def run_eval(load_model, load_sess, filename, sample_num_file, hparams, flag):
# load sample num
with open(sample_num_file, 'r') as f:
sample_num = int(f.readlines()[0].strip())
load_sess.run(load_model.iterator.initializer, feed_dict={load_model.filenames: [filename]})
preds = []
labels = []
while True:
try:
_, _, step_pred, step_labels = load_model.model.eval(load_sess)
preds.extend(np.reshape(step_pred, -1))
labels.extend(np.reshape(step_labels, -1))
except tf.errors.OutOfRangeError:
break
preds = preds[:sample_num]
labels = labels[:sample_num]
hparams.logger.info("data num:{0:d}".format(len(labels)))
res = metric.cal_metric(labels, preds, hparams, flag)
return res
def run_eval(load_model, load_sess, filename, sample_num_file, hparams, flag):
# load sample num
with open(sample_num_file, 'r') as f:
sample_num = int(f.readlines()[0].strip())
load_sess.run(load_model.iterator.initializer, feed_dict={load_model.filenames: [filename]})
preds = []
labels = []
while True:
try:
_, _, step_pred, step_labels = load_model.model.eval(load_sess)
preds.extend(np.reshape(step_pred, -1))
labels.extend(np.reshape(step_labels, -1))
except tf.errors.OutOfRangeError:
break
preds = preds[:sample_num]
labels = labels[:sample_num]
hparams.logger.info("data num:{0:d}".format(len(labels)))
res = metric.cal_metric(labels, preds, hparams, flag)
return res