AdamOptimizer() to Adam()

official/r1/mnist/mnist.py

@@ -125,7 +125,7 @@ def model_fn(features, labels, mode, params):
             'classify': tf.estimator.export.PredictOutput(predictions)
         })
   if mode == tf.estimator.ModeKeys.TRAIN:
-    optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=LEARNING_RATE)
+    optimizer = tf.keras.optimizers.Adam(learning_rate=LEARNING_RATE)
 
     logits = model(image, training=True)
     loss = tf.compat.v1.losses.sparse_softmax_cross_entropy(labels=labels,

official/r1/wide_deep/movielens_main.py

@@ -69,7 +69,7 @@ def build_estimator(model_dir, model_type, model_column_fn, inter_op, intra_op):
       model_dir=model_dir,
       feature_columns=deep_columns,
       hidden_units=hidden_units,
-      optimizer=tf.train.AdamOptimizer(),
+      optimizer=tf.keras.optimizers.Adam(),
       activation_fn=tf.nn.sigmoid,
       dropout=0.3,
       loss_reduction=tf.losses.Reduction.MEAN)

official/recommendation/neumf_model.py

@@ -109,7 +109,7 @@ def neumf_model_fn(features, labels, mode, params):
     mlperf_helper.ncf_print(key=mlperf_helper.TAGS.OPT_HP_ADAM_EPSILON,
                             value=params["epsilon"])
 
-    optimizer = tf.compat.v1.train.AdamOptimizer(
+    optimizer = tf.keras.optimizers.Adam(
         learning_rate=params["learning_rate"],
         beta1=params["beta1"],
         beta2=params["beta2"],