Datasets:
HenryAI
/
KerasBERTv1-Data

Dataset card Data Studio Files Community
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... reduction=tf.keras.losses.Reduction.SUM)
>>> h(y_true, y_pred).numpy()
0.31
>>> # Using 'none' reduction type.
>>> h = tf.keras.losses.Huber(
... reduction=tf.keras.losses.Reduction.NONE)
>>> h(y_true, y_pred).numpy()
array([0.18, 0.13], dtype=float32)
Usage with the compile() API:
model.compile(optimizer='sgd', loss=tf.keras.losses.Huber())
huber function
tf.keras.losses.huber(y_true, y_pred, delta=1.0)
Computes Huber loss value.
For each value x in error = y_true - y_pred:
loss = 0.5 * x^2 if |x| <= d
loss = d * |x| - 0.5 * d^2 if |x| > d
where d is delta. See: https://en.wikipedia.org/wiki/Huber_loss
Arguments
y_true: tensor of true targets.
y_pred: tensor of predicted targets.
delta: A float, the point where the Huber loss function changes from a quadratic to linear.
Returns
Tensor with one scalar loss entry per sample.
LogCosh class
tf.keras.losses.LogCosh(reduction="auto", name="log_cosh")
Computes the logarithm of the hyperbolic cosine of the prediction error.
logcosh = log((exp(x) + exp(-x))/2), where x is the error y_pred - y_true.
Standalone usage:
>>> y_true = [[0., 1.], [0., 0.]]
>>> y_pred = [[1., 1.], [0., 0.]]
>>> # Using 'auto'/'sum_over_batch_size' reduction type.
>>> l = tf.keras.losses.LogCosh()
>>> l(y_true, y_pred).numpy()
0.108
>>> # Calling with 'sample_weight'.
>>> l(y_true, y_pred, sample_weight=[0.8, 0.2]).numpy()
0.087
>>> # Using 'sum' reduction type.
>>> l = tf.keras.losses.LogCosh(
... reduction=tf.keras.losses.Reduction.SUM)
>>> l(y_true, y_pred).numpy()
0.217
>>> # Using 'none' reduction type.
>>> l = tf.keras.losses.LogCosh(
... reduction=tf.keras.losses.Reduction.NONE)
>>> l(y_true, y_pred).numpy()
array([0.217, 0.], dtype=float32)
Usage with the compile() API:
model.compile(optimizer='sgd', loss=tf.keras.losses.LogCosh())
log_cosh function
tf.keras.losses.log_cosh(y_true, y_pred)
Logarithm of the hyperbolic cosine of the prediction error.
log(cosh(x)) is approximately equal to (x ** 2) / 2 for small x and to abs(x) - log(2) for large x. This means that 'logcosh' works mostly like the mean squared error, but will not be so strongly affected by the occasional wildly incorrect prediction.
Standalone usage:
>>> y_true = np.random.random(size=(2, 3))
>>> y_pred = np.random.random(size=(2, 3))
>>> loss = tf.keras.losses.logcosh(y_true, y_pred)
>>> assert loss.shape == (2,)
>>> x = y_pred - y_true
>>> assert np.allclose(
... loss.numpy(),
... np.mean(x + np.log(np.exp(-2. * x) + 1.) - math_ops.log(2.), axis=-1),
... atol=1e-5)
Arguments
y_true: Ground truth values. shape = [batch_size, d0, .. dN].
y_pred: The predicted values. shape = [batch_size, d0, .. dN].
Returns
Logcosh error values. shape = [batch_size, d0, .. dN-1].
Hinge losses for "maximum-margin" classification
Hinge class