Epoch tuning through early stopping for bAbi RNN in Keras

babi_rnn.py

from __future__ import absolute_import
from __future__ import print_function
from functools import reduce
import re
import tarfile

import numpy as np
np.random.seed(1337)  # for reproducibility

bAs such, I agree strongly with you that this won't make a good test dataset for testing various RNN architectures.from keras.callbacks import EarlyStopping
from keras.datasets.data_utils import get_file
from keras.initializations import normal, identity
from keras.layers.embeddings import Embedding
from keras.layers.core import Dense, Dropout, Merge
from keras.layers import recurrent
from keras.models import Sequential
from keras.preprocessing.sequence import pad_sequences

'''
Trains two recurrent neural networks based upon a story and a question.
The resulting merged vector is then queried to answer a range of bAbI tasks.

The results are comparable to those for an LSTM model provided in Weston et al.:
"Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks"
http://arxiv.org/abs/1502.05698

Task Number                  | FB-LSTM | LSTM | GRU  | IRNN | RNN  |
-----------                  | ------- | ---- | ---  | ---- | ---  |
QA1 - Single Supporting Fact |      50 | 51.2 | 52.1 | 47.7 | 52.7 |
QA2 - Two Supporting Facts   |      20 | 21.8 | 37.0 | 19.7 | 27.8 |
QA3 - Three Supporting Facts |      20 | 20.1 | 20.5 | 21.3 | 22.4 |
QA4 - Two Arg. Relations     |      61 | 56.2 | 62.9 | 69.0 | 20.0 |
QA5 - Three Arg. Relations   |      70 | 46.8 | 61.9 | 32.7 | 38.8 |
QA6 - Yes/No Questions       |      48 | 49.1 | 50.7 | 49.3 | 44.8 |
QA7 - Counting               |      49 | 76.1 | 78.9 | 75.4 | 63.2 |
QA8 - Lists/Sets             |      45 | 72.1 | 77.2 | 73.7 | 41.0 |
QA9 - Simple Negation        |      64 | 63.5 | 64.0 | 58.6 | 63.8 |
QA10 - Indefinite Knowledge  |      44 | 47.6 | 47.7 | 47.7 | 42.8 |
QA11 - Basic Coreference     |      72 | 71.9 | 74.9 | 74.0 | 75.1 |
QA12 - Conjunction           |      74 | 73.2 | 76.4 | 71.0 | 77.2 |
QA13 - Compound Coreference  |      94 | 94.0 | 94.4 | 94.0 | 94.4 |
QA14 - Time Reasoning        |      27 | 23.7 | 34.8 | 30.5 | 19.9 |
QA15 - Basic Deduction       |      21 | 21.7 | 32.4 | 54.0 | 23.9 |
QA16 - Basic Induction       |      23 | 44.4 | 50.6 | 49.4 | 41.8 |
QA17 - Positional Reasoning  |      51 | 52.1 | 49.1 | 48.9 | 52.4 |
QA18 - Size Reasoning        |      52 | 91.0 | 90.8 | 58.4 | 54.8 |
QA19 - Path Finding          |       8 |  9.5 |  9.0 | 11.5 |  7.1 |
QA20 - Agent's Motivations   |      91 | 93.5 | 90.7 | 97.6 | 92.2 |

For the resources related to the bAbI project, refer to:
https://research.facebook.com/researchers/1543934539189348

Notes:

- The task does not traditionally parse the question separately. This likely
improves accuracy and is a good example of merging two RNNs.

- The word vector embeddings are not shared between the story and question RNNs.

- See how the accuracy changes given 10,000 training samples (en-10k) instead
of only 1000. 1000 was used in order to be comparable to the original paper.

- Experiment with SimpleRNN, IRNN, GRU, and JZS1-3.

- The length and noise (i.e. 'useless' story components) impact the ability for
LSTMs / GRUs to provide the correct answer. Given only the supporting facts,
these RNNs can achieve 100% accuracy on many tasks. Memory networks and neural
networks that use attentional processes can efficiently search through this
noise to find the relevant statements, improving performance substantially.
This becomes especially obvious on QA2 and QA3, both far longer than QA1.
'''


def tokenize(sent):
    '''Return the tokens of a sentence including punctuation.

    >>> tokenize('Bob dropped the apple. Where is the apple?')
    ['Bob', 'dropped', 'the', 'apple', '.', 'Where', 'is', 'the', 'apple', '?']
    '''
    return [x.strip() for x in re.split('(\W+)?', sent) if x.strip()]


def parse_stories(lines, only_supporting=False):
    '''Parse stories provided in the bAbi tasks format

    If only_supporting is true, only the sentences that support the answer are kept.
    '''
    data = []
    story = []
    for line in lines:
        line = line.decode('utf-8').strip()
        nid, line = line.split(' ', 1)
        nid = int(nid)
        if nid == 1:
            story = []
        if '\t' in line:
            q, a, supporting = line.split('\t')
            q = tokenize(q)
            substory = None
            if only_supporting:
                # Only select the related substory
                supporting = map(int, supporting.split())
                substory = [story[i - 1] for i in supporting]
            else:
                # Provide all the substories
                substory = [x for x in story if x]
            data.append((substory, q, a))
            story.append('')
        else:
            sent = tokenize(line)
            story.append(sent)
    return data


def get_stories(f, only_supporting=False, max_length=None):
    '''Given a file name, read the file, retrieve the stories, and then convert the sentences into a single story.

