def train_ann():
"""Training ANN model."""
# Load sentences, labels, and training parameters
logger.info('✔︎ Loading data...')
logger.info('✔︎ Training data processing...')
train_data = dh.load_data_and_labels(FLAGS.training_data_file,
FLAGS.num_classes,
FLAGS.embedding_dim,
data_aug_flag=False)
logger.info('✔︎ Validation data processing...')
validation_data = dh.load_data_and_labels(FLAGS.validation_data_file,
FLAGS.num_classes,
FLAGS.embedding_dim,
data_aug_flag=False)
logger.info('Recommended padding Sequence length is: {0}'.format(
FLAGS.pad_seq_len))
logger.info('✔︎ Training data padding...')
x_train, y_train = dh.pad_data(train_data, FLAGS.pad_seq_len)
logger.info('✔︎ Validation data padding...')
x_validation, y_validation = dh.pad_data(validation_data,
FLAGS.pad_seq_len)
# Build vocabulary
VOCAB_SIZE = dh.load_vocab_size(FLAGS.embedding_dim)
pretrained_word2vec_matrix = dh.load_word2vec_matrix(
VOCAB_SIZE, FLAGS.embedding_dim)
# Build a graph and ann object
with tf.Graph().as_default():
session_conf = tf.ConfigProto(
allow_soft_placement=FLAGS.allow_soft_placement,
log_device_placement=FLAGS.log_device_placement)
session_conf.gpu_options.allow_growth = FLAGS.gpu_options_allow_growth
sess = tf.Session(config=session_conf)
with sess.as_default():
ann = TextANN(sequence_length=FLAGS.pad_seq_len,
num_classes=FLAGS.num_classes,
vocab_size=VOCAB_SIZE,
fc_hidden_size=FLAGS.fc_hidden_size,
embedding_size=FLAGS.embedding_dim,
embedding_type=FLAGS.embedding_type,
l2_reg_lambda=FLAGS.l2_reg_lambda,
pretrained_embedding=pretrained_word2vec_matrix)
# Define training procedure
with tf.control_dependencies(
tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
learning_rate = tf.train.exponential_decay(
learning_rate=FLAGS.learning_rate,
global_step=ann.global_step,
decay_steps=FLAGS.decay_steps,
decay_rate=FLAGS.decay_rate,
staircase=True)
optimizer = tf.train.AdamOptimizer(learning_rate)
grads, vars = zip(*optimizer.compute_gradients(ann.loss))
grads, _ = tf.clip_by_global_norm(grads,
clip_norm=FLAGS.norm_ratio)
train_op = optimizer.apply_gradients(
zip(grads, vars),
global_step=ann.global_step,
name="train_op")
# Keep track of gradient values and sparsity (optional)
grad_summaries = []
for g, v in zip(grads, vars):
if g is not None:
grad_hist_summary = tf.summary.histogram(
"{0}/grad/hist".format(v.name), g)
sparsity_summary = tf.summary.scalar(
"{0}/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)
# Output directory for models and summaries
if FLAGS.train_or_restore == 'R':
MODEL = input(
"☛ Please input the checkpoints model you want to restore, "
"it should be like(1490175368): "
) # The model you want to restore
while not (MODEL.isdigit() and len(MODEL) == 10):
MODEL = input(
'✘ The format of your input is illegal, please re-input: '
)
logger.info(
'✔︎ The format of your input is legal, now loading to next step...'
