def gaws(rv, window_size, word_embedding, hidden_size, restore_name, grad, k,
d):
rnn_sentiment = 'pos' if not d[0] else 'neg'
args = np.argsort(grad) #sorted from smallest to largest
args = np.flip(args, axis=0)
k = min(k, args.size) # new line
window_size = min(window_size, rv.length)
g = tf.Graph()
with g.as_default():
global_step_tensor = tf.Variable(0,
trainable=False,
name='global_step')
r = rnn.classifier(batch_size=10000,
learning_rate=0.0,
hidden_size=hidden_size,
max_time=window_size,
embeddings=word_embedding,
global_step=global_step_tensor)
with tf.Session() as sess:
tf.train.Saver().restore(sess, restore_name)
print('Running window attack...')
ii = [0] * k
jj = [0] * k
if rv.sentiment == 'pos':
pp = [float('inf')] * k
else:
pp = [-float('inf')] * k
for i in range(min(rv.length, k)):
d, p, _ = r.infer_window(sess, rv, args[i], window_size)
#rnn_sent = 'pos' if not d[rv.index_vector[0,0]] else 'neg'
#if rnn_sent != rv.sentiment:
# print('RNN sentiment: ',rnn_sent,'Review sentiment: ',rv.sentiment)
# print('Neural Net was wrong')
# return None,None,None
p = p[:, 0]
pmin = np.amin(p, axis=0)
pmax = np.amax(p, axis=0)
if rv.sentiment == 'pos':
ii[i] = np.argmin(p)
pp[i] = pmin
else:
ii[i] = np.argmax(p)
pp[i] = pmax
jj[i] = i
ii = np.array(ii)
jj = np.array(jj)
pp = np.array(pp)
return (ii, jj, pp)
def win_atk(rv, window_size, word_embedding, hidden_size, restore_name):
window_size = min(window_size, rv.length)
g = tf.Graph()
with g.as_default():
global_step_tensor = tf.Variable(0,
trainable=False,
name='global_step')
r = rnn.classifier(batch_size=10000,
learning_rate=0.0,
hidden_size=hidden_size,
max_time=window_size,
embeddings=word_embedding,
global_step=global_step_tensor)
with tf.Session() as sess:
tf.train.Saver().restore(sess, restore_name)
print('Running window attack...')
ii = [0] * rv.length
jj = [0] * rv.length
if rv.sentiment == 'pos':
pp = [float('inf')] * rv.length
else:
pp = [-float('inf')] * rv.length
for i in range(rv.length):
d, p, g = r.infer_window(sess, rv, i, window_size)
rnn_sent = 'pos' if not d[rv.index_vector[0, 0]] else 'neg'
if rnn_sent != rv.sentiment:
print('RNN sentiment: ', rnn_sent, 'Review sentiment: ',
rv.sentiment)
print('Neural Net was wrong')
return None, None, None
p = p[:, 0]
pmin = np.amin(p, axis=0)
pmax = np.amax(p, axis=0)
if rv.sentiment == 'pos':
ii[i] = np.argmin(p)
pp[i] = pmin
else:
ii[i] = np.argmax(p)
pp[i] = pmax
jj[i] = i
ii = np.array(ii)
jj = np.array(jj)
pp = np.array(pp)
return (ii, jj, pp)
# Load the word embedding
emb_dict = np.load('emb_dict.npy').item()
embeddings = np.load('final_embeddings.npy')
for hidden_size,lr in zip([2,4,8,16,32,64,128]*2,[0.01,0.001]*7):
# Reset the TensorFlow graph
g = tf.Graph()
tf.reset_default_graph()
with g.as_default():
# Set global step to zero, for keeping track of training progress
global_step_tensor = tf.Variable(0, trainable = False, name = 'global_step')
# Make the RNN
r = rnn.classifier(
batch_size = batch_size,
learning_rate = lr,
hidden_size = hidden_size,
max_time = max_time,
embeddings = embeddings,
global_step = global_step_tensor
)
# Training session
with tf.Session() as sess:
saver = tf.train.Saver(max_to_keep = 200)
train_writer = tf.summary.FileWriter(sys.argv[1]+'/train_'+str(hidden_size)+'_'+str(int(lr*1000)))
test_writer = tf.summary.FileWriter(sys.argv[1]+'/test_'+str(hidden_size)+'_'+str(int(lr*1000)))
sess.run(tf.global_variables_initializer())
for epoch in range(50):
saver.save(sess, './'+sys.argv[2]+'_'+str(hidden_size)+'_'+str(int(lr*1000))+"/imdb-rnn-e%d.ckpt"%epoch)
print('epoch: ',epoch)
# Testing #
sess.run(tf.local_variables_initializer())
# Following are conditions where one-word replacement worked.
if prob_positive < 0.5 and rv.sentiment == 'pos':
continue
elif prob_positive > 0.5 and rv.sentiment == 'neg':
continue
g = tf.Graph()
tf.reset_default_graph()
rv.translate(1024, word_to_embedding_index, embedding_index_to_word)
with g.as_default():
global_step_tensor = tf.Variable(0,
trainable=False,
name='global_step')
# Create RNN graph
r = rnn.classifier(batch_size=rv.length,
learning_rate=0.0,
hidden_size=16,
max_time=1024,
embeddings=word_embedding,
global_step=global_step_tensor)
with tf.Session() as sess:
tf.train.Saver().restore(
sess, './ckpts/gridckpt_16_10/imdb-rnn-e15.ckpt')
rv.translate(r.max_time, word_to_embedding_index,
embedding_index_to_word)
rv.vec(word_embedding)
decision, probability = \
r.infer_rep_dpg(sess,rv,rv.index_vector[0])
#grad = batch_grad[0][0,0:rv.length,:]
#W = word_embedding; G = grad
#D = W @ (G.T)
#c = np.sum(np.multiply(rv.vector_list,G),axis=1)
rv = rp.review('./aclImdb/test/posneg/' + test_file)
per = (rv.length) // divs
rv.translate(rv.length, word_to_embedding_index,
embedding_index_to_word)
rv.vec(word_embedding)
min_arr = np.array([float('inf')] * divs)
max_arr = np.array([float('-inf')] * divs)
g = tf.Graph()
with g.as_default():
global_step_tensor = tf.Variable(0,
trainable=False,
name='global_step')
# Create RNN graph
r = rnn.classifier(batch_size=top_k * divs,
learning_rate=0.0,
hidden_size=16,
max_time=per,
embeddings=word_embedding,
global_step=global_step_tensor)
with tf.Session() as sess:
tf.train.Saver().restore(\
sess, './ckpts/gridckpt_16_10/imdb-rnn-e15.ckpt')
print('Processing ' + test_file)
ii = [0] * divs
jj = [0] * divs
for ins_location in range(per):
print(ins_location)
_, p, _, im, rep_vec = r.infer_swap(
sess, rv, ins_location, divs, top_k)
p = p[:, 0]
p = np.reshape(p, (r.batch_size // divs, divs), 'F')
min_arr = np.minimum(np.amin(p, axis=0), min_arr)
