1 files changed, 21 insertions, 55 deletions

diff --git a/tensorflow/python/keras/engine/training_generator.py b/tensorflow/python/keras/engine/training_generator.py
index 432cf2bddd..413c1f4fba 100644
--- a/tensorflow/python/keras/engine/training_generator.py
+++ b/tensorflow/python/keras/engine/training_generator.py
@@ -21,7 +21,6 @@ from __future__ import print_function
 
 import numpy as np
 
-from tensorflow.python.keras import backend as K
 from tensorflow.python.keras import callbacks as cbks
 from tensorflow.python.keras.utils.data_utils import GeneratorEnqueuer
 from tensorflow.python.keras.utils.data_utils import OrderedEnqueuer
@@ -79,66 +78,37 @@ def fit_generator(model,
                      ' class. Please specify `validation_steps` or use'
                      ' the `keras.utils.Sequence` class.')
 
-  # Prepare display labels.
-  out_labels = model.metrics_names
-  callback_metrics = out_labels + ['val_%s' % n for n in out_labels]
-
-  # prepare callbacks
-  model.history = cbks.History()
-  callbacks = [cbks.BaseLogger()] + (callbacks or []) + [model.history]
-  if verbose:
-    callbacks += [cbks.ProgbarLogger(count_mode='steps')]
-  callbacks = cbks.CallbackList(callbacks)
-
-  # it's possible to callback a different model than self:
-  if hasattr(model, 'callback_model') and model.callback_model:
-    callback_model = model.callback_model
-  else:
-    callback_model = model
-  callbacks.set_model(callback_model)
-
-  callback_params = {
-      'epochs': epochs,
-      'steps': steps_per_epoch,
-      'verbose': verbose,
-      'do_validation': do_validation,
-      'metrics': callback_metrics,
-  }
-  if do_validation:
-    # need to create the test_function before start of the first epoch
-    # because TensorBoard callback on_epoch_begin adds summary to the
-    # list of fetches of the test_function
-    model._make_test_function()
-    # determine the number of validation batches given a generator
-    if validation_steps:
-      callback_params.update({'validation_steps': validation_steps})
-    elif isinstance(validation_data, Sequence):
-      callback_params.update({'validation_steps': len(validation_data)})
-  callbacks.set_params(callback_params)
-
   enqueuer = None
   val_enqueuer = None
 
   try:
+    val_x, val_y, val_sample_weights = validation_data, None, None
     if do_validation and not val_gen:
       # Prepare data for validation
       if len(validation_data) == 2:
         val_x, val_y = validation_data  # pylint: disable=unpacking-non-sequence
-        val_sample_weight = None
+        val_sample_weights = None
       elif len(validation_data) == 3:
-        val_x, val_y, val_sample_weight = validation_data  # pylint: disable=unpacking-non-sequence
+        val_x, val_y, val_sample_weights = validation_data  # pylint: disable=unpacking-non-sequence
       else:
         raise ValueError(
             '`validation_data` should be a tuple '
             '`(val_x, val_y, val_sample_weight)` '
             'or `(val_x, val_y)`. Found: ' + str(validation_data))
       val_x, val_y, val_sample_weights = model._standardize_user_data(
-          val_x, val_y, val_sample_weight)
-      val_data = val_x + val_y + val_sample_weights
-      if model.uses_learning_phase and not isinstance(K.learning_phase(), int):
-        val_data += [0.]
-      for cbk in callbacks:
-        cbk.validation_data = val_data
+          val_x, val_y, val_sample_weights)
+
+    callbacks = cbks.configure_callbacks(
+        callbacks,
+        model,
+        do_validation=do_validation,
+        val_inputs=val_x,
+        val_targets=val_y,
+        val_sample_weights=val_sample_weights,
+        epochs=epochs,
+        validation_steps=validation_steps,
+        steps_per_epoch=steps_per_epoch,
+        verbose=verbose)
 
     if workers > 0:
       if is_sequence:
@@ -159,9 +129,6 @@ def fit_generator(model,
       else:
         output_generator = generator
 
-    callback_model.stop_training = False
-    # validation_data must be set before on_train_begin() is called
-    # so that TensorboardCallback can validate its input
     callbacks.on_train_begin()
     # Construct epoch logs.
     epoch_logs = {}
@@ -205,7 +172,7 @@ def fit_generator(model,
 
         if not isinstance(outs, list):
           outs = [outs]
-        for l, o in zip(out_labels, outs):
+        for l, o in zip(model.metrics_names, outs):
           batch_logs[l] = o
 
         callbacks.on_batch_end(batch_index, batch_logs)
@@ -235,15 +202,15 @@ def fit_generator(model,
           if not isinstance(val_outs, list):
             val_outs = [val_outs]
           # Same labels assumed.
-          for l, o in zip(out_labels, val_outs):
+          for l, o in zip(model.metrics_names, val_outs):
             epoch_logs['val_' + l] = o
 
-        if callback_model.stop_training:
+        if callbacks.model.stop_training:
           break
 
       callbacks.on_epoch_end(epoch, epoch_logs)
       epoch += 1
-      if callback_model.stop_training:
+      if callbacks.model.stop_training:
         break
 
   finally:
@@ -266,7 +233,6 @@ def evaluate_generator(model,
                        use_multiprocessing=False,
                        verbose=0):
   """See docstring for `Model.evaluate_generator`."""
-  stateful_metric_indices = []
   if hasattr(model, 'metrics'):
     for m in model.stateful_metric_functions:
       m.reset_states()
@@ -364,7 +330,7 @@ def evaluate_generator(model,
         averages.append(
             np.average([out[i] for out in all_outs], weights=batch_sizes))
       else:
-        averages.append(float(all_outs[-1][i]))
+        averages.append(np.float64(all_outs[-1][i]))
     return averages