Why use axis=-1 in Keras metrics function?

Question

keras version:2.0.8

In some Keras metric functions and loss functions, use axis=-1 as parameter.

For example：

def binary_accuracy(y_true, y_pred):
    return K.mean(K.equal(y_true, K.round(y_pred)), axis=-1)

In my case:

shape of y_true:(4,256,256,2)

shape of y_pred:(4,256,256,2)

So, binary_accuracy(y_true, y_pred) should return a tensor with shape=(4,256,256) instead of a scalar tensor.

But when use binary_accuracy as metric function:

model.compile(optimizer=adam, loss=keras.losses.binary_crossentropy, metrics=[binary_accuracy])

The log still prints binary_accuracy as scalar,which confused me a lot.

Does keras do some special on the return of binary_accuracy function?

Epoch 11/300

0s - loss: 0.4158 - binary_accuracy: 0.9308 - val_loss: 0.4671 - val_binary_accuracy: 0.7767

Answer 1

Here's what you're looking for, inside training_utils.py:

def weighted(y_true, y_pred, weights, mask=None):
    """Wrapper function.
    # Arguments
        y_true: `y_true` argument of `fn`.
        y_pred: `y_pred` argument of `fn`.
        weights: Weights tensor.
        mask: Mask tensor.
    # Returns
        Scalar tensor.
    """
    # score_array has ndim >= 2
    score_array = fn(y_true, y_pred)
    if mask is not None:
        # Cast the mask to floatX to avoid float64 upcasting in Theano
        mask = K.cast(mask, K.floatx())
        # mask should have the same shape as score_array
        score_array *= mask
        #  the loss per batch should be proportional
        #  to the number of unmasked samples.
        score_array /= K.mean(mask) + K.epsilon()

    # apply sample weighting
    if weights is not None:
        # reduce score_array to same ndim as weight array
        ndim = K.ndim(score_array)
        weight_ndim = K.ndim(weights)
        score_array = K.mean(score_array,
                             axis=list(range(weight_ndim, ndim)))
        score_array *= weights
        score_array /= K.mean(K.cast(K.not_equal(weights, 0), K.floatx()))
    return K.mean(score_array)
return weighted

The metric function is called by score_array = fn(y_true, y_pred) (it's a nested function and fn is defined in the outer function). This array is averaged in the last line return K.mean(score_array). That's why you're seeing scalar metrics instead of tensors. The lines in between are just to introduce masks and weights if necessary.