Revision 71f0914d39c4dbf5bf56107955cfabfc5c29780b authored by donmesh on 13 January 2020, 19:06:47 UTC, committed by donmesh on 13 January 2020, 19:06:47 UTC
0 parent
BaseC.py
import numpy as np
import tensorflow as tf
import os
import datetime
from DataReader import Reader
class BaseC():
def __init__(self, FLAGS):
self.FLAGS = FLAGS
def formData(self, sentence_tf, aspect_tf):
with tf.name_scope('Data_formation'):
sentence_tf_exp = tf.expand_dims(sentence_tf, 2)
aspect_tf_exp = tf.expand_dims(aspect_tf, 1)
mask = tf.where(tf.equal(sentence_tf_exp, aspect_tf_exp))
idx_start_aspect = tf.cast(tf.segment_min(mask[:,1], mask[:,0]), dtype=tf.int64)
aspect_length = tf.reduce_sum(tf.cast(tf.not_equal(aspect_tf, tf.constant(0, dtype=tf.int64)), dtype=tf.int64), 1)
idx_end_aspect = idx_start_aspect + aspect_length
idx_end = tf.reduce_sum(tf.cast(tf.not_equal(sentence_tf, tf.constant(0, dtype=tf.int64)), dtype=tf.int64), 1)
indices = tf.cast(tf.reshape(tf.tile(tf.range(0, self.FLAGS.N), [tf.shape(sentence_tf)[0]]), [tf.shape(sentence_tf)[0], self.FLAGS.N]),tf.int64)
idx_lc = tf.tile(tf.expand_dims(idx_start_aspect, 1), [1, self.FLAGS.N])
idx_a = tf.tile(tf.expand_dims(idx_end_aspect, 1), [1, self.FLAGS.N])
idx_rc = tf.tile(tf.expand_dims(idx_end, 1), [1, self.FLAGS.N])
pad_length = self.FLAGS.N - tf.shape(sentence_tf)[1]
paddings = [tf.zeros([2], dtype=tf.int64), [tf.zeros([1], dtype=tf.int64)[0], pad_length]]
whole = tf.cast(tf.pad(sentence_tf, paddings, 'CONSTANT', constant_values=0), dtype=tf.int64)
LC = tf.where(tf.less(indices, idx_lc), whole, tf.zeros_like(whole, dtype=tf.int64))
A = tf.where(tf.logical_and(tf.greater_equal(indices, idx_lc), tf.less(indices, idx_a)), whole, tf.zeros_like(whole, dtype=tf.int64))
RC = tf.where(tf.logical_and(tf.greater_equal(indices, idx_a), tf.less(indices, idx_rc)), whole, tf.zeros_like(whole, dtype=tf.int64))
return LC, A, RC
def getMinibatches(self, batch_size, sentence_data, aspect_data, Y):
with tf.name_scope('Mini_batch'):
batch_size = tf.cond(batch_size > tf.cast(tf.shape(Y)[0], tf.int64), lambda: tf.cast(tf.shape(Y)[0], tf.int64), lambda: batch_size)
df = tf.data.Dataset.from_tensor_slices((sentence_data, aspect_data, Y)).repeat().batch(batch_size)
iterator = df.make_initializable_iterator()
next_batch = iterator.get_next()
nr_batches = tf.identity(tf.cast(tf.math.ceil(tf.shape(sentence_data)[0]/tf.cast(batch_size, tf.int32)), tf.int64), name='nr_batches')
LC, A, RC = self.formData(sentence_data, aspect_data)
return iterator, next_batch, nr_batches, LC, A, RC
def getAccuracy(self, pred, y, data_len, data_len_glove):
acc = tf.reduce_mean(tf.cast(tf.equal(tf.math.argmax(y,1), tf.math.argmax(pred,1)), tf.float64)) * (data_len_glove / data_len)
return tf.metrics.mean(acc)
def getLoss(self, logits, y):
return tf.nn.softmax_cross_entropy_with_logits_v2(logits=logits, labels=y, name='loss')
def getLossOp(self, loss):
return tf.metrics.mean(loss, name='loss_op')
def getSummaries(self, logits, prediction, Y, data_len, data_len_glove):
