# sys.exit(0)
# for index, w in enumerate(vocab_processor.vocabulary_._mapping):
# print('vocab-{}:{}'.format(index, w))
# sys.exit(0)
with tf.Graph().as_default():
session_conf = tf.ConfigProto(allow_soft_placement=ALLOW_SOFT_PLACEMENT,
log_device_placement=LOG_DEVICE_PLACEMENT)
sess = tf.Session(config=session_conf)
with sess.as_default():
siameseModel = SiameseLSTMw2v(sequence_length=MAX_DOCUMENT_LENGTH,
vocab_size=len(
vocab_processor.vocabulary_),
embedding_size=EMBEDDING_DIM,
hidden_units=HIDDEN_UNITS,
l2_reg_lambda=L2_REG_LAMBDA,
batch_size=BATCH_SIZE,
trainableEmbeddings=False)
# Define Training procedure
global_step = tf.Variable(0, name="global_step", trainable=False)
optimizer = tf.train.AdamOptimizer(1e-3)
grads_and_vars = optimizer.compute_gradients(siameseModel.loss)
tr_op_set = optimizer.apply_gradients(grads_and_vars,
global_step=global_step)
print("defined training_ops")
# Keep track of gradient values and sparsity (optional)
grad_summaries = []
for g, v in grads_and_vars:
if g is not None:
with sess.as_default():
if FLAGS.is_char_based:
siameseModel = SiameseLSTM(
sequence_length=max_document_length,
vocab_size=len(vocab_processor.vocabulary_),
embedding_size=FLAGS.embedding_dim,
hidden_units=FLAGS.hidden_units,
l2_reg_lambda=FLAGS.l2_reg_lambda,
batch_size=FLAGS.batch_size
)
else:
siameseModel = SiameseLSTMw2v(
sequence_length=max_document_length,
vocab_size=len(vocab_processor.vocabulary_),
embedding_size=FLAGS.embedding_dim,
hidden_units=FLAGS.hidden_units,
l2_reg_lambda=FLAGS.l2_reg_lambda,
batch_size=FLAGS.batch_size,
trainableEmbeddings=trainableEmbeddings
)
# Define Training procedure
global_step = tf.Variable(0, name="global_step", trainable=False)
optimizer = tf.train.AdamOptimizer(1e-3)
print("initialized siameseModel object")
grads_and_vars=optimizer.compute_gradients(siameseModel.loss)
tr_op_set = optimizer.apply_gradients(grads_and_vars, global_step=global_step)
print("defined training_ops")
# Keep track of gradient values and sparsity (optional)
grad_summaries = []
for g, v in grads_and_vars:
def __launch_from_build(self, vocab_processor, trainableEmbeddings,
out_dir, checkpoint_dir_abs, initW):
# ==================================================
print("starting graph def")
graph = tf.Graph()
with graph.as_default():
# will use default_graph as input para, and current default_graph is the `graph`
sess = tf.Session(graph=graph, config=self.session_conf)
print("started session")
with sess.as_default():
if self.FLAGS.is_char_based:
siameseModel = SiameseLSTM(
sequence_length=self.FLAGS.max_document_length,
vocab_size=len(vocab_processor.vocabulary_),
embedding_size=self.FLAGS.embedding_dim,
hidden_units=self.FLAGS.hidden_units,
l2_reg_lambda=self.FLAGS.l2_reg_lambda,
batch_size=self.FLAGS.batch_size)
else:
siameseModel = SiameseLSTMw2v(
sequence_length=self.FLAGS.max_document_length,
vocab_size=len(vocab_processor.vocabulary_),
embedding_size=self.FLAGS.embedding_dim,
hidden_units=self.FLAGS.hidden_units,
l2_reg_lambda=self.FLAGS.l2_reg_lambda,
batch_size=self.FLAGS.batch_size,
trainableEmbeddings=trainableEmbeddings)
# Define Training procedure
global_step = tf.Variable(0, name="global_step", trainable=False)
optimizer = tf.train.AdamOptimizer(1e-3)
print("initialized siameseModel object")
grads_and_vars = optimizer.compute_gradients(siameseModel.loss)
tr_op_set = optimizer.apply_gradients(grads_and_vars,
global_step=global_step,
name='tr_op_set')
print("defined training_ops")
# Keep track of gradient values and sparsity (optional)
grad_summaries = []
for g, v in grads_and_vars:
if g is not None:
grad_hist_summary = tf.summary.histogram(
"{}/grad/hist".format(v.name), g)
sparsity_summary = tf.summary.scalar(
"{}/grad/sparsity".format(v.name),
tf.nn.zero_fraction(g))
grad_summaries.append(grad_hist_summary)
grad_summaries.append(sparsity_summary)
grad_summaries_merged = tf.summary.merge(grad_summaries)
print("defined gradient summaries")
# Summaries for loss and accuracy
loss_summary = tf.summary.scalar("loss", siameseModel.loss)
acc_summary = tf.summary.scalar("accuracy", siameseModel.accuracy)
# Train Summaries
train_summary_op = tf.summary.merge(
[loss_summary, acc_summary, grad_summaries_merged])
train_summary_op = tf.identity(train_summary_op,
'train_summary_op')
train_summary_dir = os.path.join(out_dir, "summaries", "train")
train_summary_writer = tf.summary.FileWriter(
train_summary_dir, sess.graph)
# Dev summaries
dev_summary_op = tf.summary.merge([loss_summary, acc_summary])
dev_summary_op = tf.identity(dev_summary_op, 'dev_summary_op')
dev_summary_dir = os.path.join(out_dir, "summaries", "dev")
dev_summary_writer = tf.summary.FileWriter(dev_summary_dir,
sess.graph)
saver = tf.train.Saver(tf.global_variables(), max_to_keep=100)
sess.run(tf.global_variables_initializer())
if initW is not None:
sess.run(siameseModel.W.assign(initW))
graphpb_txt = str(graph.as_graph_def())
with open(os.path.join(checkpoint_dir_abs, "graphpb.txt"),
'w') as f:
f.write(graphpb_txt)
input_tensors = InputTensors(siameseModel.input_x1,
siameseModel.input_x2,
siameseModel.input_y,
siameseModel.dropout_keep_prob)
result_tensors = ResultTensors(global_step, siameseModel.loss,
siameseModel.accuracy,
siameseModel.distance,
siameseModel.temp_sim)
metric_ops = MetricOps(tr_op_set, train_summary_op, dev_summary_op,
train_summary_writer, dev_summary_writer)
return saver, sess, input_tensors, result_tensors, metric_ops