)
checkpoint_dir = 'runs/' + MODEL + '/checkpoints/'
out_dir = os.path.abspath(
os.path.join(os.path.curdir, "runs", MODEL))
logger.info("✔︎ Writing to {0}\n".format(out_dir))
else:
timestamp = str(int(time.time()))
out_dir = os.path.abspath(
os.path.join(os.path.curdir, "runs", timestamp))
logger.info("✔︎ Writing to {0}\n".format(out_dir))
# Summaries for loss
loss_summary = tf.summary.scalar("loss", ann.loss)
# Train summaries
train_summary_op = tf.summary.merge(
[loss_summary, grad_summaries_merged])
train_summary_dir = os.path.join(out_dir, "summaries", "train")
train_summary_writer = tf.summary.FileWriter(
train_summary_dir, sess.graph)
# Validation summaries
validation_summary_op = tf.summary.merge([loss_summary])
validation_summary_dir = os.path.join(out_dir, "summaries",
"validation")
validation_summary_writer = tf.summary.FileWriter(
validation_summary_dir, sess.graph)
saver = tf.train.Saver(tf.global_variables(),
max_to_keep=FLAGS.num_checkpoints)
if FLAGS.train_or_restore == 'R':
# Load ann model
logger.info("✔ Loading model...")
checkpoint_file = tf.train.latest_checkpoint(checkpoint_dir)
logger.info(checkpoint_file)
# Load the saved meta graph and restore variables
saver = tf.train.import_meta_graph(
"{0}.meta".format(checkpoint_file))
saver.restore(sess, checkpoint_file)
else:
checkpoint_dir = os.path.abspath(
os.path.join(out_dir, "checkpoints"))
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
# Embedding visualization config
config = projector.ProjectorConfig()
embedding_conf = config.embeddings.add()
embedding_conf.tensor_name = 'embedding'
embedding_conf.metadata_path = FLAGS.metadata_file
projector.visualize_embeddings(train_summary_writer, config)
projector.visualize_embeddings(validation_summary_writer,
config)
# Save the embedding visualization
saver.save(
sess, os.path.join(out_dir, 'embedding', 'embedding.ckpt'))
current_step = sess.run(ann.global_step)
def train_step(x_batch, y_batch):
"""A single training step"""
feed_dict = {
ann.input_x: x_batch,
ann.input_y: y_batch,
ann.dropout_keep_prob: FLAGS.dropout_keep_prob,
ann.is_training: True
}
_, step, summaries, loss = sess.run(
[train_op, ann.global_step, train_summary_op, ann.loss],
feed_dict)
logger.info("step {0}: loss {1:g}".format(step, loss))
train_summary_writer.add_summary(summaries, step)
def validation_step(x_validation, y_validation, writer=None):
"""Evaluates model on a validation set"""
batches_validation = dh.batch_iter(
list(zip(x_validation, y_validation)), FLAGS.batch_size, 1)
# Predict classes by threshold or topk ('ts': threshold; 'tk': topk)
eval_counter, eval_loss, eval_rec_ts, eval_pre_ts, eval_F_ts = 0, 0.0, 0.0, 0.0, 0.0
eval_rec_tk = [0.0] * FLAGS.top_num
eval_pre_tk = [0.0] * FLAGS.top_num
eval_F_tk = [0.0] * FLAGS.top_num
for batch_validation in batches_validation:
x_batch_validation, y_batch_validation = zip(
*batch_validation)
feed_dict = {
ann.input_x: x_batch_validation,
ann.input_y: y_batch_validation,
ann.dropout_keep_prob: 1.0,
ann.is_training: False
}
step, summaries, scores, cur_loss = sess.run([
ann.global_step, validation_summary_op, ann.scores,
ann.loss
], feed_dict)
# Predict by threshold
predicted_labels_threshold, predicted_values_threshold = \
dh.get_label_using_scores_by_threshold(scores=scores, threshold=FLAGS.threshold)
cur_rec_ts, cur_pre_ts, cur_F_ts = 0.0, 0.0, 0.0
for index, predicted_label_threshold in enumerate(
predicted_labels_threshold):
rec_inc_ts, pre_inc_ts = dh.cal_metric(
