De-dup few eager mode tests, remove some unused functions and params.

PiperOrigin-RevId: 206621105

1 files changed, 6 insertions, 12 deletions

diff --git a/tensorflow/python/keras/engine/training_arrays.py b/tensorflow/python/keras/engine/training_arrays.py
index adefffab11..6572e2c344 100644
--- a/tensorflow/python/keras/engine/training_arrays.py
+++ b/tensorflow/python/keras/engine/training_arrays.py
@@ -50,7 +50,6 @@ def fit_loop(model,
              val_targets=None,
              val_sample_weights=None,
              shuffle=True,
-             callback_metrics=None,
              initial_epoch=0,
              steps_per_epoch=None,
              validation_steps=None):
@@ -69,8 +68,6 @@ def fit_loop(model,
       val_targets: List of target arrays.
       val_sample_weights: Optional list of sample weight arrays.
       shuffle: Whether to shuffle the data at the beginning of each epoch
-      callback_metrics: List of strings, the display names of the metrics
-          passed to the callbacks. They should be the
           concatenation of list the display names of the outputs of
            `f` and the list of display names of the outputs of `f_val`.
       initial_epoch: Epoch at which to start training
@@ -121,9 +118,7 @@ def fit_loop(model,
 
   out_labels = model.metrics_names
   if do_validation:
-    callback_metrics = copy.copy(out_labels) + [
-        'val_' + n for n in out_labels
-    ]
+    callback_metrics = copy.copy(out_labels) + ['val_' + n for n in out_labels]
     # 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
@@ -197,9 +192,7 @@ def fit_loop(model,
     if steps_per_epoch is not None:
       # Step-wise fit loop.
       for step_index in range(steps_per_epoch):
-        batch_logs = {}
-        batch_logs['batch'] = step_index
-        batch_logs['size'] = 1
+        batch_logs = {'batch': step_index, 'size': 1}
         callbacks.on_batch_begin(step_index, batch_logs)
         try:
           outs = f(ins)
@@ -388,7 +381,9 @@ def predict_loop(model, inputs, batch_size=32, verbose=0, steps=None):
     return outs
 
 
-def test_loop(model, inputs, targets,
+def test_loop(model,
+              inputs,
+              targets,
               sample_weights=None,
               batch_size=None,
               verbose=0,
@@ -485,8 +480,7 @@ def test_loop(model, inputs, targets,
 
       if isinstance(batch_outs, list):
         if batch_index == 0:
-          for batch_out in enumerate(batch_outs):
-            outs.append(0.)
+          outs.extend([0.] * len(batch_outs))
         for i, batch_out in enumerate(batch_outs):
           if i in stateful_metric_indices:
             outs[i] = batch_out