Merge pull request #1508 from microsoft/bug/vae_keras

Missing import in multinomial_vae.py and standard_vae.py
recommenders-team · Aug 27, 2021 · dbc49ca · dbc49ca
2 parents 3e23511 + 987957e
commit dbc49ca
Show file tree

Hide file tree

Showing 2 changed files with 9 additions and 8 deletions.
diff --git a/recommenders/models/vae/multinomial_vae.py b/recommenders/models/vae/multinomial_vae.py
@@ -287,8 +287,8 @@ def _create_model(self):
         self.h = Dense(
             self.intermediate_dim,
             activation="tanh",
-            kernel_initializer=keras.initializers.glorot_uniform(seed=self.seed),
-            bias_initializer=keras.initializers.truncated_normal(
+            kernel_initializer=tf.keras.initializers.glorot_uniform(seed=self.seed),
+            bias_initializer=tf.keras.initializers.truncated_normal(
                 stddev=0.001, seed=self.seed
             ),
         )(self.dropout_encoder)
@@ -304,8 +304,8 @@ def _create_model(self):
         self.h_decoder = Dense(
             self.intermediate_dim,
             activation="tanh",
-            kernel_initializer=keras.initializers.glorot_uniform(seed=self.seed),
-            bias_initializer=keras.initializers.truncated_normal(
+            kernel_initializer=tf.keras.initializers.glorot_uniform(seed=self.seed),
+            bias_initializer=tf.keras.initializers.truncated_normal(
                 stddev=0.001, seed=self.seed
             ),
         )
@@ -318,7 +318,7 @@ def _create_model(self):
         # Training
         self.model = Model(self.x, self.x_decoded)
         self.model.compile(
-            optimizer=keras.optimizers.Adam(learning_rate=0.001),
+            optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
             loss=self._get_vae_loss,
         )
 

diff --git a/recommenders/models/vae/standard_vae.py b/recommenders/models/vae/standard_vae.py
@@ -5,15 +5,16 @@
 import pandas as pd
 import matplotlib.pyplot as plt
 import seaborn as sns
-from recommenders.evaluation.python_evaluation import ndcg_at_k
-
 import bottleneck as bn
+import tensorflow as tf
 from tensorflow.keras.layers import *
 from tensorflow.keras.models import Model
 from tensorflow.keras.losses import binary_crossentropy
 from tensorflow.keras import backend as K
 from tensorflow.keras.callbacks import ReduceLROnPlateau, ModelCheckpoint, Callback
 
+from recommenders.evaluation.python_evaluation import ndcg_at_k
+
 
 class LossHistory(Callback):
     """This class is used for saving the validation loss and the training loss per epoch."""
@@ -292,7 +293,7 @@ def _create_model(self):
         # Training
         self.model = Model(self.x, self.x_decoded)
         self.model.compile(
-            optimizer=keras.optimizers.Adam(learning_rate=0.001),
+            optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
             loss=self._get_vae_loss,
         )