predicted_label_threshold,
y_batch_validation[index])
cur_rec_ts, cur_pre_ts = cur_rec_ts + rec_inc_ts, cur_pre_ts + pre_inc_ts
cur_rec_ts = cur_rec_ts / len(y_batch_validation)
cur_pre_ts = cur_pre_ts / len(y_batch_validation)
cur_F_ts = dh.cal_F(cur_rec_ts, cur_pre_ts)
eval_rec_ts, eval_pre_ts = eval_rec_ts + cur_rec_ts, eval_pre_ts + cur_pre_ts
# Predict by topK
topK_predicted_labels = []
for top_num in range(FLAGS.top_num):
predicted_labels_topk, predicted_values_topk = \
dh.get_label_using_scores_by_topk(scores=scores, top_num=top_num+1)
topK_predicted_labels.append(predicted_labels_topk)
cur_rec_tk = [0.0] * FLAGS.top_num
cur_pre_tk = [0.0] * FLAGS.top_num
cur_F_tk = [0.0] * FLAGS.top_num
for top_num, predicted_labels_topK in enumerate(
topK_predicted_labels):
for index, predicted_label_topK in enumerate(
predicted_labels_topK):
rec_inc_tk, pre_inc_tk = dh.cal_metric(
predicted_label_topK,
y_batch_validation[index])
cur_rec_tk[top_num], cur_pre_tk[top_num] = \
cur_rec_tk[top_num] + rec_inc_tk, cur_pre_tk[top_num] + pre_inc_tk
cur_rec_tk[top_num] = cur_rec_tk[top_num] / len(
y_batch_validation)
cur_pre_tk[top_num] = cur_pre_tk[top_num] / len(
y_batch_validation)
cur_F_tk[top_num] = dh.cal_F(cur_rec_tk[top_num],
cur_pre_tk[top_num])
eval_rec_tk[top_num], eval_pre_tk[top_num] = \
eval_rec_tk[top_num] + cur_rec_tk[top_num], eval_pre_tk[top_num] + cur_pre_tk[top_num]
eval_loss = eval_loss + cur_loss
eval_counter = eval_counter + 1
logger.info("✔︎ validation batch {0}: loss {1:g}".format(
eval_counter, cur_loss))
logger.info(
"︎☛ Predict by threshold: recall {0:g}, precision {1:g}, F {2:g}"
.format(cur_rec_ts, cur_pre_ts, cur_F_ts))
logger.info("︎☛ Predict by topK:")
for top_num in range(FLAGS.top_num):
logger.info(
"Top{0}: recall {1:g}, precision {2:g}, F {3:g}".
format(top_num + 1, cur_rec_tk[top_num],
cur_pre_tk[top_num], cur_F_tk[top_num]))
if writer:
writer.add_summary(summaries, step)
eval_loss = float(eval_loss / eval_counter)
eval_rec_ts = float(eval_rec_ts / eval_counter)
eval_pre_ts = float(eval_pre_ts / eval_counter)
eval_F_ts = dh.cal_F(eval_rec_ts, eval_pre_ts)
for top_num in range(FLAGS.top_num):
eval_rec_tk[top_num] = float(eval_rec_tk[top_num] /
eval_counter)
eval_pre_tk[top_num] = float(eval_pre_tk[top_num] /
eval_counter)
eval_F_tk[top_num] = dh.cal_F(eval_rec_tk[top_num],
eval_pre_tk[top_num])
return eval_loss, eval_rec_ts, eval_pre_ts, eval_F_ts, eval_rec_tk, eval_pre_tk, eval_F_tk
# Generate batches
batches_train = dh.batch_iter(list(zip(x_train, y_train)),
FLAGS.batch_size, FLAGS.num_epochs)
num_batches_per_epoch = int(
(len(x_train) - 1) / FLAGS.batch_size) + 1
# Training loop. For each batch...
for batch_train in batches_train:
x_batch_train, y_batch_train = zip(*batch_train)
train_step(x_batch_train, y_batch_train)
current_step = tf.train.global_step(sess, ann.global_step)
if current_step % FLAGS.evaluate_every == 0:
logger.info("\nEvaluation:")
eval_loss, eval_rec_ts, eval_pre_ts, eval_F_ts, eval_rec_tk, eval_pre_tk, eval_F_tk = \
validation_step(x_validation, y_validation, writer=validation_summary_writer)
logger.info(
"All Validation set: Loss {0:g}".format(eval_loss))
# Predict by threshold
logger.info(
"︎☛ Predict by threshold: Recall {0:g}, Precision {1:g}, F {2:g}"
.format(eval_rec_ts, eval_pre_ts, eval_F_ts))
# Predict by topK
logger.info("︎☛ Predict by topK:")
for top_num in range(FLAGS.top_num):
logger.info(
"Top{0}: Recall {1:g}, Precision {2:g}, F {3:g}".
