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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
from collections import Counter
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)
    """
    word_counts = Counter(text)
    sorted_vocab = sorted(word_counts, key=word_counts.get, reverse=True)
    int_to_vocab = {ii: text for ii, text in enumerate(sorted_vocab)}
    vocab_to_int = {text: ii for ii, text in int_to_vocab.items()}
    
    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
    """
    tokenize = {}
    tokenize['.']="||period||"
    tokenize[',']="||comma||"
    tokenize['"']="||quotation_mark||"
    tokenize[';']="||semicolon||"
    tokenize['!']="||exclamation_mark||"
    tokenize['?']="||question_mark||"
    tokenize['(']="||left_parantheses||"
    tokenize[')']="||right_parantheses||"
    tokenize['--'] = "||dash||"
    tokenize['\n'] = "||return||"
    
    return tokenize

"""
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: 1.0.0
Default GPU Device: /gpu:0

tf.test.gpu_device_name()
'/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)


"""
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)

# 这里的模型需要不断的调整
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)
    """
    #batch_size = tf.placeholder(tf.int32, [], name='batch_size')
    lstm1 = tf.contrib.rnn.BasicLSTMCell(rnn_size)
    lstm2 = tf.contrib.rnn.BasicLSTMCell(rnn_size)
    lstm3 = tf.contrib.rnn.BasicLSTMCell(rnn_size)
    cell = tf.contrib.rnn.MultiRNNCell([lstm1,lstm2,lstm3])
    initial_state = cell.zero_state(batch_size, tf.float32)
    initial_state = tf.identity(initial_state, name='initial_state')
    return cell, initial_state
    
#     lstm1 = tf.contrib.rnn.BasicLSTMCell(rnn_size)
#     lstm2 = tf.contrib.rnn.BasicLSTMCell(rnn_size)
#     cell = tf.contrib.rnn.MultiRNNCell([lstm1,lstm2])
#     initial_state = tf.identity(cell.zero_state(batch_size, tf.float32), "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.
    """
    embedding = tf.Variable(tf.random_uniform((vocab_size, embed_dim), -1, 1))
    embed = tf.nn.embedding_lookup(embedding, input_data)
    return embed


"""
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.

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='float32')
    final_state = tf.identity(final_state, name='final_state')
    return outputs, 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)
    """
    embed = get_embed(input_data, vocab_size, embed_dim)
    outputs, final_state = build_rnn(cell, embed)
    weights = tf.truncated_normal_initializer(stddev=0.1)
    logits = tf.contrib.layers.fully_connected(outputs, 
                                               vocab_size, 
                                               activation_fn=None,
                                               weights_initializer=weights,
                                               biases_initializer=tf.zeros_initializer())
    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], 2, 3) would return a Numpy array of the following:

[
  # First Batch
  [
    # Batch of Input
    [[ 1  2  3], [ 7  8  9]],
    # Batch of targets
    [[ 2  3  4], [ 8  9 10]]
  ],
 
  # Second Batch
  [
    # Batch of Input
    [[ 4  5  6], [10 11 12]],
    # Batch of targets
    [[ 5  6  7], [11 12 13]]
  ]
]

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))
    # Drop the last few characters to make only full batches
    xdata = np.array(int_text[: n_batches * batch_size * seq_length])
    ydata = np.array(int_text[1: n_batches * batch_size * seq_length + 1])
    ydata[-1] = xdata[0]

    x_batches = np.split(xdata.reshape(batch_size, -1), n_batches, 1)
    y_batches = np.split(ydata.reshape(batch_size, -1), n_batches, 1)
    return np.array(list(zip(x_batches, y_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 = 1000
# Batch Size
batch_size = 128
# RNN Size
rnn_size = 256
# Embedding Dimension Size
embed_dim = 600
# Sequence Length
seq_length = 50
# Learning Rate
learning_rate = 0.001
# 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.

