CategoricalCrossentropy

Computes the crossentropy loss between the labels and predictions.

Use this crossentropy loss function when there are two or more label classes. We expect labels to be provided in a one_hot representation. If you want to provide labels as integers, please use SparseCategoricalCrossentropy loss. There should be # classes floating point values per feature.

Standalone usage:

    Operand<TFloat32> labels =
        tf.constant(new float[][] { {0, 1, 0}, {0, 0, 1} });
    Operand<TFloat32> predictions =
        tf.constant(new float[][] { {0.05f, 0.95f, 0f}, {0.1f, 0.8f, 0.1f} });
    CategoricalCrossentropy cce = new CategoricalCrossentropy(tf);
    Operand<TFloat32> result = cce.call(labels, predictions);
    // produces 1.177
 

Calling with sample weight:

    Operand<TFloat32> sampleWeight = tf.constant(new float[] {0.3f, 0.7f});
    Operand<TFloat32> result = cce.call(labels, predictions, sampleWeight);
    // produces 0.814f
 

Using SUM reduction type:

    CategoricalCrossentropy cce = new CategoricalCrossentropy(tf, Reduction.SUM);
    Operand<TFloat32> result = cce.call(labels, predictions);
    // produces 2.354f
 

Using NONE reduction type:

    CategoricalCrossentropy cce =
        new CategoricalCrossentropy(tf, Reduction.NONE);
    Operand<TFloat32> result = cce.call(labels, predictions);
    // produces [0.0513f, 2.303f]