with tf.name_scope('Summaries'):
loss = self.getLoss(logits, Y)
loss_log, loss_log_update_op = self.getLossOp(loss)
loss_scalar = tf.summary.scalar('loss', loss_log)
accuracy, accuracy_update_op = self.getAccuracy(prediction, Y, data_len, data_len_glove)
accuracy_scalar = tf.summary.scalar('accuracy', accuracy)
return loss, loss_log, loss_log_update_op, loss_scalar, accuracy, accuracy_update_op, accuracy_scalar
def initWeights(self):
with tf.name_scope('weights'):
weights = {\
### Sentence Level Content Attention Module
'W_1' : tf.get_variable(initializer=tf.random_normal([1, self.d], mean=self.FLAGS.mean, stddev=self.FLAGS.stddev, seed=self.FLAGS.seed), dtype=tf.float32, name='W_1'),
'W_2' : tf.get_variable(initializer=tf.random_normal([self.d, self.d], mean=self.FLAGS.mean, stddev=self.FLAGS.stddev, seed=self.FLAGS.seed), dtype=tf.float32, name='W_2'),
'W_3' : tf.get_variable(initializer=tf.random_normal([self.d, self.d], mean=self.FLAGS.mean, stddev=self.FLAGS.stddev, seed=self.FLAGS.seed), dtype=tf.float32, name='W_3'),
'W_4' : tf.get_variable(initializer=tf.random_normal([self.d, self.d], mean=self.FLAGS.mean, stddev=self.FLAGS.stddev, seed=self.FLAGS.seed), dtype=tf.float32, name='W_4'),
'b_1' : tf.get_variable(initializer=tf.zeros([1, self.d]), dtype=tf.float32, name='b_1'),
### MLP
'W_5' : tf.get_variable(initializer=tf.random_normal([self.d, self.d], mean=self.FLAGS.mean, stddev=self.FLAGS.stddev, seed=self.FLAGS.seed), dtype=tf.float32, name='W_5'),
'b_2' : tf.get_variable(initializer=tf.zeros([1, self.d]), dtype=tf.float32, name='b_2'),
### Linear Layer
'W_6' : tf.get_variable(initializer=tf.random_normal([self.FLAGS.nr_cat, self.d], mean=self.FLAGS.mean, stddev=self.FLAGS.stddev, seed=self.FLAGS.seed), dtype=tf.float32, name='W_6'),
'b_3' : tf.get_variable(initializer=tf.zeros([1, self.FLAGS.nr_cat]), dtype=tf.float32, name='b_3')}
return weights
def getLambda(self, E, E_LC, E_A, E_RC):
mask_left = tf.not_equal(E_LC, tf.constant(0.0), name='mask_left')
mask_left_with_aspect = tf.not_equal(E_LC+E_A, tf.constant(0.0), name='mask_left_with_aspect')
mask_right = tf.not_equal(E_RC, tf.constant(0.0), name='mask_right')
mask_right_with_aspect = tf.not_equal(E_RC+E_A, tf.constant(0.0), name='mask_right_with_aspect')
mask_sentence = tf.not_equal(E, tf.constant(0.0), name='mask_sentence')
left_length = tf.reduce_sum(tf.cast(mask_left_with_aspect[:,:,0], tf.int32), 1, name='left_length') # (S)
right_length = tf.reduce_sum(tf.cast(mask_right_with_aspect[:,:,0], tf.int32), 1, name='right_length') # (S)
sentence_length = tf.reduce_sum(tf.cast(mask_sentence[:,:,0], tf.int32), 1, name='sentence_length') # (S)
left_tiled = tf.identity(tf.tile(tf.reshape(tf.range(self.FLAGS.N+1, 1, -1), [1, -1]), [tf.shape(E)[0], 1]) -
tf.reshape(right_length, [-1,1]), name='left_tiled') # (S x N)
left_reversed = tf.identity(left_tiled - (self.FLAGS.N+1 - tf.reshape(sentence_length, [-1,1])), name='left_reversed') # (S x N)
