Implementing an LSTM RNN Model

Implementing an LSTM RNN Model

# -*- coding: utf-8 -*-
#
# Implementing an LSTM RNN Model
#------------------------------
#  Here we implement an LSTM model on all a data set of Shakespeare works.
#
#
#

import os
import re
import string
import requests
import numpy as np
import collections
import random
import pickle
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.python.framework import ops
ops.reset_default_graph()

# Start a session
sess = tf.Session()

# Set RNN Parameters
min_word_freq = 5  # Trim the less frequent words off
rnn_size = 128  # RNN Model size
epochs = 10  # Number of epochs to cycle through data
batch_size = 100  # Train on this many examples at once
learning_rate = 0.001  # Learning rate
training_seq_len = 50  # how long of a word group to consider
embedding_size = rnn_size  # Word embedding size
save_every = 500  # How often to save model checkpoints
eval_every = 50  # How often to evaluate the test sentences
prime_texts = ['thou art more', 'to be or not to', 'wherefore art thou']

# Download/store Shakespeare data
data_dir = 'temp'
data_file = 'shakespeare.txt'
model_path = 'shakespeare_model'
full_model_dir = os.path.join(data_dir, model_path)

# Declare punctuation to remove, everything except hyphens and apostrophes
punctuation = string.punctuation
punctuation = ''.join([x for x in punctuation if x not in ['-', "'"]])

# Make Model Directory
if not os.path.exists(full_model_dir):
    os.makedirs(full_model_dir)

# Make data directory
if not os.path.exists(data_dir):
    os.makedirs(data_dir)

print('Loading Shakespeare Data')
# Check if file is downloaded.
if not os.path.isfile(os.path.join(data_dir, data_file)):
    print('Not found, downloading Shakespeare texts from www.gutenberg.org')
    shakespeare_url = 'http://www.gutenberg.org/cache/epub/100/pg100.txt'
    # Get Shakespeare text
    response = requests.get(shakespeare_url)
    shakespeare_file = response.content
    # Decode binary into string
    s_text = shakespeare_file.decode('utf-8')
    # Drop first few descriptive paragraphs.
    s_text = s_text[7675:]
    # Remove newlines
    s_text = s_text.replace('\r\n', '')
    s_text = s_text.replace('\n', '')
    
    # Write to file
    with open(os.path.join(data_dir, data_file), 'w') as out_conn:
        out_conn.write(s_text)
else:
    # If file has been saved, load from that file
    with open(os.path.join(data_dir, data_file), 'r') as file_conn:
        s_text = file_conn.read().replace('\n', '')

# Clean text
print('Cleaning Text')
s_text = re.sub(r'[{}]'.format(punctuation), ' ', s_text)
s_text = re.sub('\s+', ' ', s_text).strip().lower()


# Build word vocabulary function
def build_vocab(text, min_freq):
    word_counts = collections.Counter(text.split(' '))
    # limit word counts to those more frequent than cutoff
    word_counts = {key: val for key, val in word_counts.items() if val > min_freq}
    # Create vocab --> index mapping
    words = word_counts.keys()
    vocab_to_ix_dict = {key: (i_x+1) for i_x, key in enumerate(words)}
    # Add unknown key --> 0 index
    vocab_to_ix_dict['unknown'] = 0
    # Create index --> vocab mapping
    ix_to_vocab_dict = {val: key for key, val in vocab_to_ix_dict.items()}
    
    return ix_to_vocab_dict, vocab_to_ix_dict


# Build Shakespeare vocabulary
print('Building Shakespeare Vocab')
ix2vocab, vocab2ix = build_vocab(s_text, min_word_freq)
vocab_size = len(ix2vocab) + 1
print('Vocabulary Length = {}'.format(vocab_size))
# Sanity Check
assert(len(ix2vocab) == len(vocab2ix))

