def step_fn(features):
"""Computes loss and applied gradient per replica."""
with tf.GradientTape() as tape:
softmax_logits = keras_model(features)
labels = features[rconst.TRAIN_LABEL_KEY]
loss = loss_object(labels, softmax_logits,
sample_weight=features[rconst.VALID_POINT_MASK])
loss *= (1.0 / (batch_size*strategy.num_replicas_in_sync))
grads = tape.gradient(loss, keras_model.trainable_variables)
# Converting gradients to dense form helps in perf on GPU for NCF
grads = neumf_model.sparse_to_dense_grads(
list(zip(grads, keras_model.trainable_variables)))
optimizer.apply_gradients(grads)
return loss
def step_fn(features):
"""Computes loss and applied gradient per replica."""
with tf.GradientTape() as tape:
softmax_logits = keras_model(features)
labels = features[rconst.TRAIN_LABEL_KEY]
loss = loss_object(
labels,
softmax_logits,
sample_weight=features[rconst.VALID_POINT_MASK])
loss *= (1.0 / params["batch_size"])
if FLAGS.dtype == "fp16":
loss = optimizer.get_scaled_loss(loss)
grads = tape.gradient(loss, keras_model.trainable_variables)
if FLAGS.dtype == "fp16":
grads = optimizer.get_unscaled_gradients(grads)
# Converting gradients to dense form helps in perf on GPU for NCF
grads = neumf_model.sparse_to_dense_grads(
list(zip(grads, keras_model.trainable_variables)))
optimizer.apply_gradients(grads)
return loss