left_masked = tf.where(mask_left[:,:,0], tf.cast(left_reversed, tf.float32), tf.zeros_like(mask_left[:,:,0], dtype=tf.float32), name='left_masked') # (S x N)
right_tiled = tf.identity(tf.tile(tf.reshape(tf.range(1, self.FLAGS.N+1), [1,-1]), [tf.shape(E)[0],1]) -
tf.reshape(left_length, [-1,1]), name='right_tiled') # (S x N)
right_masked = tf.where(mask_right[:,:,0], tf.cast(right_tiled, tf.float32), tf.zeros_like(mask_right[:,:,0], dtype=tf.float32), name='right_masked') # (S x N)
sentence_locations = tf.identity((left_masked + right_masked) / tf.cast(tf.reshape(sentence_length, [-1,1]), tf.float32), name='sentence_location') # (S x N)
sentence_masked = tf.where(mask_sentence[:,:,0], 1-sentence_locations, tf.zeros_like(mask_sentence[:,:,0], dtype=tf.float32), name='sentence_masked') # (S x N)
Lambda = tf.identity(tf.tile(tf.expand_dims(sentence_masked, 2), [1,1,self.d]), name='Lambda') # (S x N x d)
return Lambda
def model(self, E, E_LC, E_A, E_RC, dropout_prob, weights):
with tf.name_scope('Position_Attention_Module'):
Lambda = self.getLambda(E, E_LC, E_A, E_RC)
with tf.name_scope('position_attention_weighted_memory'):
M_w = tf.identity(tf.multiply(Lambda, E), name='M_w')
with tf.name_scope('Content_Attention_Module'):
with tf.name_scope('scores_of_words'):
v_a = tf.identity(tf.reduce_sum(E_A, 1) / tf.math.count_nonzero(E_A, 1, dtype=tf.float32), name='v_a') # (S x d)
v_s = tf.identity(tf.reduce_sum(E, 1) / tf.math.count_nonzero(E, 1, dtype=tf.float32), name='v_s') # (S x d)
v_a_matrix = tf.tile(tf.expand_dims(v_a, 1), [1, self.FLAGS.N, 1], name='v_a_matrix') # (S x N x d)
v_s_matrix = tf.tile(tf.expand_dims(v_s, 1), [1, self.FLAGS.N, 1], name='v_s_matrix') # (S x N x d)
C = tf.where(tf.not_equal(E, tf.constant(0.0))[:,:,0],
tf.reshape(tf.tensordot(tf.nn.tanh(
tf.tensordot(tf.nn.dropout(M_w, rate=dropout_prob, seed=self.FLAGS.seed), weights['W_2'], [2,1]) +
tf.tensordot(tf.nn.dropout(v_a_matrix, rate=dropout_prob, seed=self.FLAGS.seed), weights['W_3'], [2,1]) +
tf.tensordot(tf.nn.dropout(v_s_matrix, rate=dropout_prob, seed=self.FLAGS.seed), weights['W_4'], [2,1]) +
tf.nn.dropout(weights['b_1'], rate=dropout_prob, seed=self.FLAGS.seed)),
weights['W_1'], [2, 1]),
[-1, self.FLAGS.N]),
-1e10*tf.ones([tf.shape(E)[0], self.FLAGS.N]), name='C') # (S x N)
with tf.name_scope('attention_weights_for_scores'):
A = tf.nn.softmax(C, name='A')
with tf.name_scope('sentence_representation'):
v_ts = tf.transpose(tf.matmul(tf.transpose(E, [0,2,1]), tf.expand_dims(A, 2)), [0,2,1], name='v_ts') # (S x d x 1)
v_ns = tf.identity(v_ts + tf.reshape(v_s, [-1, 1, self.d]), name='v_ns')
with tf.name_scope('MLP'):
v_ms = tf.nn.dropout(tf.nn.tanh(tf.tensordot(tf.nn.dropout(v_ns, rate=dropout_prob, seed=self.FLAGS.seed), weights['W_5'], [2,1]) +
weights['b_2']),
rate=dropout_prob, seed=self.FLAGS.seed, name='v_ms') # (S x d x 1)
with tf.name_scope('Linear_Layer'):
v_L = tf.reshape(tf.tensordot(v_ms, weights['W_6'], [2,1]) + weights['b_3'],
