tf.compat.v1.losses.softmax_cross_entropy

Creates a cross-entropy loss using tf.nn.softmax_cross_entropy_with_logits_v2.

tf.compat.v1.losses.softmax_cross_entropy(
    onehot_labels,
    logits,
    weights=1.0,
    label_smoothing=0,
    scope=None,
    loss_collection=ops.GraphKeys.LOSSES,
    reduction=Reduction.SUM_BY_NONZERO_WEIGHTS
)

Migrate to TF2

tf.compat.v1.losses.softmax_cross_entropy is mostly compatible with eager execution and tf.function. But, the loss_collection argument is ignored when executing eagerly and no loss will be written to the loss collections. You will need to either hold on to the return value manually or rely on tf.keras.Model loss tracking.

To switch to native TF2 style, instantiate the tf.keras.losses.CategoricalCrossentropy class with from_logits set as True and call the object instead.

Structural Mapping to Native TF2

Before:

loss = tf.compat.v1.losses.softmax_cross_entropy(
  onehot_labels=onehot_labels,
  logits=logits,
  weights=weights,
  label_smoothing=smoothing)

After:

loss_fn = tf.keras.losses.CategoricalCrossentropy(
  from_logits=True,
  label_smoothing=smoothing)
loss = loss_fn(
  y_true=onehot_labels,
  y_pred=logits,
  sample_weight=weights)

How to Map Arguments

TF1 Arg Name TF2 Arg Name Note
- from_logits Set from_logits as True to have identical behavior
onehot_labels y_true In __call__() method
logits y_pred In __call__() method
weights sample_weight In __call__() method
label_smoothing label_smoothing In constructor
scope Not supported -
loss_collection Not supported Losses should be tracked explicitly or with Keras APIs, for example, add_loss, instead of via collections
reduction reduction In constructor. Value of tf.compat.v1.losses.Reduction.SUM_OVER_BATCH_SIZE, tf.compat.v1.losses.Reduction.SUM, tf.compat.v1.losses.Reduction.NONE in tf.compat.v1.losses.softmax_cross_entropy correspond to tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE, tf.keras.losses.Reduction.SUM, tf.keras.losses.Reduction.NONE, respectively. If you used other value for reduction, including the default value tf.compat.v1.losses.Reduction.SUM_BY_NONZERO_WEIGHTS, there is no directly corresponding value. Please modify the loss implementation manually.

Before & After Usage Example

Before:

y_true = [[0, 1, 0], [0, 0, 1]]
y_pred = [[0.05, 0.95, 0], [0.1, 0.8, 0.1]]
weights = [0.3, 0.7]
smoothing = 0.2
tf.compat.v1.losses.softmax_cross_entropy(y_true, y_pred, weights=weights,
  label_smoothing=smoothing).numpy()
0.57618

After:

cce = tf.keras.losses.CategoricalCrossentropy(from_logits=True,
  label_smoothing=smoothing)
cce(y_true, y_pred, sample_weight=weights).numpy()
0.57618