This repository was archived by the owner on May 21, 2025. It is now read-only.
This repository was archived by the owner on May 21, 2025. It is now read-only.
Most of the TFR Losses are always zero #363
Description
Hi,
I am trying to build a model based on tensorflow ranking. The project will try to learn the ranking rule of some online service. My features are 0-1 scaled properties of listings and labels are the numeric ranks (Positive whole numbers > 0, 1 - 500 usually).
Here is my model,
class RankingModel(tf.keras.Model):
def __init__(self, n_feats, **kwargs):
super(RankingModel, self).__init__(**kwargs)
self.n_feats = n_feats
self.inner_layers = [
Dense(1024, activation="relu"),
BatchNormalization(),
Dropout(0.2),
Dense(1024, activation="relu"),
BatchNormalization(),
Dropout(0.2),
Dense(512, activation="relu"),
BatchNormalization(),
Dropout(0.2),
Dense(512, activation="relu"),
BatchNormalization(),
Dropout(0.2),
Dense(256, activation="relu"),
BatchNormalization(),
Dropout(0.2),
Dense(256, activation="relu"),
BatchNormalization(),
Dropout(0.2),
Dense(128, activation="relu"),
BatchNormalization(),
Dropout(0.2),
Dense(128, activation="relu"),
BatchNormalization(),
Dropout(0.2),
Dense(64, activation="relu"),
BatchNormalization(),
Dropout(0.2),
Dense(64, activation="relu"),
BatchNormalization(),
Dropout(0.2),
Dense(32, activation="relu"),
BatchNormalization(),
Dropout(0.2),
Dense(32, activation="relu"),
BatchNormalization(),
Dropout(0.2)
]
self.o = Dense(1)
def call(self, inputs):
x = self.inner_layers[0](inputs)
for layer in self.inner_layers[1:]:
x = layer(x)
o = self.o(x)
return o
And here are the compilation and training statements,
import tensorflow_ranking as tfr
optimizer = tf.keras.optimizers.Adagrad(0.5)
loss = tfr.keras.losses.SoftmaxLoss()
eval_metrics = [
tfr.keras.metrics.get(key="ndcg", name="metric/ndcg"),
tfr.keras.metrics.get(key="mrr", name="metric/mrr")
]
model.compile(optimizer=optimizer, loss=loss, metrics=eval_metrics)
model.fit(train_ds, validation_data=valid_ds, epochs=3)
The datasets are properly intialized. My device is a M2 Macbook.
Any loss function I use in the tfr package, it calculates 0 in all epochs. What I am doing wrong?
Moreover, the ndcg metric is getting calculated to infinity.