format(top_num + 1, eval_rec_tk[top_num],
eval_pre_tk[top_num], eval_F_tk[top_num]))
if current_step % FLAGS.checkpoint_every == 0:
checkpoint_prefix = os.path.join(checkpoint_dir, "model")
path = saver.save(sess,
checkpoint_prefix,
global_step=current_step)
logger.info(
"✔︎ Saved model checkpoint to {0}\n".format(path))
if current_step % num_batches_per_epoch == 0:
current_epoch = current_step // num_batches_per_epoch
logger.info(
"✔︎ Epoch {0} has finished!".format(current_epoch))
logger.info("✔︎ Done.")
def train_ann():
"""Training ANN model."""
# Print parameters used for the model
dh.tab_printer(args, logger)
# Load sentences, labels, and training parameters
logger.info("Loading data...")
logger.info("Data processing...")
train_data = dh.load_data_and_labels(args.train_file, args.num_classes, args.word2vec_file, data_aug_flag=False)
val_data = dh.load_data_and_labels(args.validation_file, args.num_classes, args.word2vec_file, data_aug_flag=False)
logger.info("Data padding...")
x_train, y_train = dh.pad_data(train_data, args.pad_seq_len)
x_val, y_val = dh.pad_data(val_data, args.pad_seq_len)
# Build vocabulary
VOCAB_SIZE, EMBEDDING_SIZE, pretrained_word2vec_matrix = dh.load_word2vec_matrix(args.word2vec_file)
# Build a graph and ann object
with tf.Graph().as_default():
session_conf = tf.ConfigProto(
allow_soft_placement=args.allow_soft_placement,
log_device_placement=args.log_device_placement)
session_conf.gpu_options.allow_growth = args.gpu_options_allow_growth
sess = tf.Session(config=session_conf)
with sess.as_default():
ann = TextANN(
sequence_length=args.pad_seq_len,
vocab_size=VOCAB_SIZE,
embedding_type=args.embedding_type,
embedding_size=EMBEDDING_SIZE,
fc_hidden_size=args.fc_dim,
num_classes=args.num_classes,
l2_reg_lambda=args.l2_lambda,
pretrained_embedding=pretrained_word2vec_matrix)
# Define training procedure
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
learning_rate = tf.train.exponential_decay(learning_rate=args.learning_rate,
global_step=ann.global_step, decay_steps=args.decay_steps,
decay_rate=args.decay_rate, staircase=True)
optimizer = tf.train.AdamOptimizer(learning_rate)
grads, vars = zip(*optimizer.compute_gradients(ann.loss))
grads, _ = tf.clip_by_global_norm(grads, clip_norm=args.norm_ratio)
train_op = optimizer.apply_gradients(zip(grads, vars), global_step=ann.global_step, name="train_op")
# Keep track of gradient values and sparsity (optional)
grad_summaries = []
for g, v in zip(grads, vars):
if g is not None:
grad_hist_summary = tf.summary.histogram("{0}/grad/hist".format(v.name), g)
sparsity_summary = tf.summary.scalar("{0}/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)
# Output directory for models and summaries
out_dir = dh.get_out_dir(OPTION, logger)
checkpoint_dir = os.path.abspath(os.path.join(out_dir, "checkpoints"))
best_checkpoint_dir = os.path.abspath(os.path.join(out_dir, "bestcheckpoints"))
# Summaries for loss
loss_summary = tf.summary.scalar("loss", ann.loss)
# Train summaries
train_summary_op = tf.summary.merge([loss_summary, grad_summaries_merged])
train_summary_dir = os.path.join(out_dir, "summaries", "train")
train_summary_writer = tf.summary.FileWriter(train_summary_dir, sess.graph)
# Validation summaries
validation_summary_op = tf.summary.merge([loss_summary])
validation_summary_dir = os.path.join(out_dir, "summaries", "validation")
validation_summary_writer = tf.summary.FileWriter(validation_summary_dir, sess.graph)
saver = tf.train.Saver(tf.global_variables(), max_to_keep=args.num_checkpoints)
best_saver = cm.BestCheckpointSaver(save_dir=best_checkpoint_dir, num_to_keep=3, maximize=True)
if OPTION == 'R':
# Load ann model
logger.info("Loading model...")
