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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:
 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
Step1:  Explore the Data
Step3:  Preprocessing
Lookup Table
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)
Step5:  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
Step6:  Preprocess all the data and save it
Running the code cell below will preprocess all the data and save it to file.
Step7:  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.
Step8:  Build the Neural Network
Check the Version of TensorFlow and Access to GPU
Step10:  Input
Step12:  Word Embedding
Step14:  Build RNN Cell and Initialize
Step16:  Build RNN
Step18:  Build the Neural Network
Apply the functions you implemented above to
Step20:  Batches
Step21:  Neural Network Training
Hyperparameters
Step22:  Build the Graph
Build the graph using the neural network you implemented.
Step23:  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.
Step24:  Save Parameters
Step25:  Checkpoint
Step27:  Implement Generate Functions
Get Tensors
Get tensors from loaded_graph using the function get_tensor_by_name().  Get the tensors using the following names
Step29:  Choose Word
Implement the pick_word() function to select the next word using probabilities.
Step30:  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:
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
End of explanation
view_sentence_range = (20, 30)
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
End of explanation
import numpy as np
import common
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)
        
    vocab_to_int, int_to_vocab = common.create_lookup_tables(text)
    
    return vocab_to_int, int_to_vocab
tests.test_create_lookup_tables(create_lookup_tables)
Explanation: Preprocessing
Lookup Table
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
        
    x = {
        '.'  : '||period||',
        ','  : '||comma||',
        '"'  : '||quote||',
        '('  : '||left_bracket||',
        '?'  : '||question_mark||',
        '!'  : '||exclamation||',
        '\n' : '||new_line||',
        ')'  : '||right_bracket||',
        ';'  : '||semi-colon||' ,
        '--' : '||dash_dash||'
        }
    
    return x
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:
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
# 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
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
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
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)
    
    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
tests.test_get_inputs(get_inputs)
Explanation: Input
End of explanation
def get_embed(input_, n_vocab, n_embedding):
    
    Create embedding for <input_data>.
    :param input_: TF placeholder for text input.
    :param n_vocab: Number of words in vocabulary.
    :param n_embedding: Number of embedding dimensions
    :return: Embedded input.
    
    
    embedding = tf.Variable( tf.random_uniform( (n_vocab, n_embedding)))
    embed = tf.nn.embedding_lookup( embedding, input_) 
    
    return embed
tests.test_get_embed(get_embed)
Explanation: Word Embedding
End of explanation
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)
    
    lstms = [tf.contrib.rnn.BasicLSTMCell(rnn_size)]
    cell = tf.contrib.rnn.MultiRNNCell( lstms )
    initial_state = cell.zero_state(batch_size, tf.float32)
    initial_state = tf.identity(initial_state, name="initial_state")
    
    return cell, initial_state
tests.test_get_init_cell(get_init_cell)
Explanation: Build RNN Cell and Initialize
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)
    
   
    outputs, state = tf.nn.dynamic_rnn(cell, inputs, dtype=tf.float32)   
    state = tf.identity(state, name="final_state")
    
    return outputs, state
tests.test_build_rnn(build_rnn)
Explanation: Build RNN
End of explanation
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, state = build_rnn(cell, embed)
    
    logits = tf.contrib.layers.fully_connected(outputs, vocab_size, activation_fn=None,
                                              weights_initializer = tf.truncated_normal_initializer(
                                                  mean = 0.0, stddev = .1),
                                              biases_initializer=tf.zeros_initializer() )
    
    return logits, state
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
    
    
    int_text = int_text[0:(len(int_text) - len(int_text) % (batch_size * seq_length))]
    
    targets = np.zeros(len(int_text)).astype(int)
    targets[:-1] = int_text[1:]
    targets[-1] = int_text[0]
    
    elements_per_batch = batch_size * seq_length
    
    num_batches = int(len(int_text) / elements_per_batch)
    
    # Build the batched data
    batches = np.zeros((num_batches, 2, batch_size, seq_length)).astype(int)
    
    for sequence in range(batch_size):
        for batch in range(num_batches):
            
            start_at = (batch * seq_length) + ( sequence * seq_length * num_batches )
            # Append to inputs
            batches[batch, 0, sequence, :] = int_text[start_at:(start_at + seq_length)]
            # Append to targets
            batches[batch, 1, sequence, :] = targets[start_at:(start_at + seq_length)]
    
    return batches
    
tests.test_get_batches(get_batches)
Explanation: Batches
End of explanation
num_epochs = 200
batch_size = 128
rnn_size = 256
embed_dim = 200
seq_length = 15
learning_rate = .001
show_every_n_batches = 100
save_dir = './save'
Explanation: Neural Network Training
Hyperparameters
End of explanation
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)
Explanation: Build the Graph
Build the graph using the neural network you implemented.
End of explanation
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
# Save parameters for checkpoint
helper.save_params((seq_length, save_dir))
Explanation: Save Parameters
End of explanation
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)
    
    
    tensors = ["input:0", "initial_state:0", "final_state:0", "probs:0"]
    output = []
    
    for t in tensors:
        output.append( loaded_graph.get_tensor_by_name(t) )
    return output[0], output[1], output[2], output[3]
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
    
    return int_to_vocab[np.argmax(probabilities)]
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 = 'homer_simpson'
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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