# Convert text to word vectors
s_text_words = s_text.split(' ')
s_text_ix = []
for ix, x in enumerate(s_text_words):
    try:
        s_text_ix.append(vocab2ix[x])
    except KeyError:
        s_text_ix.append(0)
s_text_ix = np.array(s_text_ix)


# Define LSTM RNN Model
class LSTM_Model():
    def __init__(self, embedding_size, rnn_size, batch_size, learning_rate,
                 training_seq_len, vocab_size, infer_sample=False):
        self.embedding_size = embedding_size
        self.rnn_size = rnn_size
        self.vocab_size = vocab_size
        self.infer_sample = infer_sample
        self.learning_rate = learning_rate
        
        if infer_sample:
            self.batch_size = 1
            self.training_seq_len = 1
        else:
            self.batch_size = batch_size
            self.training_seq_len = training_seq_len
        
        self.lstm_cell = tf.contrib.rnn.BasicLSTMCell(self.rnn_size)
        self.initial_state = self.lstm_cell.zero_state(self.batch_size, tf.float32)
        
        self.x_data = tf.placeholder(tf.int32, [self.batch_size, self.training_seq_len])
        self.y_output = tf.placeholder(tf.int32, [self.batch_size, self.training_seq_len])
        
        with tf.variable_scope('lstm_vars'):
            # Softmax Output Weights
            W = tf.get_variable('W', [self.rnn_size, self.vocab_size], tf.float32, tf.random_normal_initializer())
            b = tf.get_variable('b', [self.vocab_size], tf.float32, tf.constant_initializer(0.0))
        
            # Define Embedding
            embedding_mat = tf.get_variable('embedding_mat', [self.vocab_size, self.embedding_size],
                                            tf.float32, tf.random_normal_initializer())
                                            
            embedding_output = tf.nn.embedding_lookup(embedding_mat, self.x_data)
            rnn_inputs = tf.split(axis=1, num_or_size_splits=self.training_seq_len, value=embedding_output)
            rnn_inputs_trimmed = [tf.squeeze(x, [1]) for x in rnn_inputs]
        
        # If we are inferring (generating text), we add a 'loop' function
        # Define how to get the i+1 th input from the i th output
        def inferred_loop(prev):
            # Apply hidden layer
            prev_transformed = tf.matmul(prev, W) + b
            # Get the index of the output (also don't run the gradient)
            prev_symbol = tf.stop_gradient(tf.argmax(prev_transformed, 1))
            # Get embedded vector
            out = tf.nn.embedding_lookup(embedding_mat, prev_symbol)
            return out
        
        decoder = tf.contrib.legacy_seq2seq.rnn_decoder
        outputs, last_state = decoder(rnn_inputs_trimmed,
                                      self.initial_state,
                                      self.lstm_cell,
                                      loop_function=inferred_loop if infer_sample else None)
        # Non inferred outputs
        output = tf.reshape(tf.concat(axis=1, values=outputs), [-1, self.rnn_size])
        # Logits and output
        self.logit_output = tf.matmul(output, W) + b
        self.model_output = tf.nn.softmax(self.logit_output)
        
        loss_fun = tf.contrib.legacy_seq2seq.sequence_loss_by_example
        loss = loss_fun([self.logit_output], [tf.reshape(self.y_output, [-1])],
                        [tf.ones([self.batch_size * self.training_seq_len])])
        self.cost = tf.reduce_sum(loss) / (self.batch_size * self.training_seq_len)
        self.final_state = last_state
        gradients, _ = tf.clip_by_global_norm(tf.gradients(self.cost, tf.trainable_variables()), 4.5)
        optimizer = tf.train.AdamOptimizer(self.learning_rate)
        self.train_op = optimizer.apply_gradients(zip(gradients, tf.trainable_variables()))
        