[-1, self.FLAGS.nr_cat], name='v_L') # (S x nr_cat)
return v_L
def trainModel(self, data, words_dict, train_data_length, train_data_length_glove, data_test=None, test_data_length=None, test_data_length_glove=None, save_to_files=False):
tf.reset_default_graph()
sentence_data = tf.placeholder(tf.int64, [None, None], name='sentence_data')
aspect_data = tf.placeholder(tf.int64, [None, None], name='aspect_data')
Y = tf.placeholder(tf.int64, [None, 3], name='Y')
batch_size = tf.placeholder(tf.int64, name='batch_size')
dropout_prob = tf.placeholder(tf.float32, name='dropout_prob')
dictionary = tf.placeholder(tf.float32, [self.V, self.d], name='dictionary')
data_len = tf.placeholder(tf.int32, None, name='data_len')
data_len_glove = tf.placeholder(tf.int32, None, name='data_len_glove')
iterator, next_batch, nr_batches, LC, A, RC = self.getMinibatches(batch_size, sentence_data, aspect_data, Y)
with tf.name_scope('Embeddings'):
E = tf.nn.embedding_lookup(dictionary, LC+A+RC, name='E')
E_LC = tf.nn.embedding_lookup(dictionary, LC, name='E_LC')
E_A = tf.nn.embedding_lookup(dictionary, A, name='E_A')
E_RC = tf.nn.embedding_lookup(dictionary, RC, name='E_RC')
weights = self.initWeights()
logits = tf.identity(self.model(E, E_LC, E_A, E_RC, dropout_prob, weights), name='logits')
prediction = tf.nn.softmax(logits, name='prediction')
loss, loss_log, loss_log_update_op, loss_scalar, accuracy, accuracy_update_op, accuracy_scalar = self.getSummaries(logits, prediction, Y, data_len, data_len_glove)
with tf.name_scope('TrainOp'):
global_step = tf.Variable(0, trainable=False, name='global_step')
optimizer = tf.train.GradientDescentOptimizer(self.FLAGS.learning_rate)
train_op = optimizer.minimize(loss, global_step=global_step)
with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as sess:
if save_to_files:
writer_train = tf.summary.FileWriter('./Results/logs/{0}/train/{1}'.format(self.__class__.__name__, datetime.datetime.now().strftime('%Y_%m_%d_%H_%M_%S')), sess.graph)
writer_test = tf.summary.FileWriter('./Results/logs/{0}/test/{1}'.format(self.__class__.__name__, datetime.datetime.now().strftime('%Y_%m_%d_%H_%M_%S')), sess.graph)
summary_op = tf.summary.merge_all()
saver = tf.train.Saver(max_to_keep = 3, save_relative_paths=True)
sess.run(tf.global_variables_initializer())
batches, _ = sess.run([nr_batches, iterator.initializer], feed_dict={'sentence_data:0':data[0],
'aspect_data:0':data[1],
'Y:0':data[2],
'batch_size:0':self.FLAGS.batch_size})
acc_test_best = 0
loss_test_best = np.inf
step_best = -1
for epoch in range(self.FLAGS.num_epochs):
sess.run(tf.local_variables_initializer())
for _ in range(batches):
sentence_batch, aspect_batch, Y_batch = sess.run(next_batch)
sess.run(train_op, feed_dict={'sentence_data:0':sentence_batch,
'aspect_data:0':aspect_batch,
'Y:0':Y_batch,
'dropout_prob:0':self.FLAGS.dropout_prob,
'dictionary:0':words_dict})
sess.run([accuracy_update_op, loss_log_update_op],
feed_dict={'sentence_data:0':sentence_batch,
