The summation is in the fact that the implementation computes bce with the two terms (one in bce =. log( output + epsilon())īce += ( 1 - target) * tf. # Compute cross entropy from probabilities.īce = target * tf. clip_by_value( output, epsilon_, 1.0 - epsilon_) sigmoid_cross_entropy_with_logits(Įpsilon_ = _constant_to_tensor( epsilon(), output. Output, from_logits, "Sigmoid", "binary_crossentropy"
Target: A tensor with the same shape as `output`.įrom_logits: Whether `output` is expected to be a logits tensor. """Binary crossentropy between an output tensor and a target tensor. However, the mistake is that the backend.binary_crossentropy function already performs this reduction internally:ĭef binary_crossentropy( target, output, from_logits = False): In other words, with a summation, you take the sum over -x*log(x), with x being the probabilities summing to 1), like here (from Wiki): However, the reduction axis there is very weird, and the author clearly got confused with the more general crossentropy definition (where you indeed take the expected value of -log(x). So clearly, judging by its name, this function promises to calculate the binary crossentropy. binary_crossentropy( y_true, y_pred, from_logits = from_logits), Label_smoothing, _smooth_labels, lambda: y_trueīackend. Return y_true * ( 1.0 - label_smoothing) + 0.5 * label_smoothing convert_to_tensor( label_smoothing, dtype = y_pred. Defaults to -1.īinary crossentropy loss value. 0.5 * label_smoothing`įor the target class and `0.5 * label_smoothing` for the non-targetĪxis: The axis along which the mean is computed.
Squeezing them towards 0.5 That is, using `1. Byĭefault, we assume that `y_pred` encodes a probability distribution. shape = ``.įrom_logits: Whether `y_pred` is expected to be a logits tensor. > loss = tf._crossentropy(y_true, y_pred) """Computes the binary crossentropy loss. Y_true, y_pred, from_logits = False, label_smoothing = 0.0, axis = - 1
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