def predict_on_batch(self, sess, data_batch):
feed = self.create_feed_dict(data_batch)
decode_output = sess.run(util.tuple_to_list(*self.decode),
feed_dict=feed)
pred = get_answer_from_span(decode_output[1])
return pred
def predict_on_batch(self, sess, data_batch):
feed = self.create_feed_dict(data_batch, dropout=1)
answer_pointer_rep = sess.run(
fetches=util.tuple_to_list(*self.answer_pointer_rep),
feed_dict=feed)
pred = du.get_answer_from_span(answer_pointer_rep[1])
return pred
def debug(self, sess, data_batch):
feed = self.create_feed_dict(data_batch)
debug_output = sess.run(util.tuple_to_list(*self.train_op),
feed_dict=feed)
logger.debug("Gradient {}".format(debug_output[1]))
logger.debug("Loss {}".format(debug_output[2]))
def predict_on_batch(self, sess, data_batch):
feed = self.create_feed_dict(data_batch)
answer_pointer_rep = sess.run(
fetches = util.tuple_to_list(*self.answer_pointer_rep),
feed_dict=feed
)
pred = answer_pointer_rep[1]
return pred
def debug(self, sess, data_batch):
feed = self.create_feed_dict(data_batch, dropout=1)
final_rep = self.train_op
output = sess.run(fetches=util.tuple_to_list(*final_rep),
feed_dict=feed)
# logger.info(output)
logger.info("grad_norms: {}".format(output[1]))
logger.info("loss: {}".format(output[2]))
logger.info("pred: {}".format(output[3]))
def train_on_batch(self, sess, data_batch):
feed = self.create_feed_dict(data_batch)
train_op_output = sess.run(util.tuple_to_list(*self.train_op),
feed_dict=feed)
grad_norm = train_op_output[1]
loss = train_op_output[2]
retain = train_op_output[3]
return grad_norm, loss, retain
def train_on_batch(self, sess, data_batch):
feed = self.create_feed_dict(data_batch, dropout=1)
train_op = sess.run(fetches=util.tuple_to_list(*self.train_op),
feed_dict=feed)
grad_norm = train_op[1]
loss = train_op[2]
return grad_norm, loss, 0.
def debug_shape(self, sess, data_batch):
feed = self.create_feed_dict(data_batch)
train_op_output = sess.run(
fetches = util.tuple_to_list(*self.train_op),
feed_dict=feed
)
for i, tensor in enumerate(self.train_op):
if tensor.name.startswith("debug_"):
logger.debug("Shape of {} == {}".format(tensor.name[6:], train_op_output[i]))
def train_on_batch(self, sess, data_batch):
feed = self.create_feed_dict(data_batch)
train_op = sess.run(
fetches = util.tuple_to_list(*self.train_op),
feed_dict=feed
)
loss = train_op[1]
pred = train_op[2]
return loss, pred
def summary_success(self, sess, data_batch):
feed = self.create_feed_dict(data_batch)
debug_output = sess.run(util.tuple_to_list(*self.train_op),
feed_dict=feed)
return debug_output[3]