checkpoint_file = tf.train.latest_checkpoint(checkpoint_dir)
logger.info(checkpoint_file)
# Load the saved meta graph and restore variables
saver = tf.train.import_meta_graph("{0}.meta".format(checkpoint_file))
saver.restore(sess, checkpoint_file)
if OPTION == 'T':
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
# Embedding visualization config
config = projector.ProjectorConfig()
embedding_conf = config.embeddings.add()
embedding_conf.tensor_name = "embedding"
embedding_conf.metadata_path = args.metadata_file
projector.visualize_embeddings(train_summary_writer, config)
projector.visualize_embeddings(validation_summary_writer, config)
# Save the embedding visualization
saver.save(sess, os.path.join(out_dir, "embedding", "embedding.ckpt"))
current_step = sess.run(ann.global_step)
def train_step(x_batch, y_batch):
"""A single training step"""
feed_dict = {
ann.input_x: x_batch,
ann.input_y: y_batch,
ann.dropout_keep_prob: args.dropout_rate,
ann.is_training: True
}
_, step, summaries, loss = sess.run(
[train_op, ann.global_step, train_summary_op, ann.loss], feed_dict)
logger.info("step {0}: loss {1:g}".format(step, loss))
train_summary_writer.add_summary(summaries, step)
def validation_step(x_val, y_val, writer=None):
"""Evaluates model on a validation set"""
batches_validation = dh.batch_iter(list(zip(x_val, y_val)), args.batch_size, 1)
# Predict classes by threshold or topk ('ts': threshold; 'tk': topk)
eval_counter, eval_loss = 0, 0.0
eval_pre_tk = [0.0] * args.topK
eval_rec_tk = [0.0] * args.topK
eval_F1_tk = [0.0] * args.topK
true_onehot_labels = []
predicted_onehot_scores = []
predicted_onehot_labels_ts = []
predicted_onehot_labels_tk = [[] for _ in range(args.topK)]
for batch_validation in batches_validation:
x_batch_val, y_batch_val = zip(*batch_validation)
feed_dict = {
ann.input_x: x_batch_val,
ann.input_y: y_batch_val,
ann.dropout_keep_prob: 1.0,
ann.is_training: False
}
step, summaries, scores, cur_loss = sess.run(
[ann.global_step, validation_summary_op, ann.scores, ann.loss], feed_dict)
# Prepare for calculating metrics
for i in y_batch_val:
true_onehot_labels.append(i)
for j in scores:
predicted_onehot_scores.append(j)
# Predict by threshold
batch_predicted_onehot_labels_ts = \
dh.get_onehot_label_threshold(scores=scores, threshold=args.threshold)
for k in batch_predicted_onehot_labels_ts:
predicted_onehot_labels_ts.append(k)
# Predict by topK
for top_num in range(args.topK):
batch_predicted_onehot_labels_tk = dh.get_onehot_label_topk(scores=scores, top_num=top_num+1)
for i in batch_predicted_onehot_labels_tk:
predicted_onehot_labels_tk[top_num].append(i)
eval_loss = eval_loss + cur_loss
eval_counter = eval_counter + 1
if writer:
writer.add_summary(summaries, step)
eval_loss = float(eval_loss / eval_counter)
# Calculate Precision & Recall & F1
eval_pre_ts = precision_score(y_true=np.array(true_onehot_labels),
