config.strong_supervision = False
config.train_mode = False
print('Testing DMN ' + dmn_type + ' on babi task', config.babi_id)
# create model
with tf.variable_scope('DMN') as scope:
if dmn_type == "original":
from dmn_original import DMN
model = DMN(config)
elif dmn_type == "plus":
from dmn_plus import DMN_PLUS
model = DMN_PLUS(config)
print('==> initializing variables')
init = tf.global_variables_initializer()
saver = tf.train.Saver()
with tf.Session() as session:
session.run(init)
print('==> restoring weights')
saver.restore(session,
'weights/task' + str(model.config.babi_id) + '.weights')
print('==> running DMN')
test_loss, test_accuracy = model.run_epoch(session, model.test)
print('')
print('Test accuracy:', test_accuracy)
if args.restore:
print '==> restoring weights'
saver.restore(
session,
'weights/task' + str(model.config.babi_id) + '.weights')
print '==> starting training'
for epoch in xrange(config.max_epochs):
print 'Epoch {}'.format(epoch)
start = time.time()
train_loss, train_accuracy = model.run_epoch(
session,
model.train,
epoch,
train_writer,
train_op=model.train_step,
train=True)
valid_loss, valid_accuracy = model.run_epoch(session, model.valid)
print 'Training loss: {}'.format(train_loss)
print 'Validation loss: {}'.format(valid_loss)
print 'Training accuracy: {}'.format(train_accuracy)
print 'Vaildation accuracy: {}'.format(valid_accuracy)
if valid_loss < best_val_loss:
best_val_loss = valid_loss
best_val_epoch = epoch
if best_val_loss < best_overall_val_loss:
print 'Saving weights'
best_overall_val_loss = best_val_loss
config.strong_supervision = False
config.train_mode = False
print( 'Testing DMN ' + dmn_type + ' on babi task', config.babi_id)
# create model
with tf.variable_scope('DMN') as scope:
if dmn_type == "original":
from dmn_original import DMN
model = DMN(config)
elif dmn_type == "plus":
from dmn_plus import DMN_PLUS
model = DMN_PLUS(config)
print('==> initializing variables')
init = tf.global_variables_initializer()
saver = tf.train.Saver()
with tf.Session() as session:
session.run(init)
print('==> restoring weights')
saver.restore(session, 'weights/task' + str(model.config.babi_id) + '.weights')
print('==> running DMN')
test_loss, test_accuracy = model.run_epoch(session, model.test)
print('')
print('Test accuracy:', test_accuracy)
best_val_epoch = 0
prev_epoch_loss = float('inf')
best_val_loss = float('inf')
best_val_accuracy = 0.0
if args.restore:
print('==> restoring weights')
saver.restore(session, 'weights/task' + str(model.config.babi_id) + '.weights')
print('==> starting training')
for epoch in range(config.max_epochs):
print('Epoch {}'.format(epoch))
start = time.time()
train_loss, train_accuracy = model.run_epoch(
session, model.train, epoch, train_writer,
train_op=model.train_step, train=True)
valid_loss, valid_accuracy = model.run_epoch(session, model.valid)
print('Training loss: {}'.format(train_loss))
print('Validation loss: {}'.format(valid_loss))
print('Training accuracy: {}'.format(train_accuracy))
print('Vaildation accuracy: {}'.format(valid_accuracy))
if valid_loss < best_val_loss:
best_val_loss = valid_loss
best_val_epoch = epoch
if best_val_loss < best_overall_val_loss:
print('Saving weights')
best_overall_val_loss = best_val_loss
best_val_accuracy = valid_accuracy
saver.save(session, 'weights/task' + str(model.config.babi_id) + '.weights')
# asd
# create model
with tf.variable_scope('DMN') as scope:
if dmn_type == "original":
from dmn_original import DMN
model = DMN(config)
elif dmn_type == "plus":
from dmn_self_plus import DMN_PLUS
model = DMN_PLUS(config)
print('==> initializing variables')
init = tf.global_variables_initializer()
saver = tf.train.Saver()
with tf.Session() as session:
session.run(init)
print('==> restoring weights')
saver.restore(session, 'weights/task' + str(model.config.babi_id) + '.weights')
print('==> running DMN')
# test_loss, test_accuracy = model.run_epoch(session, model.test)
# print(model.test)
# qp, ip, ql, il, im, a = data
# questions, inputs, q_lens, input_lens, input_masks, answers
asd
answer = model.run_epoch(session, model.test)
print(answer)
# print('Test accuracy:', test_accuracy)