def train_model(model_type, attack_level, num_modified_words, percentage_attacked_samples):
print("train")
print("%s white-box adversarial attack modifies %d words of %d%% of the instances: " % (
attack_level, num_modified_words, percentage_attacked_samples))
global model_conf
if model_type == "lstm":
import movieqa.conf_lstm as model_conf
else:
import movieqa.conf_cnn as model_conf
global_step = tf.contrib.framework.get_or_create_global_step()
init = False
if not tf.io.gfile.exists(data_conf.TRAIN_DIR):
init = True
print("RESTORING WEIGHTS")
tf.io.gfile.makedirs(data_conf.TRAIN_DIR)
util.save_config_values(data_conf, data_conf.TRAIN_DIR + "/data")
util.save_config_values(model_conf, data_conf.TRAIN_DIR + "/model")
filenames = glob.glob(data_conf.TRAIN_RECORD_PATH + '/*')
print("Reading training dataset from %s" % filenames)
dataset = tf.contrib.data.TFRecordDataset(filenames)
dataset = dataset.map(get_single_sample)
dataset = dataset.shuffle(buffer_size=9000)
dataset = dataset.repeat(data_conf.NUM_EPOCHS)
batch_size = data_conf.BATCH_SIZE
dataset = dataset.padded_batch(data_conf.BATCH_SIZE, padded_shapes=(
[None], [ANSWER_COUNT, None], [None], (), [None, None], ()))
iterator = tf.compat.v1.data.make_one_shot_iterator(dataset)
next_q, next_a, next_l, next_plot_ids, next_plots, next_q_types = iterator.get_next()
_, w_atts, s_atts, _ = predict_batch(model_type, [next_q, next_a, next_plots], training=True)
if attack_level == "sentence":
m_p = tf.compat.v1.py_func(remove_plot_sentence, [next_plots, s_atts, next_l], [tf.int64])[0]
elif attack_level == "word":
m_p = tf.compat.v1.py_func(modify_plot_sentence, [next_plots, w_atts, s_atts, next_l], [tf.int64])[0]
logits, _, _, _ = predict_batch(model_type, [next_q, next_a, m_p], training=True)
probabs = model.compute_probabilities(logits=logits)
loss_batch = model.compute_batch_mean_loss(logits, next_l, model_conf.LOSS_FUNC)
accuracy = model.compute_accuracies(logits=logits, labels=next_l, dim=1)
accuracy_batch = tf.reduce_mean(input_tensor=accuracy)
tf.compat.v1.summary.scalar("train_accuracy", accuracy_batch)
tf.compat.v1.summary.scalar("train_loss", loss_batch)
training_op = update_op(loss_batch, global_step, model_conf.OPTIMIZER, model_conf.INITIAL_LEARNING_RATE)
config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
with tf.compat.v1.train.MonitoredTrainingSession(
checkpoint_dir=data_conf.TRAIN_DIR,
save_checkpoint_secs=60,
save_summaries_steps=5,
hooks=[tf.estimator.StopAtStepHook(last_step=model_conf.MAX_STEPS),
], config=config) as sess:
step = 0
total_acc = 0.0
if init:
_ = sess.run(set_embeddings_op, feed_dict={place: vectors})
while not sess.should_stop():
_, loss_val, acc_val, probs_val, lab_val, gs_val = sess.run(
[training_op, loss_batch, accuracy_batch, probabs, next_l, global_step])
print(probs_val)
print(lab_val)
print("Batch loss: " + str(loss_val))
print("Batch acc: " + str(acc_val))
step += 1
total_acc += acc_val
print("Total acc: " + str(total_acc / step))
print("Local_step: " + str(step * batch_size))
print("Global_step: " + str(gs_val))
print("===========================================")
util.copy_model(data_conf.TRAIN_DIR, gs_val)
def train_model():
print("train RNN-LSTM model")
global_step = tf.compat.v1.train.get_or_create_global_step()
if not tf.io.gfile.exists(data_conf.TRAIN_DIR):
print("RESTORING WEIGHTS")
tf.io.gfile.makedirs(data_conf.TRAIN_DIR)
util.save_config_values(data_conf, data_conf.TRAIN_DIR + "/data")
util.save_config_values(model_conf, data_conf.TRAIN_DIR + "/model")
filenames = glob.glob(data_conf.TRAIN_RECORD_PATH + '/*')
print("Reading training dataset from %s" % filenames)
dataset = tf.data.TFRecordDataset(filenames)
dataset = dataset.map(get_single_sample)
dataset = dataset.shuffle(buffer_size=9000)
dataset = dataset.repeat(model_conf.NUM_EPOCHS)
batch_size = model_conf.BATCH_SIZE
dataset = dataset.padded_batch(model_conf.BATCH_SIZE,
padded_shapes=([None], [ANSWER_COUNT, None],
[None], (), [None,
None], ()))
iterator = tf.compat.v1.data.make_one_shot_iterator(dataset)
next_q, next_a, next_l, next_plot_ids, next_plots, next_q_types = iterator.get_next(
)
logits, _, _, _ = predict_batch([next_q, next_a, next_plots],
training=True)
probabs = model.compute_probabilities(logits=logits)
loss_batch = model.compute_batch_mean_loss(logits, next_l,
model_conf.LOSS_FUNC)
accuracy = model.compute_accuracies(logits=logits, labels=next_l, dim=1)
accuracy_batch = tf.reduce_mean(input_tensor=accuracy)
tf.compat.v1.summary.scalar("train_accuracy", accuracy_batch)
tf.compat.v1.summary.scalar("train_loss", loss_batch)
training_op = update_op(loss_batch, global_step, model_conf.OPTIMIZER,
model_conf.INITIAL_LEARNING_RATE)
config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
config.graph_options.optimizer_options.global_jit_level = tf.compat.v1.OptimizerOptions.OFF
with tf.compat.v1.train.MonitoredTrainingSession(
checkpoint_dir=data_conf.TRAIN_DIR,
save_checkpoint_secs=60,
save_summaries_steps=5,
hooks=[
tf.estimator.StopAtStepHook(last_step=model_conf.MAX_STEPS),
],
config=config) as sess:
step = 0
total_acc = 0.0
# print("Feeding embeddings %s of size %s" % (str(vectors), len(vectors)))
sess.run(set_embeddings_op, feed_dict={place: vectors})
while not sess.should_stop():
_, loss_val, acc_val, probs_val, lab_val, gs_val = sess.run([
training_op, loss_batch, accuracy_batch, probabs, next_l,
global_step
])
print(probs_val)
print(lab_val)
print("Batch loss: " + str(loss_val))
print("Batch acc: " + str(acc_val))
step += 1
total_acc += acc_val
print("Total acc: " + str(total_acc / step))
print("Local_step: " + str(step * batch_size))
print("Global_step: " + str(gs_val))
print("===========================================")
util.copy_model(data_conf.TRAIN_DIR, gs_val)