def validation_step(x_validation,
y_validation,
y_validation_bind,
writer=None):
"""Evaluates model on a validation set"""
batches_validation = data_helpers.batch_iter(
list(zip(x_validation, y_validation, y_validation_bind)),
FLAGS.batch_size, FLAGS.num_epochs)
eval_loss, eval_rec, eval_acc, eval_counter = 0.0, 0.0, 0.0, 0
for batch_validation in batches_validation:
x_batch_validation, y_batch_validation, y_batch_validation_bind = zip(
*batch_validation)
feed_dict = {
fasttext.input_x: x_batch_validation,
fasttext.input_y: y_batch_validation,
fasttext.dropout_keep_prob: 1.0,
fasttext.is_training: False
}
step, summaries, logits, cur_loss = sess.run([
fasttext.global_step, validation_summary_op,
fasttext.logits, fasttext.loss
], feed_dict)
if FLAGS.use_classbind_or_not == 'Y':
predicted_labels = data_helpers.get_label_using_logits_and_classbind(
logits,
y_batch_validation_bind,
top_number=FLAGS.top_num)
if FLAGS.use_classbind_or_not == 'N':
predicted_labels = data_helpers.get_label_using_logits(
logits, top_number=FLAGS.top_num)
cur_rec, cur_acc = 0.0, 0.0
for index, predicted_label in enumerate(predicted_labels):
rec_inc, acc_inc = data_helpers.cal_rec_and_acc(
predicted_label, y_batch_validation[index])
cur_rec, cur_acc = cur_rec + rec_inc, cur_acc + acc_inc
cur_rec = cur_rec / len(y_batch_validation)
cur_acc = cur_acc / len(y_batch_validation)
eval_loss, eval_rec, eval_acc, eval_counter = eval_loss + cur_loss, eval_rec + cur_rec, \
eval_acc + cur_acc, eval_counter + 1
logger.info("✔︎ validation batch {} finished.".format(
eval_counter))
if writer:
writer.add_summary(summaries, step)
eval_loss = float(eval_loss / eval_counter)
eval_rec = float(eval_rec / eval_counter)
eval_acc = float(eval_acc / eval_counter)
return eval_loss, eval_rec, eval_acc
def test_fasttext():
"""Test FASTTEXT model."""
# Load data
logger.info("✔ Loading data...")
logger.info('Recommand padding Sequence length is: {}'.format(
FLAGS.pad_seq_len))
logger.info('✔︎ Test data processing...')
test_data = data_helpers.load_data_and_labels(FLAGS.test_data_file,
FLAGS.num_classes,
FLAGS.embedding_dim)
logger.info('✔︎ Test data padding...')
x_test, y_test = data_helpers.pad_data(test_data, FLAGS.pad_seq_len)
y_test_bind = test_data.labels_bind
# Build vocabulary
VOCAB_SIZE = data_helpers.load_vocab_size(FLAGS.embedding_dim)
pretrained_word2vec_matrix = data_helpers.load_word2vec_matrix(
VOCAB_SIZE, FLAGS.embedding_dim)
# Load fasttext model
logger.info("✔ Loading model...")
checkpoint_file = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)
logger.info(checkpoint_file)
graph = tf.Graph()
with 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():
# Load the saved meta graph and restore variables
saver = tf.train.import_meta_graph(
"{}.meta".format(checkpoint_file))
saver.restore(sess, checkpoint_file)
# Get the placeholders from the graph by name
input_x = graph.get_operation_by_name("input_x").outputs[0]
# input_y = graph.get_operation_by_name("input_y").outputs[0]
dropout_keep_prob = graph.get_operation_by_name(
"dropout_keep_prob").outputs[0]
# pre-trained_word2vec
pretrained_embedding = graph.get_operation_by_name(
"embedding/embedding").outputs[0]
# Tensors we want to evaluate
logits = graph.get_operation_by_name("output/logits").outputs[0]
# Generate batches for one epoch
batches = data_helpers.batch_iter(list(
zip(x_test, y_test, y_test_bind)),
FLAGS.batch_size,
1,
shuffle=False)
# Collect the predictions here
all_predicitons = []
eval_loss, eval_rec, eval_acc, eval_counter = 0.0, 0.0, 0.0, 0
for batch_test in batches:
x_batch_test, y_batch_test, y_batch_test_bind = zip(
*batch_test)
feed_dict = {input_x: x_batch_test, dropout_keep_prob: 1.0}
batch_logits = sess.run(logits, feed_dict)
if FLAGS.use_classbind_or_not == 'Y':
predicted_labels = data_helpers.get_label_using_logits_and_classbind(
batch_logits,
y_batch_test_bind,
top_number=FLAGS.top_num)
if FLAGS.use_classbind_or_not == 'N':
predicted_labels = data_helpers.get_label_using_logits(
batch_logits, top_number=FLAGS.top_num)
all_predicitons = np.append(all_predicitons, predicted_labels)
cur_rec, cur_acc = 0.0, 0.0
for index, predicted_label in enumerate(predicted_labels):
rec_inc, acc_inc = data_helpers.cal_rec_and_acc(
predicted_label, y_batch_test[index])
cur_rec, cur_acc = cur_rec + rec_inc, cur_acc + acc_inc
cur_rec = cur_rec / len(y_batch_test)
cur_acc = cur_acc / len(y_batch_test)
eval_rec, eval_acc, eval_counter = eval_rec + cur_rec, eval_acc + cur_acc, eval_counter + 1
logger.info(
"✔︎ validation batch {} finished.".format(eval_counter))
eval_rec = float(eval_rec / eval_counter)
eval_acc = float(eval_acc / eval_counter)
logger.info("☛ Recall {:g}, Accuracy {:g}".format(
eval_rec, eval_acc))
np.savetxt(SAVE_FILE, list(zip(all_predicitons)), fmt='%s')
logger.info("✔ Done.")