diff --git a/docs/sources/objectives.md b/docs/sources/objectives.md
index 45b6f0b76..4b25fbfb7 100644
--- a/docs/sources/objectives.md
+++ b/docs/sources/objectives.md
@@ -18,6 +18,8 @@ For a few examples of such functions, check out the [objectives source](https://
 
 - __mean_squared_error__ / __mse__
 - __mean_absolute_error__ / __mae__
+- __mean_absolute_percentage_error__ / __mape__
+- __mean_squared_logarithmic_error__ / __msle__
 - __squared_hinge__
 - __hinge__
 - __binary_crossentropy__: Also known as logloss. 
diff --git a/keras/objectives.py b/keras/objectives.py
index 7da2359b7..ba8f1a1c2 100644
--- a/keras/objectives.py
+++ b/keras/objectives.py
@@ -12,6 +12,9 @@ def mean_squared_error(y_true, y_pred):
 def mean_absolute_error(y_true, y_pred):
     return T.abs_(y_pred - y_true).mean(axis=-1)
 
+def mean_absolute_percentage_error(y_true, y_pred):
+    return T.abs_((y_true - y_pred) / T.clip(T.abs_(y_true), epsilon, np.inf)).mean(axis=-1) * 100.
+
 def mean_squared_logarithmic_error(y_true, y_pred):
     return T.sqr(T.log(T.clip(y_pred, epsilon, np.inf) + 1.) - T.log(T.clip(y_true, epsilon, np.inf) + 1.)).mean(axis=-1)
 
@@ -38,6 +41,7 @@ def binary_crossentropy(y_true, y_pred):
 # aliases
 mse = MSE = mean_squared_error
 mae = MAE = mean_absolute_error
+mape = MAPE = mean_absolute_percentage_error
 msle = MSLE = mean_squared_logarithmic_error
 
 from .utils.generic_utils import get_from_module