def compile_model(model, params, learning_rate, gradient_clip_norm):
"""Compile keras model."""
model.compile(
optimizer=_get_optimizer(
learning_rate=learning_rate, gradient_clip_norm=gradient_clip_norm),
loss=losses.GlobalSoftmax(),
metrics=_get_metrics(params['eval_top_k']))
def build_keras_model(params):
"""Construct and compile recommendation keras model."""
model = recommendation_model.RecommendationModel(params)
model.compile(optimizer=_get_optimizer(
learning_rate=FLAGS.learning_rate,
gradient_clip_norm=FLAGS.gradient_clip_norm),
loss=losses.GlobalSoftmax(),
metrics=_get_metrics(params['eval_top_k']))
return model
def test_global_softmax_loss(self):
global_softmax = keras_losses.GlobalSoftmax()
true_label = tf.constant([[2], [0], [1]], dtype=tf.int32)
logits = tf.constant(
[[0.8, 0.1, 0.2, 0.3], [0.2, 0.7, 0.1, 0.5], [0.5, 0.4, 0.9, 0.2]],
dtype=tf.float32)
self.assertBetween(
global_softmax.call(y_true=true_label, y_pred=logits).numpy(), 1.5,
1.6)