y_pred=np.array(predicted_onehot_labels_ts), average='micro')
eval_rec_ts = recall_score(y_true=np.array(true_onehot_labels),
y_pred=np.array(predicted_onehot_labels_ts), average='micro')
eval_F1_ts = f1_score(y_true=np.array(true_onehot_labels),
y_pred=np.array(predicted_onehot_labels_ts), average='micro')
for top_num in range(args.topK):
eval_pre_tk[top_num] = precision_score(y_true=np.array(true_onehot_labels),
y_pred=np.array(predicted_onehot_labels_tk[top_num]),
average='micro')
eval_rec_tk[top_num] = recall_score(y_true=np.array(true_onehot_labels),
y_pred=np.array(predicted_onehot_labels_tk[top_num]),
average='micro')
eval_F1_tk[top_num] = f1_score(y_true=np.array(true_onehot_labels),
y_pred=np.array(predicted_onehot_labels_tk[top_num]),
average='micro')
# Calculate the average AUC
eval_auc = roc_auc_score(y_true=np.array(true_onehot_labels),
y_score=np.array(predicted_onehot_scores), average='micro')
# Calculate the average PR
eval_prc = average_precision_score(y_true=np.array(true_onehot_labels),
y_score=np.array(predicted_onehot_scores), average='micro')
return eval_loss, eval_auc, eval_prc, eval_pre_ts, eval_rec_ts, eval_F1_ts, \
eval_pre_tk, eval_rec_tk, eval_F1_tk
# Generate batches
batches_train = dh.batch_iter(
list(zip(x_train, y_train)), args.batch_size, args.epochs)
num_batches_per_epoch = int((len(x_train) - 1) / args.batch_size) + 1
# Training loop. For each batch...
for batch_train in batches_train:
x_batch_train, y_batch_train = zip(*batch_train)
train_step(x_batch_train, y_batch_train)
current_step = tf.train.global_step(sess, ann.global_step)
if current_step % args.evaluate_steps == 0:
logger.info("\nEvaluation:")
eval_loss, eval_auc, eval_prc, \
eval_pre_ts, eval_rec_ts, eval_F1_ts, eval_pre_tk, eval_rec_tk, eval_F1_tk = \
validation_step(x_val, y_val, writer=validation_summary_writer)
logger.info("All Validation set: Loss {0:g} | AUC {1:g} | AUPRC {2:g}"
.format(eval_loss, eval_auc, eval_prc))
# Predict by threshold
logger.info("Predict by threshold: Precision {0:g}, Recall {1:g}, F1 {2:g}"
.format(eval_pre_ts, eval_rec_ts, eval_F1_ts))
# Predict by topK
logger.info("Predict by topK:")
for top_num in range(args.topK):
logger.info("Top{0}: Precision {1:g}, Recall {2:g}, F1 {3:g}"
.format(top_num+1, eval_pre_tk[top_num], eval_rec_tk[top_num], eval_F1_tk[top_num]))
best_saver.handle(eval_prc, sess, current_step)
if current_step % args.checkpoint_steps == 0:
checkpoint_prefix = os.path.join(checkpoint_dir, "model")
path = saver.save(sess, checkpoint_prefix, global_step=current_step)
logger.info("Saved model checkpoint to {0}\n".format(path))
if current_step % num_batches_per_epoch == 0:
current_epoch = current_step // num_batches_per_epoch
logger.info("Epoch {0} has finished!".format(current_epoch))
logger.info("All Done.")
def train_ann():
"""Training ANN model."""
