import pandas as pd

import numpy as np

import tensorflow as tf

import nltk, re, time

from nltk.corpus import stopwords

from collections import defaultdict

from tqdm import tqdm

from sklearn.model_selection import train_test_split

from keras.preprocessing.text import Tokenizer

from keras.preprocessing.sequence import pad_sequences

from collections import namedtuple

def clean_text(text, remove_stopwords=True):

    '''Clean the text, with the option to remove stopwords'''

    # Convert words to lower case and split them

    text = text.lower().split()

    # Optionally, remove stop words

    if remove_stopwords:

        stops = set(stopwords.words("english"))

        text = [w for w in text if not w in stops]

    text = " ".join(text)

    # Clean the text

    text = re.sub(r"<br />", " ", text)

    text = re.sub(r"[^a-z]", " ", text)

    text = re.sub(r"   ", " ", text) # Remove any extra spaces

    text = re.sub(r"  ", " ", text)

    # Return a list of words

    return(text)

Data clean

# Tokenize the reviews

all_reviews = train_clean + test_clean

tokenizer = Tokenizer()

tokenizer.fit_on_texts(all_reviews)

print("Fitting is complete.")

train_seq = tokenizer.texts_to_sequences(train_clean)

print("train_seq is complete.")

test_seq = tokenizer.texts_to_sequences(test_clean)

print("test_seq is complete")

word_index = tokenizer.word_index

[“The”, “cat”, “went”, “to”, “the”, “zoo”, “.”] --> [1, 2, 3, 4, 1, 5, 6]

def build_rnn(n_words, embed_size, batch_size, lstm_size, num_layers, dropout, learning_rate, multiple_fc, fc_units):

    '''Build the Recurrent Neural Network'''

    tf.reset_default_graph()

    # Declare placeholders we'll feed into the graph

    with tf.name_scope('inputs'):

        inputs = tf.placeholder(tf.int32, [None, None], name='inputs')

    with tf.name_scope('labels'):

        labels = tf.placeholder(tf.int32, [None, None], name='labels')

    keep_prob = tf.placeholder(tf.float32, name='keep_prob')

    # Create the embeddings

    with tf.name_scope("embeddings"):

        embedding = tf.Variable(tf.random_uniform((n_words, embed_size), -1, 1))

        embed = tf.nn.embedding_lookup(embedding, inputs)

    # Build the RNN layers

    with tf.name_scope("RNN_layers"):

        lstm = tf.contrib.rnn.BasicLSTMCell(lstm_size)

        drop = tf.contrib.rnn.DropoutWrapper(lstm, output_keep_prob=keep_prob)

        cell = tf.contrib.rnn.MultiRNNCell([drop] * num_layers)

    # Set the initial state

    with tf.name_scope("RNN_init_state"):

        initial_state = cell.zero_state(batch_size, tf.float32)

    # Run the data through the RNN layers

    with tf.name_scope("RNN_forward"):

        outputs, final_state = tf.nn.dynamic_rnn(

                                        cell,

                                        embed,

                                        initial_state=initial_state)    

    # Create the fully connected layers

    with tf.name_scope("fully_connected"):

        # Initialize the weights and biases

        weights = tf.truncated_normal_initializer(stddev=0.1)

        biases  = tf.zeros_initializer()

        dense = tf.contrib.layers.fully_connected(outputs[:, -1],

                    num_outputs = fc_units,

                    activation_fn = tf.sigmoid,

                    weights_initializer = weights,

                    biases_initializer = biases)

        dense = tf.contrib.layers.dropout(dense, keep_prob)

        # Depending on the iteration, use a second fully connected

          layer

        if multiple_fc == True:

            dense = tf.contrib.layers.fully_connected(dense,

                        num_outputs = fc_units,

                        activation_fn = tf.sigmoid,

                        weights_initializer = weights,

                        biases_initializer = biases)

            dense = tf.contrib.layers.dropout(dense, keep_prob)

    # Make the predictions

    with tf.name_scope('predictions'):

        predictions = tf.contrib.layers.fully_connected(dense,

                          num_outputs = 1,

                          activation_fn=tf.sigmoid,

                          weights_initializer = weights,

                          biases_initializer = biases)

        tf.summary.histogram('predictions', predictions)

    # Calculate the cost

    with tf.name_scope('cost'):

        cost = tf.losses.mean_squared_error(labels, predictions)

        tf.summary.scalar('cost', cost)

    # Train the model

    with tf.name_scope('train'):

        optimizer =

            tf.train.AdamOptimizer(learning_rate).minimize(cost)

    # Determine the accuracy

    with tf.name_scope("accuracy"):

        correct_pred = tf.equal(tf.cast(tf.round(predictions),

                                        tf.int32),

                                        labels)

        accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

        tf.summary.scalar('accuracy', accuracy)

    # Merge all of the summaries

    merged = tf.summary.merge_all()    

    # Export the nodes

    export_nodes = ['inputs', 'labels', 'keep_prob','initial_state',

                    'final_state','accuracy', 'predictions', 'cost',

                    'optimizer', 'merged']

    Graph = namedtuple('Graph', export_nodes)

    local_dict = locals()

    graph = Graph(*[local_dict[each] for each in export_nodes])

    return graph

model = Sequential()

model.add(Embedding(top_words, embedding_vecor_length, input_length=max_review_length))

model.add(Dropout(0.2))

model.add(LSTM(100))

model.add(Dropout(0.2))

model.add(Dense(1, activation='sigmoid'))