Missed TensorFlow World? Check out the recap. Learn more

tf.keras.backend.categorical_crossentropy

TensorFlow 1 version View source on GitHub

Categorical crossentropy between an output tensor and a target tensor.

Aliases:

tf.keras.backend.categorical_crossentropy(
    target,
    output,
    from_logits=False,
    axis=-1
)

Arguments:

  • target: A tensor of the same shape as output.
  • output: A tensor resulting from a softmax (unless from_logits is True, in which case output is expected to be the logits).
  • from_logits: Boolean, whether output is the result of a softmax, or is a tensor of logits.
  • axis: Int specifying the channels axis. axis=-1 corresponds to data format channels_last', andaxis=1corresponds to data formatchannels_first`.

Returns:

Output tensor.

Raises:

  • ValueError: if axis is neither -1 nor one of the axes of output.

Example:

    import tensorflow as tf
    from tensorflow.keras import backend as K
    a = tf.constant([1., 0., 0., 0., 1., 0., 0., 0., 1.], shape=[3,3])
    print("a: ", a)
    b = tf.constant([.9, .05, .05, .5, .89, .6, .05, .01, .94], shape=[3,3])
    print("b: ", b)
    loss = K.categorical_crossentropy(a, b)
    print('Loss: ', loss) #Loss: tf.Tensor([0.10536055 0.8046684  0.06187541], shape=(3,), dtype=float32)
    loss = K.categorical_crossentropy(a, a)
    print('Loss: ', loss) #Loss:  tf.Tensor([1.1920929e-07 1.1920929e-07 1.1920929e-07], shape=(3,), dtype=float32)