--- a
+++ b/metrics.py
@@ -0,0 +1,32 @@
+# -*- coding: utf-8 -*-
+from __future__ import absolute_import, division, print_function
+
+import keras
+import numpy as np
+from sklearn.metrics import *
+
+
+class SKLearnMetrics(keras.callbacks.Callback):
+    """ SKLearnMetrics computes various classification metrics at the end of a batch.
+     Unforunately, doesn't work when used with generators...."""
+
+    def on_train_begin(self, logs={}):
+        self.confusion = []
+        self.precision = []
+        self.recall = []
+        self.f1s = []
+        self.kappa = []
+        self.auc = []
+
+    def on_epoch_end(self, epoch, logs={}):
+        score = np.asarray(self.model.predict(self.validation_data[0]))
+        predict = np.round(np.asarray(self.model.predict(self.validation_data[0])))
+        target = self.validation_data[1]
+
+        self.auc.append(roc_auc_score(target, score))
+        self.confusion.append(confusion_matrix(target, predict))
+        self.precision.append(precision_score(target, predict))
+        self.recall.append(recall_score(target, predict))
+        self.f1s.append(f1_score(target, predict))
+        self.kappa.append(cohen_kappa_score(target, predict))
+        return