RNN using LSTM

source: http://colah.github.io/posts/2015-08-Understanding-LSTMs

from keras.optimizers import SGD
from keras.preprocessing.text import one_hot, text_to_word_sequence
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.layers.embeddings import Embedding
from keras.layers.recurrent import LSTM, GRU
from keras.preprocessing import sequence

Reading blog post from data directory

import os
import pickle
import numpy as np
DATA_DIRECTORY = os.path.join(os.path.abspath(os.path.curdir), '..', 'data', 'word_embeddings')
print(DATA_DIRECTORY)
male_posts = []
female_post = []
with open(os.path.join(DATA_DIRECTORY,"male_blog_list.txt"),"rb") as male_file:
    male_posts= pickle.load(male_file)

with open(os.path.join(DATA_DIRECTORY,"female_blog_list.txt"),"rb") as female_file:
    female_posts = pickle.load(female_file)
filtered_male_posts = list(filter(lambda p: len(p) > 0, male_posts))
filtered_female_posts = list(filter(lambda p: len(p) > 0, female_posts))
# text processing - one hot builds index of the words
male_one_hot = []
female_one_hot = []
n = 30000
for post in filtered_male_posts:
    try:
        male_one_hot.append(one_hot(post, n, split=" ", lower=True))
    except:
        continue

for post in filtered_female_posts:
    try:
        female_one_hot.append(one_hot(post,n,split=" ", lower=True))
    except:
        continue
# 0 for male, 1 for female
concatenate_array_rnn = np.concatenate((np.zeros(len(male_one_hot)),
                                        np.ones(len(female_one_hot))))
from sklearn.model_selection import train_test_split

X_train_rnn, X_test_rnn, y_train_rnn, y_test_rnn = train_test_split(np.concatenate((female_one_hot,male_one_hot)),
                                                                    concatenate_array_rnn, 
                                                                    test_size=0.2)
maxlen = 100
X_train_rnn = sequence.pad_sequences(X_train_rnn, maxlen=maxlen)
X_test_rnn = sequence.pad_sequences(X_test_rnn, maxlen=maxlen)
print('X_train_rnn shape:', X_train_rnn.shape, y_train_rnn.shape)
print('X_test_rnn shape:', X_test_rnn.shape, y_test_rnn.shape)
max_features = 30000
dimension = 128
output_dimension = 128
model = Sequential()
model.add(Embedding(max_features, dimension))
model.add(LSTM(output_dimension))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss='mean_squared_error', optimizer='sgd', metrics=['accuracy'])
model.fit(X_train_rnn, y_train_rnn, batch_size=32,
          epochs=4, validation_data=(X_test_rnn, y_test_rnn))
score, acc = model.evaluate(X_test_rnn, y_test_rnn, batch_size=32)
print(score, acc)

Using TFIDF Vectorizer as an input instead of one hot encoder

from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer(decode_error='ignore', norm='l2', min_df=5)
tfidf_male = vectorizer.fit_transform(filtered_male_posts)
tfidf_female = vectorizer.fit_transform(filtered_female_posts)
flattened_array_tfidf_male = tfidf_male.toarray()
flattened_array_tfidf_female = tfidf_male.toarray()
y_rnn = np.concatenate((np.zeros(len(flattened_array_tfidf_male)),
                        np.ones(len(flattened_array_tfidf_female))))
X_train_rnn, X_test_rnn, y_train_rnn, y_test_rnn = train_test_split(np.concatenate((flattened_array_tfidf_male, 
                                                                                    flattened_array_tfidf_female)),
                                                                    y_rnn,test_size=0.2)
maxlen = 100
X_train_rnn = sequence.pad_sequences(X_train_rnn, maxlen=maxlen)
X_test_rnn = sequence.pad_sequences(X_test_rnn, maxlen=maxlen)
print('X_train_rnn shape:', X_train_rnn.shape, y_train_rnn.shape)
print('X_test_rnn shape:', X_test_rnn.shape, y_test_rnn.shape)
max_features = 30000
model = Sequential()
model.add(Embedding(max_features, dimension))
model.add(LSTM(output_dimension))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss='mean_squared_error',optimizer='sgd', metrics=['accuracy'])
model.fit(X_train_rnn, y_train_rnn, 
          batch_size=32, epochs=1,
          validation_data=(X_test_rnn, y_test_rnn))
score,acc = model.evaluate(X_test_rnn, y_test_rnn, 
                           batch_size=32)
print(score, acc)

Sentence Generation using LSTM

# reading all the male text data into one string
male_post = ' '.join(filtered_male_posts)

#building character set for the male posts
character_set_male = set(male_post)
#building two indices - character index and index of character
char_indices = dict((c, i) for i, c in enumerate(character_set_male))
indices_char = dict((i, c) for i, c in enumerate(character_set_male))


# cut the text in semi-redundant sequences of maxlen characters
maxlen = 20
step = 1
sentences = []
next_chars = []
for i in range(0, len(male_post) - maxlen, step):
    sentences.append(male_post[i : i + maxlen])
    next_chars.append(male_post[i + maxlen])
#Vectorisation of input
x_male = np.zeros((len(male_post), maxlen, len(character_set_male)), dtype=np.bool)
y_male = np.zeros((len(male_post), len(character_set_male)), dtype=np.bool)

print(x_male.shape, y_male.shape)

for i, sentence in enumerate(sentences):
    for t, char in enumerate(sentence):
        x_male[i, t, char_indices[char]] = 1
    y_male[i, char_indices[next_chars[i]]] = 1

print(x_male.shape, y_male.shape)

# build the model: a single LSTM
print('Build model...')
model = Sequential()
model.add(LSTM(128, input_shape=(maxlen, len(character_set_male))))
model.add(Dense(len(character_set_male)))
model.add(Activation('softmax'))

optimizer = RMSprop(lr=0.01)
model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])
auto_text_generating_male_model.compile(loss='mean_squared_error',optimizer='sgd')
import random, sys
# helper function to sample an index from a probability array
def sample(a, diversity=0.75):
    if random.random() > diversity:
        return np.argmax(a)
    while 1:
        i = random.randint(0, len(a)-1)
        if a[i] > random.random():
            return i
# train the model, output generated text after each iteration
for iteration in range(1,10):
    print()
    print('-' * 50)
    print('Iteration', iteration)
    model.fit(x_male, y_male, batch_size=128, epochs=1)

    start_index = random.randint(0, len(male_post) - maxlen - 1)

    for diversity in [0.2, 0.4, 0.6, 0.8]:
        print()
        print('----- diversity:', diversity)

        generated = ''
        sentence = male_post[start_index : start_index + maxlen]
        generated += sentence
        print('----- Generating with seed: "' + sentence + '"')

        for iteration in range(400):
            try:
                x = np.zeros((1, maxlen, len(character_set_male)))
                for t, char in enumerate(sentence):
                    x[0, t, char_indices[char]] = 1.

                preds = model.predict(x, verbose=0)[0]
                next_index = sample(preds, diversity)
                next_char = indices_char[next_index]

                generated += next_char
                sentence = sentence[1:] + next_char
            except:
                continue

        print(sentence)
        print()

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