_loss = 1.916
Epoch  40 Batch    0/16   train_loss = 1.580
Epoch  44 Batch    0/16   train_loss = 1.274
Epoch  48 Batch    0/16   train_loss = 0.925
Epoch  52 Batch    0/16   train_loss = 0.816
Epoch  56 Batch    0/16   train_loss = 0.648
Epoch  60 Batch    0/16   train_loss = 0.488
Epoch  64 Batch    0/16   train_loss = 0.313
Epoch  68 Batch    0/16   train_loss = 0.260
Epoch  72 Batch    0/16   train_loss = 0.238
Epoch  76 Batch    0/16   train_loss = 0.174
Epoch  80 Batch    0/16   train_loss = 0.140
Epoch  84 Batch    0/16   train_loss = 0.141
Epoch  88 Batch    0/16   train_loss = 0.108
Epoch  92 Batch    0/16   train_loss = 0.099
Epoch  96 Batch    0/16   train_loss = 0.095
Model Trained and Saved

Save Parameters

Save seq_length and save_dir for generating a new TV script.

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
# Save parameters for checkpoint
helper.save_params((seq_length, save_dir))

Checkpoint

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
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)
    """
    # TODO: Implement Function
    
    return loaded_graph.get_tensor_by_name('input:0'), \
           loaded_graph.get_tensor_by_name('initial_state:0'), \
           loaded_graph.get_tensor_by_name('final_state:0'), \
           loaded_graph.get_tensor_by_name('probs:0')


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_get_tensors(get_tensors)

Tests Passed

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
    """
    # TODO: Implement Function
    
    return int_to_vocab[int(np.searchsorted(np.cumsum(probabilities), np.random.rand()))]


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_pick_word(pick_word)

Tests Passed

Generate TV Script

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

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

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
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)

INFO:tensorflow:Restoring parameters from ./save
moe_szyslak: goodnight, moon.
moe_szyslak: goodnight, broom. good night jukebox that won't play a tune.
moe_szyslak: goodnight eggs. goodnight dregs. goodnight bugs crawlin' up my dad on my special bannister...


homer_simpson:(to cat) to make some backbone and-- how'd your love...


homer_simpson: moe, this is a girl!
homer_simpson:(happy) hey, there's like not say to move on christmas.
moe_szyslak:(sigh) oh, my friends! please!
marge_simpson: it goin' on the railroad and this hobo, all you should hate drunk by no way. / oh, it's paint.
lenny_leonard: what about you, for them more out? well, i'm your mind it's just 'cause he gave 'em right tonight!
homer_simpson: i'm just a liar, kind of homer's love." business(sigh) i,"." you've never eat your money. good.
homer_simpson: i know a thing or two about tow-joes. i never pulls her back.
carl_carlson: so

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 have you train on all the data, because that would take too long. However, you are free to train your neural network on all the data. After you complete the project, of course.

Name		Name	Last commit message	Last commit date
Latest commit History 2 Commits
data/simpsons		data/simpsons
.floydexpt		.floydexpt
.floydignore		.floydignore
README.md		README.md
helper.py		helper.py
params.p		params.p
preprocess.p		preprocess.p
problem_unittests.py		problem_unittests.py
requirements.txt		requirements.txt
simpsons_script.html		simpsons_script.html
simpsons_script.ipynb		simpsons_script.ipynb

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TV Script Generation

Get the Data

Explore the Data

Implement Preprocessing Functions

Lookup Table

Tokenize Punctuation

Preprocess all the data and save it

Check Point

Build the Neural Network

Check the Version of TensorFlow and Access to GPU

Input

Build RNN Cell and Initialize

Word Embedding

Build RNN

Build the Neural Network

Batches

Neural Network Training

Hyperparameters

Build the Graph

Train

Save Parameters

Checkpoint

Implement Generate Functions

Get Tensors

Choose Word

Generate TV Script

The TV Script is Nonsensical

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bestkao/simpsons_script

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Repository files navigation

TV Script Generation

Get the Data

Explore the Data

Implement Preprocessing Functions

Lookup Table

Tokenize Punctuation

Preprocess all the data and save it

Check Point

Build the Neural Network

Check the Version of TensorFlow and Access to GPU

Input

Build RNN Cell and Initialize

Word Embedding

Build RNN

Build the Neural Network

Batches

Neural Network Training

Hyperparameters

Build the Graph

Train

Save Parameters

Checkpoint

Implement Generate Functions

Get Tensors

Choose Word

Generate TV Script

The TV Script is Nonsensical

About

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