--- a
+++ b/helper.py
@@ -0,0 +1,31 @@
+import numpy as np
+import random
+
+from sklearn.metrics import roc_auc_score, average_precision_score
+
+def f_get_minibatch_set(mb_size_, x_set_, y_, m_):
+    idx = range(np.shape(y_)[0])
+    idx = random.sample(idx, mb_size_)
+    
+    x_set_mb = {}
+    for m in range(len(x_set_)):
+        x_set_mb[m] = x_set_[m][idx].astype(float)    
+    y_mb   = y_[idx].astype(float)
+    m_mb  = m_[idx].astype(float)
+
+    return x_set_mb, y_mb, m_mb
+
+
+def evaluate(true_y_, pred_y_, y_type_):
+    if y_type_ == 'categorical':
+        acc = np.mean(np.argmax(true_y_, axis=1) == np.argmax(pred_y_, axis=1))
+        return acc
+    elif y_type_ == 'binary':
+        auroc = roc_auc_score(true_y_[:, 1], pred_y_[:,1])
+        auprc = average_precision_score(true_y_[:, 1], pred_y_[:,1])
+        return auroc, auprc
+    elif y_type_ == 'continuous':
+        mse   = np.mean((true_y_.reshape([-1]) - pred_y_.reshape([-1]))**2)
+        return mse
+    else:
+        ValueError(print("y_type should be {'categorical', 'binary', 'continuous'}"))
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