Add support for stateful metrics.

keras/backend/cntk_backend.py

@@ -2039,8 +2039,8 @@ def batch_get_value(xs):
 def set_value(x, value):
     if (isinstance(x, C.variables.Parameter) or
        isinstance(x, C.variables.Constant)):
-        if isinstance(value, float):
-            value = np.full(x.shape, value)
+        if isinstance(value, (float, int)):
+            value = np.full(x.shape, value, dtype=floatx())
         x.value = value
     else:
         raise NotImplementedError

keras/callbacks.py

@@ -18,6 +18,7 @@
 from collections import Iterable
 from .utils.generic_utils import Progbar
 from . import backend as K
+from .engine.topology import Layer
 
 try:
     import requests
@@ -202,8 +203,20 @@ class BaseLogger(Callback):
     """Callback that accumulates epoch averages of metrics.
 
     This callback is automatically applied to every Keras model.
+
+    # Arguments
+        stateful_metrics: Iterable of string names of metrics that
+            should *not* be averaged over an epoch.
+            Metrics in this list will be logged as-is in `on_epoch_end`.
+            All others will be averaged in `on_epoch_end`.
     """
 
+    def __init__(self, stateful_metrics=None):
+        if stateful_metrics:
+            self.stateful_metrics = set(stateful_metrics)
+        else:
+            self.stateful_metrics = set()
+
     def on_epoch_begin(self, epoch, logs=None):
         self.seen = 0
         self.totals = {}
@@ -214,17 +227,23 @@ def on_batch_end(self, batch, logs=None):
         self.seen += batch_size
 
         for k, v in logs.items():
-            if k in self.totals:
-                self.totals[k] += v * batch_size
+            if k in self.stateful_metrics:
+                self.totals[k] = v
             else:
-                self.totals[k] = v * batch_size
+                if k in self.totals:
+                    self.totals[k] += v * batch_size
+                else:
+                    self.totals[k] = v * batch_size
 
     def on_epoch_end(self, epoch, logs=None):
         if logs is not None:
             for k in self.params['metrics']:
                 if k in self.totals:
                     # Make value available to next callbacks.
-                    logs[k] = self.totals[k] / self.seen
+                    if k in self.stateful_metrics:
+                        logs[k] = self.totals[k]
+                    else:
+                        logs[k] = self.totals[k] / self.seen
 
 
 class TerminateOnNaN(Callback):
@@ -250,19 +269,28 @@ class ProgbarLogger(Callback):
         count_mode: One of "steps" or "samples".
             Whether the progress bar should
             count samples seen or steps (batches) seen.
+        stateful_metrics: Iterable of string names of metrics that
+            should *not* be averaged over an epoch.
+            Metrics in this list will be logged as-is.
+            All others will be averaged over time (e.g. loss, etc).
 
     # Raises
         ValueError: In case of invalid `count_mode`.
     """
 
-    def __init__(self, count_mode='samples'):
+    def __init__(self, count_mode='samples',
+                 stateful_metrics=None):
         super(ProgbarLogger, self).__init__()
         if count_mode == 'samples':
             self.use_steps = False
         elif count_mode == 'steps':
             self.use_steps = True
         else:
             raise ValueError('Unknown `count_mode`: ' + str(count_mode))
+        if stateful_metrics:
+            self.stateful_metrics = set(stateful_metrics)
+        else:
+            self.stateful_metrics = set()
 
     def on_train_begin(self, logs=None):
         self.verbose = self.params['verbose']
@@ -277,7 +305,8 @@ def on_epoch_begin(self, epoch, logs=None):
                 target = self.params['samples']
             self.target = target
             self.progbar = Progbar(target=self.target,
-                                   verbose=self.verbose)
+                                   verbose=self.verbose,
+                                   stateful_metrics=self.stateful_metrics)
         self.seen = 0
 
     def on_batch_begin(self, batch, logs=None):