# Load sentences, labels, and training parameters
logger.info('✔︎ Loading data...')
logger.info('✔︎ Training data processing...')
train_data = dh.load_data_and_labels(FLAGS.training_data_file,
FLAGS.embedding_dim)
logger.info('✔︎ Validation data processing...')
validation_data = dh.load_data_and_labels(FLAGS.validation_data_file,
FLAGS.embedding_dim)
logger.info('Recommended padding Sequence length is: {0}'.format(
FLAGS.pad_seq_len))
logger.info('✔︎ Training data padding...')
x_train_front, x_train_behind, y_train = dh.pad_data(
train_data, FLAGS.pad_seq_len)
logger.info('✔︎ Validation data padding...')
x_validation_front, x_validation_behind, y_validation = dh.pad_data(
validation_data, FLAGS.pad_seq_len)
# Build vocabulary
VOCAB_SIZE = dh.load_vocab_size(FLAGS.embedding_dim)
pretrained_word2vec_matrix = dh.load_word2vec_matrix(
VOCAB_SIZE, FLAGS.embedding_dim)
# Build a graph and ann object
with tf.Graph().as_default():
session_conf = tf.ConfigProto(
allow_soft_placement=FLAGS.allow_soft_placement,
log_device_placement=FLAGS.log_device_placement)
session_conf.gpu_options.allow_growth = FLAGS.gpu_options_allow_growth
sess = tf.Session(config=session_conf)
with sess.as_default():
ann = TextANN(sequence_length=FLAGS.pad_seq_len,
num_classes=y_train.shape[1],
vocab_size=VOCAB_SIZE,
fc_hidden_size=FLAGS.fc_hidden_size,
embedding_size=FLAGS.embedding_dim,
embedding_type=FLAGS.embedding_type,
l2_reg_lambda=FLAGS.l2_reg_lambda,
pretrained_embedding=pretrained_word2vec_matrix)
# Define training procedure
with tf.control_dependencies(
tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
learning_rate = tf.train.exponential_decay(
learning_rate=FLAGS.learning_rate,
global_step=ann.global_step,
decay_steps=FLAGS.decay_steps,
decay_rate=FLAGS.decay_rate,
staircase=True)
optimizer = tf.train.AdamOptimizer(learning_rate)
grads, vars = zip(*optimizer.compute_gradients(ann.loss))
grads, _ = tf.clip_by_global_norm(grads,
clip_norm=FLAGS.norm_ratio)
train_op = optimizer.apply_gradients(
zip(grads, vars),
global_step=ann.global_step,
name="train_op")
# Keep track of gradient values and sparsity (optional)
grad_summaries = []
for g, v in zip(grads, vars):
if g is not None:
grad_hist_summary = tf.summary.histogram(
"{0}/grad/hist".format(v.name), g)
sparsity_summary = tf.summary.scalar(
"{0}/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)
# Output directory for models and summaries
if FLAGS.train_or_restore == 'R':
MODEL = input(
"☛ Please input the checkpoints model you want to restore, "
"it should be like(1490175368): "
) # The model you want to restore
while not (MODEL.isdigit() and len(MODEL) == 10):
MODEL = input(
'✘ The format of your input is illegal, please re-input: '
)
logger.info(
'✔︎ The format of your input is legal, now loading to next step...'
)
checkpoint_dir = 'runs/' + MODEL + '/checkpoints/'
out_dir = os.path.abspath(
os.path.join(os.path.curdir, "runs", MODEL))
logger.info("✔︎ Writing to {0}\n".format(out_dir))
else:
timestamp = str(int(time.time()))
out_dir = os.path.abspath(
os.path.join(os.path.curdir, "runs", timestamp))
logger.info("✔︎ Writing to {0}\n".format(out_dir))
# Summaries for loss and accuracy
loss_summary = tf.summary.scalar("loss", ann.loss)
acc_summary = tf.summary.scalar("accuracy", ann.accuracy)
# Train summaries
train_summary_op = tf.summary.merge(
[loss_summary, acc_summary, grad_summaries_merged])
train_summary_dir = os.path.join(out_dir, "summaries", "train")
train_summary_writer = tf.summary.FileWriter(
train_summary_dir, sess.graph)
# Validation summaries
validation_summary_op = tf.summary.merge(
[loss_summary, acc_summary])
validation_summary_dir = os.path.join(out_dir, "summaries",
"validation")
validation_summary_writer = tf.summary.FileWriter(
validation_summary_dir, sess.graph)
saver = tf.train.Saver(tf.global_variables(),
max_to_keep=FLAGS.num_checkpoints)
if FLAGS.train_or_restore == 'R':
# Load ann model
logger.info("✔ Loading model...")