    def sample(self, sess, words=ix2vocab, vocab=vocab2ix, num=10, prime_text='thou art'):
        state = sess.run(self.lstm_cell.zero_state(1, tf.float32))
        word_list = prime_text.split()
        for word in word_list[:-1]:
            x = np.zeros((1, 1))
            x[0, 0] = vocab[word]
            feed_dict = {self.x_data: x, self.initial_state: state}
            [state] = sess.run([self.final_state], feed_dict=feed_dict)

        out_sentence = prime_text
        word = word_list[-1]
        for n in range(num):
            x = np.zeros((1, 1))
            x[0, 0] = vocab[word]
            feed_dict = {self.x_data: x, self.initial_state: state}
            [model_output, state] = sess.run([self.model_output, self.final_state], feed_dict=feed_dict)
            sample = np.argmax(model_output[0])
            if sample == 0:
                break
            word = words[sample]
            out_sentence = out_sentence + ' ' + word
        return out_sentence


# Define LSTM Model
lstm_model = LSTM_Model(embedding_size, rnn_size, batch_size, learning_rate,
                        training_seq_len, vocab_size)

# Tell TensorFlow we are reusing the scope for the testing
with tf.variable_scope(tf.get_variable_scope(), reuse=True):
    test_lstm_model = LSTM_Model(embedding_size, rnn_size, batch_size, learning_rate,
                                 training_seq_len, vocab_size, infer_sample=True)


# Create model saver
saver = tf.train.Saver(tf.global_variables())

# Create batches for each epoch
num_batches = int(len(s_text_ix)/(batch_size * training_seq_len)) + 1
# Split up text indices into subarrays, of equal size
batches = np.array_split(s_text_ix, num_batches)
# Reshape each split into [batch_size, training_seq_len]
batches = [np.resize(x, [batch_size, training_seq_len]) for x in batches]

# Initialize all variables
init = tf.global_variables_initializer()
sess.run(init)

# Train model
train_loss = []
iteration_count = 1
for epoch in range(epochs):
    # Shuffle word indices
    random.shuffle(batches)
    # Create targets from shuffled batches
    targets = [np.roll(x, -1, axis=1) for x in batches]
    # Run a through one epoch
    print('Starting Epoch #{} of {}.'.format(epoch+1, epochs))
    # Reset initial LSTM state every epoch
    state = sess.run(lstm_model.initial_state)
    for ix, batch in enumerate(batches):
        training_dict = {lstm_model.x_data: batch, lstm_model.y_output: targets[ix]}
        c, h = lstm_model.initial_state
        training_dict[c] = state.c
        training_dict[h] = state.h
        
        temp_loss, state, _ = sess.run([lstm_model.cost, lstm_model.final_state, lstm_model.train_op],
                                       feed_dict=training_dict)
        train_loss.append(temp_loss)
        
        # Print status every 10 gens
        if iteration_count % 10 == 0:
            summary_nums = (iteration_count, epoch+1, ix+1, num_batches+1, temp_loss)
            print('Iteration: {}, Epoch: {}, Batch: {} out of {}, Loss: {:.2f}'.format(*summary_nums))
        
        # Save the model and the vocab
        if iteration_count % save_every == 0:
            # Save model
            model_file_name = os.path.join(full_model_dir, 'model')
            saver.save(sess, model_file_name, global_step=iteration_count)
            print('Model Saved To: {}'.format(model_file_name))
            # Save vocabulary
            dictionary_file = os.path.join(full_model_dir, 'vocab.pkl')
            with open(dictionary_file, 'wb') as dict_file_conn:
                pickle.dump([vocab2ix, ix2vocab], dict_file_conn)
        
        if iteration_count % eval_every == 0:
            for sample in prime_texts:
                print(test_lstm_model.sample(sess, ix2vocab, vocab2ix, num=10, prime_text=sample))
                
        iteration_count += 1


# Plot loss over time
plt.plot(train_loss, 'k-')
plt.title('Sequence to Sequence Loss')
plt.xlabel('Generation')
plt.ylabel('Loss')
plt.show()