diff --git a/model_training_nnn_tpu/rnn_model_tf.py b/model_training_nnn_tpu/rnn_model_tf.py
index 14dd242..e34fcf2 100644
--- a/model_training_nnn_tpu/rnn_model_tf.py
+++ b/model_training_nnn_tpu/rnn_model_tf.py
@@ -99,13 +99,13 @@ class NoiseModel(keras.Model):
         self.h0_1 = self.add_weight(
             name='h0_1',
             shape=(1, self.input_size),
-            initializer='glorot_uniform',
+            initializer=tf.keras.initializers.GlorotUniform(),
             trainable=True
         )
         self.h0_2 = self.add_weight(
             name='h0_2',
             shape=(1, self.input_size),
-            initializer='glorot_uniform',
+            initializer=tf.keras.initializers.GlorotUniform(),
             trainable=True
         )
 
@@ -284,19 +284,19 @@ class CleanSpeechModel(keras.Model):
         self.h0_1 = self.add_weight(
             name='h0_1',
             shape=(1, n_units),
-            initializer='glorot_uniform',
+            initializer=tf.keras.initializers.GlorotUniform(),
             trainable=True
         )
         self.h0_2 = self.add_weight(
             name='h0_2',
             shape=(1, n_units),
-            initializer='glorot_uniform',
+            initializer=tf.keras.initializers.GlorotUniform(),
             trainable=True
         )
         self.h0_3 = self.add_weight(
             name='h0_3',
             shape=(1, n_units),
-            initializer='glorot_uniform',
+            initializer=tf.keras.initializers.GlorotUniform(),
             trainable=True
         )
 
@@ -434,13 +434,13 @@ class NoisySpeechModel(keras.Model):
         self.h0_1 = self.add_weight(
             name='h0_1',
             shape=(1, n_units),
-            initializer='glorot_uniform',
+            initializer=tf.keras.initializers.GlorotUniform(),
             trainable=True
         )
         self.h0_2 = self.add_weight(
             name='h0_2',
             shape=(1, n_units),
-            initializer='glorot_uniform',
+            initializer=tf.keras.initializers.GlorotUniform(),
             trainable=True
         )