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remove deprecated and update to python3 and tensorflow 1.3.0 #11

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remove deprecated and update to python3 and tensorflow 1.3.0 #11

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16 changes: 8 additions & 8 deletions key_value_memory/memn2n_kv.py
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Original file line number Diff line number Diff line change
Expand Up @@ -33,7 +33,7 @@ def add_gradient_noise(t, stddev=1e-3, name=None):

0.001 was said to be a good fixed value for memory networks [2].
"""
with tf.op_scope([t, stddev], name, "add_gradient_noise") as name:
with tf.name_scope(name, "add_gradient_noise", [t, stddev]) as name:
t = tf.convert_to_tensor(t, name="t")
gn = tf.random_normal(tf.shape(t), stddev=stddev)
return tf.add(t, gn, name=name)
Expand All @@ -44,11 +44,11 @@ def zero_nil_slot(t, name=None):
The nil_slot is a dummy slot and should not be trained and influence
the training algorithm.
"""
with tf.op_scope([t], name, "zero_nil_slot") as name:
with tf.name_scope(name, "zero_nil_slot", [t]) as name:
t = tf.convert_to_tensor(t, name="t")
s = tf.shape(t)[1]
z = tf.zeros(tf.pack([1, s]))
return tf.concat(0, [z, tf.slice(t, [1, 0], [-1, -1])], name=name)
return tf.concat([z, tf.slice(t, [1, 0], [-1, -1])], 0, name=name)

class MemN2N_KV(object):
"""Key Value Memory Network."""
Expand Down Expand Up @@ -120,10 +120,10 @@ def __init__(self, batch_size, vocab_size,
# Embedding layer
with tf.device('/cpu:0'), tf.name_scope("embedding"):
nil_word_slot = tf.zeros([1, embedding_size])
self.W = tf.concat(0, [nil_word_slot, tf.get_variable('W', shape=[vocab_size-1, embedding_size],
initializer=tf.contrib.layers.xavier_initializer())])
self.W_memory = tf.concat(0, [nil_word_slot, tf.get_variable('W_memory', shape=[vocab_size-1, embedding_size],
initializer=tf.contrib.layers.xavier_initializer())])
self.W = tf.concat([nil_word_slot, tf.get_variable('W', shape=[vocab_size-1, embedding_size],
initializer=tf.contrib.layers.xavier_initializer())], 0)
self.W_memory = tf.concat([nil_word_slot, tf.get_variable('W_memory', shape=[vocab_size-1, embedding_size],
initializer=tf.contrib.layers.xavier_initializer())], 0)
# self.W_memory = self.W
self._nil_vars = set([self.W.name, self.W_memory.name])
# shape: [batch_size, query_size, embedding_size]
Expand Down Expand Up @@ -186,7 +186,7 @@ def __init__(self, batch_size, vocab_size,
#logits = tf.nn.dropout(tf.matmul(o, self.B) + logits_bias, self.keep_prob)
probs = tf.nn.softmax(tf.cast(logits, tf.float32))

cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits, tf.cast(self._labels, tf.float32), name='cross_entropy')
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=tf.cast(self._labels, tf.float32), name='cross_entropy')
cross_entropy_sum = tf.reduce_sum(cross_entropy, name="cross_entropy_sum")

# loss op
Expand Down
11 changes: 6 additions & 5 deletions key_value_memory/single.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,10 +4,11 @@
from __future__ import print_function

from data_utils import load_task, vectorize_data
from sklearn import cross_validation, metrics
from sklearn import model_selection, metrics
from memn2n_kv import MemN2N_KV
from itertools import chain
from six.moves import range
from functools import reduce

import tensorflow as tf
import numpy as np
Expand Down Expand Up @@ -44,7 +45,7 @@
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(map(len, (s for s, _, _ in data))))
mean_story_size = int(np.mean(list(map(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)
Expand All @@ -57,7 +58,7 @@

# 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)
trainS, valS, trainQ, valQ, trainA, valA = model_selection.train_test_split(S, Q, A, test_size=.1)
testS, testQ, testA = vectorize_data(test, word_idx, sentence_size, memory_size)

print("Training set shape", trainS.shape)
Expand All @@ -76,7 +77,7 @@
val_labels = np.argmax(valA, axis=1)

batch_size = FLAGS.batch_size
batches = zip(range(0, n_train-batch_size, batch_size), range(batch_size, n_train, batch_size))
batches = list(zip(range(0, n_train-batch_size, batch_size), range(batch_size, n_train, batch_size)))

with tf.Graph().as_default():
session_conf = tf.ConfigProto(
Expand Down Expand Up @@ -110,7 +111,7 @@
nil_grads_and_vars.append((g, v))

train_op = optimizer.apply_gradients(nil_grads_and_vars, name="train_op", global_step=global_step)
sess.run(tf.initialize_all_variables())
sess.run(tf.global_variables_initializer())

def train_step(s, q, a):
feed_dict = {
Expand Down