'aspect_data:0':aspect_batch,
'Y:0':Y_batch,
'dropout_prob:0':self.FLAGS.dropout_prob,
'dictionary:0':words_dict,
'data_len:0':train_data_length,
'data_len_glove:0':train_data_length_glove})
acc, cost, step = sess.run([accuracy, loss_log, global_step])
if save_to_files:
writer_train.add_summary(sess.run(summary_op), step)
writer_train.flush()
if data_test is not None:
sess.run(tf.local_variables_initializer())
acc_test, cost_test = sess.run([accuracy_update_op, loss_log_update_op],
feed_dict={'sentence_data:0':data_test[0],
'aspect_data:0':data_test[1],
'Y:0':data_test[2],
'dropout_prob:0':0.0,
'dictionary:0':words_dict,
'data_len:0':test_data_length,
'data_len_glove:0':test_data_length_glove})
if acc_test > acc_test_best:
acc_test_best = acc_test
loss_test_best = cost_test
step_best = step
if save_to_files:
if not os.path.exists('./Results/ckpt/{}'.format(self.__class__.__name__)):
os.makedirs('./Results/ckpt/{}'.format(self.__class__.__name__))
saver.save(sess, './Results/ckpt/{}/model.ckpt'.format(self.__class__.__name__), step)
improved = '*'
else:
improved = ''
print('Step: {0:>6}, Train-Batch Accuracy: {1:>6.1%}, Test Accuracy: {2:>6.1%} Train-Batch Loss: {3} Test Loss: {4} {5}'.
format(step, acc, acc_test, np.round(cost,5), np.round(cost_test,5), improved))
if save_to_files:
writer_test.add_summary(sess.run(loss_scalar), step)
writer_test.add_summary(sess.run(accuracy_scalar), step)
writer_test.flush()
else:
print('Step: {0:>6}, Train-Batch Accuracy: {1:>6.1%}, Train Loss: {2}'.
format(step, acc, np.round(cost,5)))
if step_best + 1000 <= step:
print('Stopping in step: {}, the best result in step: {}'.format(step, step_best))
break
if save_to_files:
writer_train.close()
writer_test.close()
return acc_test_best, loss_test_best
def predict(self, data, words_dict, test_data_length, test_data_length_glove):
tf.reset_default_graph()
with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as sess:
ckpt = tf.train.latest_checkpoint('./Results/ckpt/{}/'.format(self.__class__.__name__))
saver = tf.train.import_meta_graph(ckpt+'.meta')
sess.run(tf.global_variables_initializer())
saver.restore(sess, ckpt)
graph = tf.get_default_graph()
global_step = graph.get_tensor_by_name('TrainOp/global_step:0')
data_len = graph.get_tensor_by_name('data_len:0')
data_len_glove = graph.get_tensor_by_name('data_len_glove:0')
Y = graph.get_tensor_by_name('Y:0')
logits = graph.get_tensor_by_name('logits:0')
prediction = graph.get_tensor_by_name('prediction:0')
_, accuracy = self.getAccuracy(prediction, Y, data_len, data_len_glove)
_, loss = self.getLossOp(self.getLoss(logits, Y))
sess.run(tf.local_variables_initializer())
pred, acc, cost, step = sess.run([prediction, accuracy, loss, global_step],
feed_dict={'sentence_data:0' : data[0],
'aspect_data:0' : data[1],
'Y:0' : data[2],
'dropout_prob:0':0.0,
'dictionary:0' : words_dict,
'data_len:0' : test_data_length,
'data_len_glove:0':test_data_length_glove})
print('Step: {0:>6}, Accuracy: {1:>6.1%}, Loss: {2}'.
