Add cosine proximity objective

docs/templates/objectives.md

@@ -27,3 +27,5 @@ For a few examples of such functions, check out the [objectives source](https://
 - __hinge__
 - __binary_crossentropy__: Also known as logloss. 
 - __categorical_crossentropy__: Also known as multiclass logloss. __Note__: using this objective requires that your labels are binary arrays of shape `(nb_samples, nb_classes)`.
+- __poisson__: mean of `(predictions - targets * log(predictions))`
+- __cosine_proximity__: the opposite (negative) of the mean cosine proximity between predictions and targets.

keras/objectives.py

@@ -44,15 +44,25 @@ def binary_crossentropy(y_true, y_pred):
     return K.mean(K.binary_crossentropy(y_pred, y_true), axis=-1)
 
 
-def poisson_loss(y_true, y_pred):
+def poisson(y_true, y_pred):
     return K.mean(y_pred - y_true * K.log(y_pred + K.epsilon()), axis=-1)
 
+
+def cosine_proximity(y_true, y_pred):
+    assert K.ndim(y_true) == 2
+    assert K.ndim(y_pred) == 2
+    y_true = K.l2_normalize(y_true, axis=1)
+    y_pred = K.l2_normalize(y_pred, axis=1)
+    return -K.mean(y_true * y_pred, axis=1)
+
+
 # aliases
 mse = MSE = mean_squared_error
 rmse = RMSE = root_mean_squared_error
 mae = MAE = mean_absolute_error
 mape = MAPE = mean_absolute_percentage_error
 msle = MSLE = mean_squared_logarithmic_error
+cosine = cosine_proximity
 
 from .utils.generic_utils import get_from_module
 def get(identifier):