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
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    0/10   train_loss = 8.827
Epoch   6 Batch    4/10   train_loss = 5.809
Epoch  12 Batch    8/10   train_loss = 5.511
Epoch  19 Batch    2/10   train_loss = 5.055
Epoch  25 Batch    6/10   train_loss = 4.761
Epoch  32 Batch    0/10   train_loss = 4.520
Epoch  38 Batch    4/10   train_loss = 4.265
Epoch  44 Batch    8/10   train_loss = 4.099
Epoch  51 Batch    2/10   train_loss = 4.016
Epoch  57 Batch    6/10   train_loss = 3.947
Epoch  64 Batch    0/10   train_loss = 3.810
Epoch  70 Batch    4/10   train_loss = 3.662
Epoch  76 Batch    8/10   train_loss = 3.564
Epoch  83 Batch    2/10   train_loss = 3.414
Epoch  89 Batch    6/10   train_loss = 3.538
Epoch  96 Batch    0/10   train_loss = 3.219
Epoch 102 Batch    4/10   train_loss = 3.273
Epoch 108 Batch    8/10   train_loss = 2.996
Epoch 115 Batch    2/10   train_loss = 2.954
Epoch 121 Batch    6/10   train_loss = 2.874
Epoch 128 Batch    0/10   train_loss = 2.678
Epoch 134 Batch    4/10   train_loss = 2.840
Epoch 140 Batch    8/10   train_loss = 2.539
Epoch 147 Batch    2/10   train_loss = 2.430
Epoch 153 Batch    6/10   train_loss = 2.372
Epoch 160 Batch    0/10   train_loss = 2.271
Epoch 166 Batch    4/10   train_loss = 2.186
Epoch 172 Batch    8/10   train_loss = 2.071
Epoch 179 Batch    2/10   train_loss = 1.997
Epoch 185 Batch    6/10   train_loss = 1.885
Epoch 192 Batch    0/10   train_loss = 1.849
Epoch 198 Batch    4/10   train_loss = 1.746
Epoch 204 Batch    8/10   train_loss = 1.579
Epoch 211 Batch    2/10   train_loss = 1.577
Epoch 217 Batch    6/10   train_loss = 1.579
Epoch 224 Batch    0/10   train_loss = 1.426
Epoch 230 Batch    4/10   train_loss = 1.424
Epoch 236 Batch    8/10   train_loss = 1.271
Epoch 243 Batch    2/10   train_loss = 1.347
Epoch 249 Batch    6/10   train_loss = 1.111
Epoch 256 Batch    0/10   train_loss = 1.087
Epoch 262 Batch    4/10   train_loss = 1.103
Epoch 268 Batch    8/10   train_loss = 0.954
Epoch 275 Batch    2/10   train_loss = 1.058
Epoch 281 Batch    6/10   train_loss = 0.906
Epoch 288 Batch    0/10   train_loss = 0.807
Epoch 294 Batch    4/10   train_loss = 0.956
Epoch 300 Batch    8/10   train_loss = 0.755
Epoch 307 Batch    2/10   train_loss = 0.667
Epoch 313 Batch    6/10   train_loss = 0.705
Epoch 320 Batch    0/10   train_loss = 0.658
Epoch 326 Batch    4/10   train_loss = 0.589
Epoch 332 Batch    8/10   train_loss = 0.573
Epoch 339 Batch    2/10   train_loss = 0.526
Epoch 345 Batch    6/10   train_loss = 0.563
Epoch 352 Batch    0/10   train_loss = 0.541
Epoch 358 Batch    4/10   train_loss = 0.470
Epoch 364 Batch    8/10   train_loss = 0.382
Epoch 371 Batch    2/10   train_loss = 0.381
Epoch 377 Batch    6/10   train_loss = 0.439
Epoch 384 Batch    0/10   train_loss = 0.449
Epoch 390 Batch    4/10   train_loss = 0.320
Epoch 396 Batch    8/10   train_loss = 0.262
Epoch 403 Batch    2/10   train_loss = 0.250
Epoch 409 Batch    6/10   train_loss = 0.244
Epoch 416 Batch    0/10   train_loss = 0.241
Epoch 422 Batch    4/10   train_loss = 0.556
Epoch 428 Batch    8/10   train_loss = 0.323
Epoch 435 Batch    2/10   train_loss = 0.200
Epoch 441 Batch    6/10   train_loss = 0.180
Epoch 448 Batch    0/10   train_loss = 0.161
Epoch 454 Batch    4/10   train_loss = 0.160
Epoch 460 Batch    8/10   train_loss = 0.151
Epoch 467 Batch    2/10   train_loss = 0.150
Epoch 473 Batch    6/10   train_loss = 0.316
Epoch 480 Batch    0/10   train_loss = 0.379
Epoch 486 Batch    4/10   train_loss = 0.147
Epoch 492 Batch    8/10   train_loss = 0.126