format(step, acc, np.round(cost,5)))
return pred
##########################################
if __name__ == '__main__':
np.set_printoptions(threshold=np.inf, edgeitems=3, linewidth=120)
FLAGS = tf.app.flags.FLAGS
for name in list(FLAGS):
if name not in ('showprefixforinfo',):
delattr(FLAGS, name)
tf.app.flags.DEFINE_string('f', '', 'kernel')
tf.app.flags.DEFINE_boolean('logtostderr', True, 'tensorboard')
tf.app.flags.DEFINE_float('train_proportion', 1.0, 'Train proportion for train/validation split') #0.75
tf.app.flags.DEFINE_boolean('shuffle', True, 'Shuffle datasets')
tf.app.flags.DEFINE_integer('num_epochs', 600, 'Number of iterations')
tf.app.flags.DEFINE_integer('seed', None, 'Random seed')
tf.app.flags.DEFINE_float('mean', 0.0, 'Mean of normally initialized variables')
tf.app.flags.DEFINE_float('stddev', 0.05, 'Standard deviation of normally initialized variables')
tf.app.flags.DEFINE_float('dropout_prob', 0.3, 'Dropout probability')
tf.app.flags.DEFINE_float('learning_rate', 0.001, 'Adam optimzier learning rate')
tf.app.flags.DEFINE_integer('batch_size', 128, 'Batch size')
tf.app.flags.DEFINE_integer('nr_cat', 3, 'Number of classification categories')
tf.app.flags.DEFINE_string('embeddings_path', './Embeddings/glove.42B.300d.txt', 'Embedding path')
tf.app.flags.DEFINE_boolean('train_model', True, 'Run Ont on train data')
tf.app.flags.DEFINE_boolean('save_to_files', True, 'Whether to save checkpoints')
tf.app.flags.DEFINE_boolean('predict_values', True, 'Run Ont on test data')
tf.app.flags.DEFINE_string('train_data_path', './Data/ABSA-16_SB1_Restaurants_Train_Data.xml', 'Train data path')
tf.app.flags.DEFINE_string('test_data_path', './Data/ABSA-16_SB1_Restaurants_Test_Gold.xml', 'Test data path')
reader = Reader(FLAGS)
embeddings, word2idx, idx2word = reader.readEmbeddings(FLAGS.embeddings_path)
data_train, longest_sentence_train = reader.readData(FLAGS.train_data_path)
data_test, longest_sentence_test = reader.readData(FLAGS.test_data_path)
longest_sentence = max(longest_sentence_train, longest_sentence_test)
tf.app.flags.DEFINE_integer('N', longest_sentence, 'Length of the longest sentence')
data_idx_train = reader.transformSent2idx(data_train, word2idx)
data_idx_test = reader.transformSent2idx(data_test, word2idx)
model = BaseC(FLAGS)
model.V, model.d = embeddings.shape
model.longest_sentence = longest_sentence
## Train and evaluate models
if FLAGS.train_model:
print('Training...')
a = datetime.datetime.now()
model.trainModel(data_idx_train, embeddings, len(data_train), len(data_idx_train[0]), data_idx_test, len(data_test), len(data_idx_test[0]), save_to_files=FLAGS.save_to_files)
b = datetime.datetime.now()
print('Time:', b-a)
## Predict using trained models
if FLAGS.predict_values:
print('Prediction...')
pred = model.predict(data_idx_test, embeddings, len(data_test), len(data_idx_test[0]))
print('')
Computing file changes ...