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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1:  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..
Step3:  Explore the Data
Play around with view_sentence_range to view different parts of the data.
Step6:  Implement Preprocessing Functions
The first thing to do to any dataset is preprocessing.  Implement the following preprocessing functions below
Step9:  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
Step11:  Preprocess all the data and save it
Running the code cell below will preprocess all the data and save it to file.
Step13:  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.
Step15:  Build the Neural Network
You'll build the components necessary to build a RNN by implementing the following functions below
Step18:  Input
Implement the get_inputs() function to create TF Placeholders for the Neural Network.  It should create the following placeholders
Step21:  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)
Step24:  Word Embedding
Apply embedding to input_data using TensorFlow.  Return the embedded sequence.
Step27:  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)
Step30:  Build the Neural Network
Apply the functions you implemented above to
Step33:  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
Step35:  Neural Network Training
Hyperparameters
Tune the following parameters
Step37:  Build the Graph
Build the graph using the neural network you implemented.
Step39:  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.
Step41:  Save Parameters
Save seq_length and save_dir for generating a new TV script.
Step43:  Checkpoint
Step46:  Implement Generate Functions
Get Tensors
Get tensors from loaded_graph using the function get_tensor_by_name().  Get the tensors using the following names
Step49:  Choose Word
Implement the pick_word() function to select the next word using probabilities.
Step51:  Generate TV Script
This will generate the TV script for you.  Set gen_length to the length of TV script you want to generate.
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Python Code:
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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:]
Explanation: 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..
End of explanation
view_sentence_range = (10, 20)
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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]]))
Explanation: Explore the Data
Play around with view_sentence_range to view different parts of the data.
End of explanation
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)
    vocab_to_int = {word: idx for idx, word in enumerate(sorted_vocab)}    
    int_to_vocab = {idx: word for idx, word in enumerate(sorted_vocab)}    
    
    return (vocab_to_int, int_to_vocab)
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tests.test_create_lookup_tables(create_lookup_tables)
Explanation: 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)
End of explanation
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
    
    punctuation = {}
    punctuation['.'] = '<PERIOD>'
    punctuation[','] = '<COMMA>'
    punctuation['"'] = '<QUOTATION_MARK>'
    punctuation[';'] = '<SEMICOLON>'
    punctuation['!'] = '<EXCLAMATION_MARK>'
    punctuation['?'] = '<QUESTION_MARK>'
    punctuation['('] = '<LEFT_PAREN>'
    punctuation[')'] = '<RIGHT_PAREN>'
    punctuation['--'] = '<DASH>'
    punctuation['\n'] = '<NEW_LINE>'
    return punctuation
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tests.test_tokenize(token_lookup)
Explanation: 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||".
End of explanation
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# Preprocess Training, Validation, and Testing Data
helper.preprocess_and_save_data(data_dir, token_lookup, create_lookup_tables)
Explanation: Preprocess all the data and save it
Running the code cell below will preprocess all the data and save it to file.
End of explanation
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import helper
import numpy as np
import problem_unittests as tests
int_text, vocab_to_int, int_to_vocab, token_dict = helper.load_preprocess()
Explanation: 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.
End of explanation
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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()))
Explanation: 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
End of explanation
def get_inputs():
    
    Create TF Placeholders for input, targets, and learning rate.
    :return: Tuple (input, targets, learning rate)
    
    inputs = tf.placeholder(tf.int32, [None, None], name='input')
    targets = tf.placeholder(tf.int32, [None, None])
    learning_rate = tf.placeholder(tf.int32)
    return (inputs, targets, learning_rate)
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tests.test_get_inputs(get_inputs)
Explanation: 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 the tuple (Input, Targets, LearingRate)
End of explanation
lstm_layers = 1
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]*lstm_layers)
    initial_state = cell.zero_state(batch_size, tf.int32)
    initial_state = tf.identity(initial_state, name='initial_state')
    return cell, initial_state
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tests.test_get_init_cell(get_init_cell)
Explanation: 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)
End of explanation
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.
    
    
    
    return embed
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tests.test_get_embed(get_embed)
Explanation: Word Embedding
Apply embedding to input_data using TensorFlow.  Return the embedded sequence.
End of explanation
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
    return None, None
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tests.test_build_rnn(build_rnn)
Explanation: 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)
End of explanation
def build_nn(cell, rnn_size, input_data, vocab_size):
    
    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
    :return: Tuple (Logits, FinalState)
    
    # TODO: Implement Function
    return None, None
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tests.test_build_nn(build_nn)
Explanation: 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)
End of explanation
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
    return None
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tests.test_get_batches(get_batches)
Explanation: 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]]
  ]
]
```
End of explanation
# Number of Epochs
num_epochs = None
# Batch Size
batch_size = None
# RNN Size
rnn_size = None
# Sequence Length
seq_length = None
# Learning Rate
learning_rate = None
# Show stats for every n number of batches
show_every_n_batches = None
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save_dir = './save'
Explanation: 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 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.
End of explanation
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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)
    # 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]
    train_op = optimizer.apply_gradients(capped_gradients)
Explanation: Build the Graph
Build the graph using the neural network you implemented.
End of explanation
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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')
Explanation: 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.
End of explanation
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# Save parameters for checkpoint
helper.save_params((seq_length, save_dir))
Explanation: Save Parameters
Save seq_length and save_dir for generating a new TV script.
End of explanation
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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()
Explanation: Checkpoint
End of explanation
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 None, None, None, None
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tests.test_get_tensors(get_tensors)
Explanation: 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)
End of explanation
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 None
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tests.test_pick_word(pick_word)
Explanation: Choose Word
Implement the pick_word() function to select the next word using probabilities.
End of explanation
gen_length = 200
# homer_simpson, moe_szyslak, or Barney_Gumble
prime_word = 'moe_szyslak'
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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)
Explanation: Generate TV Script
This will generate the TV script for you.  Set gen_length to the length of TV script you want to generate.
End of explanation
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