[tune] tune.track -> tune.report (#8388)

python/ray/tune/integration/keras.py

@@ -1,27 +1,26 @@
 from tensorflow import keras
-from ray.tune import track
 
 
 class TuneReporterCallback(keras.callbacks.Callback):
     """Tune Callback for Keras."""
 
-    def __init__(self, reporter=None, freq="batch", logs={}):
+    def __init__(self, reporter=None, freq="batch", logs=None):
         """Initializer.
 
         Args:
-            reporter (StatusReporter|tune.track.log|None): Tune object for
-                returning results.
             freq (str): Sets the frequency of reporting intermediate results.
                 One of ["batch", "epoch"].
         """
-        self.reporter = reporter or track.log
         self.iteration = 0
+        logs = logs or {}
         if freq not in ["batch", "epoch"]:
             raise ValueError("{} not supported as a frequency.".format(freq))
         self.freq = freq
         super(TuneReporterCallback, self).__init__()
 
-    def on_batch_end(self, batch, logs={}):
+    def on_batch_end(self, batch, logs=None):
+        from ray import tune
+        logs = logs or {}
         if not self.freq == "batch":
             return
         self.iteration += 1
@@ -29,11 +28,13 @@ class TuneReporterCallback(keras.callbacks.Callback):
             if "loss" in metric and "neg_" not in metric:
                 logs["neg_" + metric] = -logs[metric]
         if "acc" in logs:
-            self.reporter(keras_info=logs, mean_accuracy=logs["acc"])
+            tune.report(keras_info=logs, mean_accuracy=logs["acc"])
         else:
-            self.reporter(keras_info=logs, mean_accuracy=logs.get("accuracy"))
+            tune.report(keras_info=logs, mean_accuracy=logs.get("accuracy"))
 
-    def on_epoch_end(self, batch, logs={}):
+    def on_epoch_end(self, batch, logs=None):
+        from ray import tune
+        logs = logs or {}
         if not self.freq == "epoch":
             return
         self.iteration += 1
@@ -41,6 +42,6 @@ class TuneReporterCallback(keras.callbacks.Callback):
             if "loss" in metric and "neg_" not in metric:
                 logs["neg_" + metric] = -logs[metric]
         if "acc" in logs:
-            self.reporter(keras_info=logs, mean_accuracy=logs["acc"])
+            tune.report(keras_info=logs, mean_accuracy=logs["acc"])
         else:
-            self.reporter(keras_info=logs, mean_accuracy=logs.get("accuracy"))
+            tune.report(keras_info=logs, mean_accuracy=logs.get("accuracy"))