Upgrade tf2 for NCF models

Author: tqtg

cornac/models/ncf/ops.py

@@ -13,8 +13,15 @@
 # limitations under the License.
 # ============================================================================
 
+import warnings
 
-import tensorflow as tf
+# disable annoying tensorflow deprecated API warnings
+warnings.filterwarnings("ignore", category=UserWarning)
+
+import tensorflow.compat.v1 as tf
+
+tf.logging.set_verbosity(tf.logging.ERROR)
+tf.disable_v2_behavior()
 
 
 act_functions = {
@@ -60,14 +67,14 @@ def emb(
             shape=[num_users, emb_size],
             dtype=tf.float32,
             initializer=tf.random_normal_initializer(stddev=0.01, seed=seed),
-            regularizer=tf.contrib.layers.l2_regularizer(scale=reg_user),
+            regularizer=tf.keras.regularizers.L2(reg_user),
         )
         item_emb = tf.get_variable(
             "item_emb",
             shape=[num_items, emb_size],
             dtype=tf.float32,
             initializer=tf.random_normal_initializer(stddev=0.01, seed=seed),
-            regularizer=tf.contrib.layers.l2_regularizer(scale=reg_item),
+            regularizer=tf.keras.regularizers.L2(reg_item),
         )
 
     return tf.nn.embedding_lookup(user_emb, uid), tf.nn.embedding_lookup(item_emb, iid)
@@ -96,7 +103,7 @@ def mlp(uid, iid, num_users, num_items, layers, reg_layers, act_fn, seed=None):
             iid=iid,
             num_users=num_users,
             num_items=num_items,
-            emb_size=layers[0] / 2,
+            emb_size=int(layers[0] / 2),
             reg_user=reg_layers[0],
             reg_item=reg_layers[0],
             seed=seed,
@@ -110,7 +117,6 @@ def mlp(uid, iid, num_users, num_items, layers, reg_layers, act_fn, seed=None):
                 name="layer{}".format(i + 1),
                 activation=act_functions.get(act_fn, tf.nn.relu),
                 kernel_initializer=tf.initializers.lecun_uniform(seed),
-                kernel_regularizer=tf.contrib.layers.l2_regularizer(reg_layers[i + 1]),
+                kernel_regularizer=tf.keras.regularizers.L2(reg_layers[i + 1]),
             )
         return interaction
-

cornac/models/ncf/recom_gmf.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 # ============================================================================
 
+
 import numpy as np
 
 from .recom_ncf_base import NCFBase

cornac/models/ncf/recom_ncf_base.py

@@ -13,12 +13,10 @@
 # limitations under the License.
 # ============================================================================
 
-import os
-import copy
+
 from tqdm.auto import trange
 
 from ..recommender import Recommender
-from ...exception import ScoreException
 from ...utils import get_rng
 
 
@@ -132,10 +130,6 @@ def fit(self, train_set, val_set=None):
     def _build_graph(self):
         import tensorflow.compat.v1 as tf
 
-        # less verbose TF
-        os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
-        tf.logging.set_verbosity(tf.logging.ERROR)
-
         self.graph = tf.Graph()
 
     def _sess_init(self):
@@ -158,8 +152,6 @@ def _step_update(self, batch_users, batch_items, batch_ratings):
         return _loss
 
     def _fit_tf(self):
-        import tensorflow.compat.v1 as tf
-
         loop = trange(self.num_epochs, disable=not self.verbose)
         for _ in loop:
             count = 0
@@ -210,9 +202,9 @@ def load(model_path, trainable=False):
             provided, the latest model will be loaded.
 
         trainable: boolean, optional, default: False
-            Set it to True if you would like to finetune the model. By default, 
+            Set it to True if you would like to finetune the model. By default,
             the model parameters are assumed to be fixed after being loaded.
-        
+
         Returns
         -------
         self : object

cornac/models/ncf/requirements.txt

@@ -1 +1 @@
-tensorflow>=1.15.2,<2.0.0
+tensorflow==2.12.0