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pythontensorflowkerastensorflow2.0
Leevo
Leevo

Reputation: 1753

TensorFlow 2.0: What is the difference between sparse_categorical_crossentropy and SparseCategoricalCrossentropy?

Reading the the docs of TensorFlow 2.0, I found:

tf.keras.losses.sparse_categorical_crossentropy

and

tf.keras.losses.SparseCategoricalCrossentropy

the way they are used in tutorials, their arguments, their descriptions, they seem equal to me. What is the difference between the two?

Upvotes: 3

Views: 2490

Answers (2)

Burton2000
Burton2000

Reputation: 2072

One is a function and one is a class.

First one is the functional version, it will just spit out the value of the loss when you evaluate it.

The second one is the class version. You need to evaluate instance of the class itself in order to get the loss value.

I believe you are right that there is little difference, if using the keras api the only difference will be when you compile the model.

e.g.

model.compile(loss=tf.keras.losses.sparse_categorical_crossentropy)

vs

model.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy())

Notice the extra brackets on the class version, you need an instance of a class to be passed in.

Upvotes: 1

Nicolas Gervais
Nicolas Gervais

Reputation: 36624

There is none. If you look at the documentation you linked, you can get to the source code on GitHub. Both point to the same object:

def sparse_categorical_crossentropy(target, output, from_logits=False, axis=-1):
  """Categorical crossentropy with integer targets.

  if not from_logits:
    if (isinstance(output, (ops.EagerTensor, variables_module.Variable)) or
        output.op.type != 'Softmax'):
      epsilon_ = _constant_to_tensor(epsilon(), output.dtype.base_dtype)
      output = clip_ops.clip_by_value(output, epsilon_, 1 - epsilon_)
      output = math_ops.log(output)
      # ... blablabla

Which is in:

tensorflow/python/keras/backend.py

For instance, the former (tf.keras.losses.sparse_categorical_crossentropy) is called like this:

def sparse_categorical_crossentropy(y_true, y_pred, from_logits=False, axis=-1):
  return K.sparse_categorical_crossentropy(
      y_true, y_pred, from_logits=from_logits, axis=axis)

So it points to the other one in tensorflow/python/keras/backend.py

Upvotes: 1

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