Epoch 499 Batch    2/10   train_loss = 0.120
Epoch 505 Batch    6/10   train_loss = 0.112
Epoch 512 Batch    0/10   train_loss = 0.104
Epoch 518 Batch    4/10   train_loss = 0.103
Epoch 524 Batch    8/10   train_loss = 0.097
Epoch 531 Batch    2/10   train_loss = 0.095
Epoch 537 Batch    6/10   train_loss = 0.090
Epoch 544 Batch    0/10   train_loss = 0.085
Epoch 550 Batch    4/10   train_loss = 0.086
Epoch 556 Batch    8/10   train_loss = 0.082
Epoch 563 Batch    2/10   train_loss = 0.081
Epoch 569 Batch    6/10   train_loss = 0.076
Epoch 576 Batch    0/10   train_loss = 0.073
Epoch 582 Batch    4/10   train_loss = 0.075
Epoch 588 Batch    8/10   train_loss = 0.072
Epoch 595 Batch    2/10   train_loss = 0.071
Epoch 601 Batch    6/10   train_loss = 0.067
Epoch 608 Batch    0/10   train_loss = 0.065
Epoch 614 Batch    4/10   train_loss = 0.067
Epoch 620 Batch    8/10   train_loss = 0.064
Epoch 627 Batch    2/10   train_loss = 0.064
Epoch 633 Batch    6/10   train_loss = 0.061
Epoch 640 Batch    0/10   train_loss = 0.059
Epoch 646 Batch    4/10   train_loss = 0.061
Epoch 652 Batch    8/10   train_loss = 0.059
Epoch 659 Batch    2/10   train_loss = 0.058
Epoch 665 Batch    6/10   train_loss = 0.057
Epoch 672 Batch    0/10   train_loss = 0.055
Epoch 678 Batch    4/10   train_loss = 0.057
Epoch 684 Batch    8/10   train_loss = 0.056
Epoch 691 Batch    2/10   train_loss = 0.063
Epoch 697 Batch    6/10   train_loss = 0.523
Epoch 704 Batch    0/10   train_loss = 0.249
Epoch 710 Batch    4/10   train_loss = 0.090
Epoch 716 Batch    8/10   train_loss = 0.062
Epoch 723 Batch    2/10   train_loss = 0.058
Epoch 729 Batch    6/10   train_loss = 0.056
Epoch 736 Batch    0/10   train_loss = 0.054
Epoch 742 Batch    4/10   train_loss = 0.055
Epoch 748 Batch    8/10   train_loss = 0.054
Epoch 755 Batch    2/10   train_loss = 0.052
Epoch 761 Batch    6/10   train_loss = 0.051
Epoch 768 Batch    0/10   train_loss = 0.050
Epoch 774 Batch    4/10   train_loss = 0.051
Epoch 780 Batch    8/10   train_loss = 0.051
Epoch 787 Batch    2/10   train_loss = 0.049
Epoch 793 Batch    6/10   train_loss = 0.049
Epoch 800 Batch    0/10   train_loss = 0.048
Epoch 806 Batch    4/10   train_loss = 0.049
Epoch 812 Batch    8/10   train_loss = 0.049
Epoch 819 Batch    2/10   train_loss = 0.047
Epoch 825 Batch    6/10   train_loss = 0.047
Epoch 832 Batch    0/10   train_loss = 0.046
Epoch 838 Batch    4/10   train_loss = 0.048
Epoch 844 Batch    8/10   train_loss = 0.048
Epoch 851 Batch    2/10   train_loss = 0.046
Epoch 857 Batch    6/10   train_loss = 0.046
Epoch 864 Batch    0/10   train_loss = 0.045
Epoch 870 Batch    4/10   train_loss = 0.047
Epoch 876 Batch    8/10   train_loss = 0.047
Epoch 883 Batch    2/10   train_loss = 0.045
Epoch 889 Batch    6/10   train_loss = 0.045
Epoch 896 Batch    0/10   train_loss = 0.044
Epoch 902 Batch    4/10   train_loss = 0.046
Epoch 908 Batch    8/10   train_loss = 0.046
Epoch 915 Batch    2/10   train_loss = 0.044
Epoch 921 Batch    6/10   train_loss = 0.045
Epoch 928 Batch    0/10   train_loss = 0.043
Epoch 934 Batch    4/10   train_loss = 0.045
Epoch 940 Batch    8/10   train_loss = 0.045
Epoch 947 Batch    2/10   train_loss = 0.044
Epoch 953 Batch    6/10   train_loss = 0.044
Epoch 960 Batch    0/10   train_loss = 0.043
Epoch 966 Batch    4/10   train_loss = 0.045
Epoch 972 Batch    8/10   train_loss = 0.045
Epoch 979 Batch    2/10   train_loss = 0.043
Epoch 985 Batch    6/10   train_loss = 0.044
Epoch 992 Batch    0/10   train_loss = 0.042
Epoch 998 Batch    4/10   train_loss = 0.044
Model Trained and Saved

