Fix deprecation warnings related to TF v1

keras/backend/tensorflow_backend.py

@@ -109,6 +109,12 @@ def eager(func):
     return eager_fn_wrapper
 
 
+def _has_compat_v1():
+    if hasattr(tf, 'compat') and hasattr(tf.compat, 'v1'):
+        return True
+    return False
+
+
 def get_uid(prefix=''):
     """Provides a unique UID given a string prefix.
 
@@ -2270,7 +2276,11 @@ def _fused_normalize_batch_in_training(x, gamma, beta, reduction_axes,
     if beta.dtype != tf.float32:
         beta = tf.cast(beta, tf.float32)
 
-    return tf.nn.fused_batch_norm(
+    if _has_compat_v1:
+        fused_batch_norm = tf.compat.v1.nn.fused_batch_norm
+    else:
+        fused_batch_norm = tf.nn.fused_batch_norm
+    return fused_batch_norm(
         x,
         gamma,
         beta,
@@ -2373,7 +2383,12 @@ def batch_normalization(x, mean, var, beta, gamma, axis=-1, epsilon=1e-3):
             if var.dtype != tf.float32:
                 var = tf.cast(var, tf.float32)
 
-            y, _, _ = tf.nn.fused_batch_norm(
+            if _has_compat_v1:
+                fused_batch_norm = tf.compat.v1.nn.fused_batch_norm
+            else:
+                fused_batch_norm = tf.nn.fused_batch_norm
+
+            y, _, _ = fused_batch_norm(
                 x,
                 gamma,
                 beta,

keras/optimizers.py

@@ -853,7 +853,11 @@ def get(identifier):
     if K.backend() == 'tensorflow':
         # Wrap TF optimizer instances
         if tf.__version__.startswith('1.'):
-            if isinstance(identifier, tf.train.Optimizer):
+            try:
+                TFOpt = tf.compat.v1.train.Optimizer
+            except AttributeError:
+                TFOpt = tf.train.Optimizer
+            if isinstance(identifier, TFOpt):
                 return TFOptimizer(identifier)
         elif isinstance(identifier, tf.keras.optimizers.Optimizer):
             return TFOptimizer(identifier)