fix

model_training_nnn_tpu/rnn_model_tf.py

@@ -79,8 +79,8 @@ class NoiseModel(keras.Model):
             return_state=True,
             dropout=self.rnn_dropout,
             recurrent_dropout=0.0,  # Avoid recurrent dropout on TPU
-            kernel_initializer='glorot_uniform',
-            recurrent_initializer='orthogonal',
+            kernel_initializer=tf.keras.initializers.GlorotUniform(),
+            recurrent_initializer=tf.keras.initializers.Orthogonal(),
             name='noise_gru1'
         )
 
@@ -90,8 +90,8 @@ class NoiseModel(keras.Model):
             return_state=True,
             dropout=self.rnn_dropout,
             recurrent_dropout=0.0,
-            kernel_initializer='glorot_uniform',
-            recurrent_initializer='orthogonal',
+            kernel_initializer=tf.keras.initializers.GlorotUniform(),
+            recurrent_initializer=tf.keras.initializers.Orthogonal(),
             name='noise_gru2'
         )
 
@@ -246,8 +246,8 @@ class CleanSpeechModel(keras.Model):
             return_state=True,
             dropout=self.rnn_dropout,
             recurrent_dropout=0.0,
-            kernel_initializer='glorot_uniform',
-            recurrent_initializer='orthogonal',
+            kernel_initializer=tf.keras.initializers.GlorotUniform(),
+            recurrent_initializer=tf.keras.initializers.Orthogonal(),
             name='clean_gru1'
         )
 
@@ -257,8 +257,8 @@ class CleanSpeechModel(keras.Model):
             return_state=True,
             dropout=self.rnn_dropout,
             recurrent_dropout=0.0,
-            kernel_initializer='glorot_uniform',
-            recurrent_initializer='orthogonal',
+            kernel_initializer=tf.keras.initializers.GlorotUniform(),
+            recurrent_initializer=tf.keras.initializers.Orthogonal(),
             name='clean_gru2'
         )
 
@@ -268,15 +268,15 @@ class CleanSpeechModel(keras.Model):
             return_state=True,
             dropout=self.rnn_dropout,
             recurrent_dropout=0.0,
-            kernel_initializer='glorot_uniform',
-            recurrent_initializer='orthogonal',
+            kernel_initializer=tf.keras.initializers.GlorotUniform(),
+            recurrent_initializer=tf.keras.initializers.Orthogonal(),
             name='clean_gru3'
         )
 
         # Output classification layer
         self.output_layer = layers.Dense(
             n_classes,
-            kernel_initializer='glorot_uniform',
+            kernel_initializer=tf.keras.initializers.GlorotUniform(),
             name='clean_output'
         )
 
@@ -407,8 +407,8 @@ class NoisySpeechModel(keras.Model):
             return_state=True,
             dropout=self.rnn_dropout,
             recurrent_dropout=0.0,
-            kernel_initializer='glorot_uniform',
-            recurrent_initializer='orthogonal',
+            kernel_initializer=tf.keras.initializers.GlorotUniform(),
+            recurrent_initializer=tf.keras.initializers.Orthogonal(),
             name='noisy_gru1'
         )
 
@@ -418,15 +418,15 @@ class NoisySpeechModel(keras.Model):
             return_state=True,
             dropout=self.rnn_dropout,
             recurrent_dropout=0.0,
-            kernel_initializer='glorot_uniform',
-            recurrent_initializer='orthogonal',
+            kernel_initializer=tf.keras.initializers.GlorotUniform(),
+            recurrent_initializer=tf.keras.initializers.Orthogonal(),
             name='noisy_gru2'
         )
 
         # Output classification layer
         self.output_layer = layers.Dense(
             n_classes,
-            kernel_initializer='glorot_uniform',
+            kernel_initializer=tf.keras.initializers.GlorotUniform(),
             name='noisy_output'
         )