Add skip_eval flag and change to new optimizer

official/resnet/keras/keras_cifar_main.py

@@ -179,9 +179,10 @@ def run(flags_obj):
                       validation_data=eval_input_dataset,
                       verbose=1)
 
-  eval_output = model.evaluate(eval_input_dataset,
-                               steps=num_eval_steps,
-                               verbose=1)
+  if not flags_obj.skip_eval:
+      eval_output = model.evaluate(eval_input_dataset,
+                                   steps=num_eval_steps,
+                                   verbose=1)
 
   stats = keras_common.analyze_fit_and_eval_result(history, eval_output)
 

official/resnet/keras/keras_common.py

@@ -105,7 +105,7 @@ def get_optimizer():
     learning_rate = BASE_LEARNING_RATE * FLAGS.batch_size / 256
     optimizer = tf.train.MomentumOptimizer(learning_rate=learning_rate, momentum=0.9)
   else:
-    optimizer = gradient_descent_v2.SGD(learning_rate=0.1, momentum=0.9)
+    optimizer = tf.keras.optimizers.SGD(learning_rate=0.1, momentum=0.9)
 
   return optimizer
 
@@ -138,6 +138,8 @@ def analyze_fit_and_eval_result(history, eval_output):
 
 def define_keras_flags():
   flags.DEFINE_boolean(name='enable_eager', default=False, help='Enable eager?')
+  flags.DEFINE_boolean(name='skip_eval', default=False, help='Skip evaluation?')
   flags.DEFINE_integer(
       name="train_steps", default=None,
       help="The number of steps to run for training")
+

official/resnet/keras/keras_imagenet_main.py

@@ -172,9 +172,10 @@ def run_imagenet_with_keras(flags_obj):
                       validation_data=eval_input_dataset,
                       verbose=1)
 
-  eval_output = model.evaluate(eval_input_dataset,
-                               steps=num_eval_steps,
-                               verbose=1)
+  if not flags_obj.skip_eval:
+    eval_output = model.evaluate(eval_input_dataset,
+                                 steps=num_eval_steps,
+                                 verbose=1)
 
   stats = keras_common.analyze_fit_and_eval_result(history, eval_output)