稍稍过拟合.

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)
    """
    input_tensor = loaded_graph.get_tensor_by_name('input:0')
    initial_state_tensor = loaded_graph.get_tensor_by_name('initial_state:0')
    final_state_tensor = loaded_graph.get_tensor_by_name('final_state:0')
    probs_tensor = loaded_graph.get_tensor_by_name('probs:0')
    
    return input_tensor, initial_state_tensor, final_state_tensor, probs_tensor


"""
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
    """
    return int_to_vocab[np.argmax(probabilities)]


"""
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 = 300
# 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)
moe_szyslak: oh, so you're looking for a mr. smithers, eh? first name, waylon, is it?(suddenly vicious) listen to me, you... when i catch you, i'm going to pull out your eyes and shove 'em up your alley!
homer_simpson:(terrified noise) can't believe there's me we'd all everyone, fellas i even ya, i'm just a rough cheer, there's what you gave you the greatest room the world, that's the wire.(singing) too happened, even you your last sickens, i'm one.


grampa_simpson: here's the beauty part with me next military saturday food better.
barney_gumble: hey, i'm sorry, it'll get a guy(indicates voice) who's a guess... when i had something with my fans? but homer, all right i should steal your new air conditioner.
moe_szyslak: shut a keys today.
bart_simpson:(offended) are that dumb?
chief_wiggum:(singing) dad, you were better this bar about if they been away more there, and you can good friends. maybe i can believe our guy barney been a duff?
moe_szyslak: this is a great product, this is smooth, glass whale, who's time, and cecil hampstead-on-cecil-cecil.
moe_szyslak: so, uh, you little rutabaga brain, i'll take your eyeball and me that that a dank. the dank!
homer_simpson:(quietly) i'm so take anything to have to take a money.(laughs)
homer_simpson:(excited) yeah. you've got me-- she since duff hounds to

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.

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_tv_script_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.