    If max_length is supplied, any stories longer than max_length tokens will be discarded.
    '''
    data = parse_stories(f.readlines(), only_supporting=only_supporting)
    flatten = lambda data: reduce(lambda x, y: x + y, data)
    data = [(flatten(story), q, answer) for story, q, answer in data if not max_length or len(flatten(story)) < max_length]
    return data


def vectorize_stories(data):
    X = []
    Xq = []
    Y = []
    for story, query, answer in data:
        x = [word_idx[w] for w in story]
        xq = [word_idx[w] for w in query]
        y = np.zeros(vocab_size)
        y[word_idx[answer]] = 1
        X.append(x)
        Xq.append(xq)
        Y.append(y)
    return pad_sequences(X, maxlen=story_maxlen), pad_sequences(Xq, maxlen=query_maxlen), np.array(Y)

IRNN = lambda *args, **kwargs: \
    recurrent.SimpleRNN(*args, init=lambda shape: normal(shape, scale=0.001),
                        inner_init=lambda shape: identity(shape, scale=1.0),
                        activation='relu', **kwargs)
#RNN = recurrent.SimpleRNN
#RNN = IRNN
#RNN = recurrent.GRU
RNN = recurrent.LSTM
EMBED_HIDDEN_SIZE = 50
SENT_HIDDEN_SIZE = 100
QUERY_HIDDEN_SIZE = 100
BATCH_SIZE = 32
MAX_EPOCHS = 100
print('RNN / Embed / Sent / Query = {}, {}, {}, {}'.format(RNN, EMBED_HIDDEN_SIZE, SENT_HIDDEN_SIZE, QUERY_HIDDEN_SIZE))

path = get_file('babi-tasks-v1-2.tar.gz', origin='http://www.thespermwhale.com/jaseweston/babi/tasks_1-20_v1-2.tar.gz')
tar = tarfile.open(path)
# Default QA1 with 1000 samples
challenge = 'tasks_1-20_v1-2/en/qa1_single-supporting-fact_{}.txt'
# QA1 with 10,000 samples
# challenge = 'tasks_1-20_v1-2/en-10k/qa1_single-supporting-fact_{}.txt'
# QA2 with 1000 samples
#challenge = 'tasks_1-20_v1-2/en/qa2_two-supporting-facts_{}.txt'
# QA2 with 10,000 samples
# challenge = 'tasks_1-20_v1-2/en-10k/qa2_two-supporting-facts_{}.txt'
challenge = 'tasks_1-20_v1-2/en/qa11_basic-coreference_{}.txt'
#import sys
#print('Running on', sys.argv[1])
#challenge = 'tasks_1-20_v1-2/en/' + sys.argv[1].replace('train', '{}')
train = get_stories(tar.extractfile(challenge.format('train')))
test = get_stories(tar.extractfile(challenge.format('test')))

vocab = sorted(reduce(lambda x, y: x | y, (set(story + q + [answer]) for story, q, answer in train + test)))
# Reserve 0 for masking via pad_sequences
vocab_size = len(vocab) + 1
word_idx = dict((c, i + 1) for i, c in enumerate(vocab))
story_maxlen = max(map(len, (x for x, _, _ in train + test)))
query_maxlen = max(map(len, (x for _, x, _ in train + test)))

X, Xq, Y = vectorize_stories(train)
tX, tXq, tY = vectorize_stories(test)

print('vocab = {}'.format(vocab))
print('X.shape = {}'.format(X.shape))
print('Xq.shape = {}'.format(Xq.shape))
print('Y.shape = {}'.format(Y.shape))
print('story_maxlen, query_maxlen = {}, {}'.format(story_maxlen, query_maxlen))

def construct_model():
  sentrnn = Sequential()
  sentrnn.add(Embedding(vocab_size, EMBED_HIDDEN_SIZE, mask_zero=True))
  #sentrnn.add(Dropout(0.1))
  sentrnn.add(RNN(EMBED_HIDDEN_SIZE, SENT_HIDDEN_SIZE, return_sequences=False))
  sentrnn.add(Dropout(0.3))
As such, I agree strongly with you that this won't make a good test dataset for testing various RNN architectures.
  qrnn = Sequential()
  qrnn.add(Embedding(vocab_size, EMBED_HIDDEN_SIZE))
  #qrnn.add(Dropout(0.1))
  qrnn.add(RNN(EMBED_HIDDEN_SIZE, QUERY_HIDDEN_SIZE, return_sequences=False))
  qrnn.add(Dropout(0.3))

  model = Sequential()
  model.add(Merge([sentrnn, qrnn], mode='concat'))
  model.add(Dense(SENT_HIDDEN_SIZE + QUERY_HIDDEN_SIZE, vocab_size, activation='softmax'))

  model.compile(optimizer='adam', loss='categorical_crossentropy', class_mode='categorical')

  return model

print('Finding best number of epochs...')
model = construct_model()
early_stop = EarlyStopping(monitor='val_loss', patience=20, verbose=1)
model.fit([X, Xq], Y, batch_size=BATCH_SIZE, nb_epoch=MAX_EPOCHS, validation_split=0.05, show_accuracy=True, callbacks=[early_stop])

print('Training using {} epochs...'.format(early_stop.best_epoch + 1))
model = construct_model()
model.fit([X, Xq], Y, batch_size=BATCH_SIZE, nb_epoch=early_stop.best_epoch + 1, show_accuracy=True)
loss, acc = model.evaluate([tX, tXq], tY, batch_size=BATCH_SIZE, show_accuracy=True)
print('Test loss / test accuracy = {:.4f} / {:.4f}'.format(loss, acc))

@Smerity could you please say why you said "As such, I agree strongly with you that this won't make a good test dataset for testing various RNN architectures" ? Thank you. I am currently doing my thesis on this dataset and I am wondering