checkpoint_file = tf.train.latest_checkpoint(checkpoint_dir)
logger.info(checkpoint_file)
# Load the saved meta graph and restore variables
saver = tf.train.import_meta_graph(
"{0}.meta".format(checkpoint_file))
saver.restore(sess, checkpoint_file)
else:
checkpoint_dir = os.path.abspath(
os.path.join(out_dir, "checkpoints"))
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
# Embedding visualization config
config = projector.ProjectorConfig()
embedding_conf = config.embeddings.add()
embedding_conf.tensor_name = 'embedding'
embedding_conf.metadata_path = FLAGS.metadata_file
projector.visualize_embeddings(train_summary_writer, config)
projector.visualize_embeddings(validation_summary_writer,
config)
# Save the embedding visualization
saver.save(
sess, os.path.join(out_dir, 'embedding', 'embedding.ckpt'))
current_step = sess.run(ann.global_step)
def train_step(x_batch_front, x_batch_behind, y_batch):
"""A single training step"""
feed_dict = {
ann.input_x_front: x_batch_front,
ann.input_x_behind: x_batch_behind,
ann.input_y: y_batch,
ann.dropout_keep_prob: FLAGS.dropout_keep_prob,
ann.is_training: True
}
_, step, summaries, loss, accuracy = sess.run([
train_op, ann.global_step, train_summary_op, ann.loss,
ann.accuracy
], feed_dict)
logger.info("step {0}: loss {1:g}, acc {2:g}".format(
step, loss, accuracy))
train_summary_writer.add_summary(summaries, step)
def validation_step(x_batch_front,
x_batch_behind,
y_batch,
writer=None):
"""Evaluates model on a validation set"""
feed_dict = {
ann.input_x_front: x_batch_front,
ann.input_x_behind: x_batch_behind,
ann.input_y: y_batch,
ann.dropout_keep_prob: 1.0,
ann.is_training: False
}
step, summaries, loss, accuracy, recall, precision, f1, auc = sess.run(
[
ann.global_step, validation_summary_op, ann.loss,
ann.accuracy, ann.recall, ann.precision, ann.F1,
ann.AUC
], feed_dict)
logger.info(
"step {0}: loss {1:g}, acc {2:g}, recall {3:g}, precision {4:g}, f1 {5:g}, AUC {6}"
.format(step, loss, accuracy, recall, precision, f1, auc))
if writer:
writer.add_summary(summaries, step)
# Generate batches
batches = dh.batch_iter(
list(zip(x_train_front, x_train_behind, y_train)),
FLAGS.batch_size, FLAGS.num_epochs)
num_batches_per_epoch = int(
(len(x_train_front) - 1) / FLAGS.batch_size) + 1
# Training loop. For each batch...
for batch in batches:
x_batch_front, x_batch_behind, y_batch = zip(*batch)
train_step(x_batch_front, x_batch_behind, y_batch)
current_step = tf.train.global_step(sess, ann.global_step)
if current_step % FLAGS.evaluate_every == 0:
logger.info("\nEvaluation:")
validation_step(x_validation_front,
x_validation_behind,
y_validation,
writer=validation_summary_writer)
if current_step % FLAGS.checkpoint_every == 0:
checkpoint_prefix = os.path.join(checkpoint_dir, "model")
path = saver.save(sess,
checkpoint_prefix,
global_step=current_step)
logger.info(
"✔︎ Saved model checkpoint to {0}\n".format(path))
if current_step % num_batches_per_epoch == 0:
current_epoch = current_step // num_batches_per_epoch
logger.info(
"✔︎ Epoch {0} has finished!".format(current_epoch))
logger.info("✔︎ Done.")