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single.py
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single.py
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"""Example running MemN2N on a single bAbI task.
Download tasks from facebook.ai/babi """
from __future__ import absolute_import
from __future__ import print_function
from data_utils import load_task, vectorize_data
from sklearn import cross_validation, metrics
from memn2n import MemN2N
from itertools import chain
from six.moves import range, reduce
import tensorflow as tf
import numpy as np
tf.flags.DEFINE_float("learning_rate", 0.01, "Learning rate for SGD.")
tf.flags.DEFINE_float("anneal_rate", 25, "Number of epochs between halving the learnign rate.")
tf.flags.DEFINE_float("anneal_stop_epoch", 100, "Epoch number to end annealed lr schedule.")
tf.flags.DEFINE_float("max_grad_norm", 40.0, "Clip gradients to this norm.")
tf.flags.DEFINE_integer("evaluation_interval", 10, "Evaluate and print results every x epochs")
tf.flags.DEFINE_integer("batch_size", 32, "Batch size for training.")
tf.flags.DEFINE_integer("hops", 3, "Number of hops in the Memory Network.")
tf.flags.DEFINE_integer("epochs", 100, "Number of epochs to train for.")
tf.flags.DEFINE_integer("embedding_size", 20, "Embedding size for embedding matrices.")
tf.flags.DEFINE_integer("memory_size", 50, "Maximum size of memory.")
tf.flags.DEFINE_integer("task_id", 1, "bAbI task id, 1 <= id <= 20")
tf.flags.DEFINE_integer("random_state", None, "Random state.")
tf.flags.DEFINE_string("data_dir", "data/tasks_1-20_v1-2/en/", "Directory containing bAbI tasks")
FLAGS = tf.flags.FLAGS
print("Started Task:", FLAGS.task_id)
# task data
train, test = load_task(FLAGS.data_dir, FLAGS.task_id)
data = train + test
vocab = sorted(reduce(lambda x, y: x | y, (set(list(chain.from_iterable(s)) + q + a) for s, q, a in data)))
word_idx = dict((c, i + 1) for i, c in enumerate(vocab))
max_story_size = max(map(len, (s for s, _, _ in data)))
mean_story_size = int(np.mean([ len(s) for s, _, _ in data ]))
sentence_size = max(map(len, chain.from_iterable(s for s, _, _ in data)))
query_size = max(map(len, (q for _, q, _ in data)))
memory_size = min(FLAGS.memory_size, max_story_size)
# Add time words/indexes
for i in range(memory_size):
word_idx['time{}'.format(i+1)] = 'time{}'.format(i+1)
vocab_size = len(word_idx) + 1 # +1 for nil word
sentence_size = max(query_size, sentence_size) # for the position
sentence_size += 1 # +1 for time words
print("Longest sentence length", sentence_size)
print("Longest story length", max_story_size)
print("Average story length", mean_story_size)
# train/validation/test sets
S, Q, A = vectorize_data(train, word_idx, sentence_size, memory_size)
trainS, valS, trainQ, valQ, trainA, valA = cross_validation.train_test_split(S, Q, A, test_size=.1, random_state=FLAGS.random_state)
testS, testQ, testA = vectorize_data(test, word_idx, sentence_size, memory_size)
print(testS[0])
print("Training set shape", trainS.shape)
# params
n_train = trainS.shape[0]
n_test = testS.shape[0]
n_val = valS.shape[0]
print("Training Size", n_train)
print("Validation Size", n_val)
print("Testing Size", n_test)
train_labels = np.argmax(trainA, axis=1)
test_labels = np.argmax(testA, axis=1)
val_labels = np.argmax(valA, axis=1)
tf.set_random_seed(FLAGS.random_state)
batch_size = FLAGS.batch_size
batches = zip(range(0, n_train-batch_size, batch_size), range(batch_size, n_train, batch_size))
batches = [(start, end) for start, end in batches]
with tf.Session() as sess:
model = MemN2N(batch_size, vocab_size, sentence_size, memory_size, FLAGS.embedding_size, session=sess,
hops=FLAGS.hops, max_grad_norm=FLAGS.max_grad_norm)
for t in range(1, FLAGS.epochs+1):
# Stepped learning rate
if t - 1 <= FLAGS.anneal_stop_epoch:
anneal = 2.0 ** ((t - 1) // FLAGS.anneal_rate)
else:
anneal = 2.0 ** (FLAGS.anneal_stop_epoch // FLAGS.anneal_rate)
lr = FLAGS.learning_rate / anneal
np.random.shuffle(batches)
total_cost = 0.0
for start, end in batches:
s = trainS[start:end]
q = trainQ[start:end]
a = trainA[start:end]
cost_t = model.batch_fit(s, q, a, lr)
total_cost += cost_t
if t % FLAGS.evaluation_interval == 0:
train_preds = []
for start in range(0, n_train, batch_size):
end = start + batch_size
s = trainS[start:end]
q = trainQ[start:end]
pred = model.predict(s, q)
train_preds += list(pred)
val_preds = model.predict(valS, valQ)
train_acc = metrics.accuracy_score(np.array(train_preds), train_labels)
val_acc = metrics.accuracy_score(val_preds, val_labels)
print('-----------------------')
print('Epoch', t)
print('Total Cost:', total_cost)
print('Training Accuracy:', train_acc)
print('Validation Accuracy:', val_acc)
print('-----------------------')
test_preds = model.predict(testS, testQ)
test_acc = metrics.accuracy_score(test_preds, test_labels)
print("Testing Accuracy:", test_acc)