Datasets:
HenryAI
/
KerasBERTv1-Data

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>>> y_true = np.random.random(size=(2, 3))
>>> y_true = np.maximum(y_true, 1e-7) # Prevent division by zero
>>> y_pred = np.random.random(size=(2, 3))
>>> loss = tf.keras.losses.mean_absolute_percentage_error(y_true, y_pred)
>>> assert loss.shape == (2,)
>>> assert np.array_equal(
... loss.numpy(),
... 100. * np.mean(np.abs((y_true - y_pred) / y_true), axis=-1))
Arguments
y_true: Ground truth values. shape = [batch_size, d0, .. dN].
y_pred: The predicted values. shape = [batch_size, d0, .. dN].
Returns
Mean absolute percentage error values. shape = [batch_size, d0, .. dN-1].
mean_squared_logarithmic_error function
tf.keras.losses.mean_squared_logarithmic_error(y_true, y_pred)
Computes the mean squared logarithmic error between y_true and y_pred.
loss = mean(square(log(y_true + 1) - log(y_pred + 1)), axis=-1)
Standalone usage:
>>> y_true = np.random.randint(0, 2, size=(2, 3))
>>> y_pred = np.random.random(size=(2, 3))
>>> loss = tf.keras.losses.mean_squared_logarithmic_error(y_true, y_pred)
>>> assert loss.shape == (2,)
>>> y_true = np.maximum(y_true, 1e-7)
>>> y_pred = np.maximum(y_pred, 1e-7)
>>> assert np.allclose(
... loss.numpy(),
... np.mean(
... np.square(np.log(y_true + 1.) - np.log(y_pred + 1.)), axis=-1))
Arguments
y_true: Ground truth values. shape = [batch_size, d0, .. dN].
y_pred: The predicted values. shape = [batch_size, d0, .. dN].
Returns
Mean squared logarithmic error values. shape = [batch_size, d0, .. dN-1].
cosine_similarity function
tf.keras.losses.cosine_similarity(y_true, y_pred, axis=-1)
Computes the cosine similarity between labels and predictions.
Note that it is a number between -1 and 1. When it is a negative number between -1 and 0, 0 indicates orthogonality and values closer to -1 indicate greater similarity. The values closer to 1 indicate greater dissimilarity. This makes it usable as a loss function in a setting where you try to maximize the proximity between predictions and targets. If either y_true or y_pred is a zero vector, cosine similarity will be 0 regardless of the proximity between predictions and targets.
loss = -sum(l2_norm(y_true) * l2_norm(y_pred))
Standalone usage:
>>> y_true = [[0., 1.], [1., 1.], [1., 1.]]
>>> y_pred = [[1., 0.], [1., 1.], [-1., -1.]]
>>> loss = tf.keras.losses.cosine_similarity(y_true, y_pred, axis=1)
>>> loss.numpy()
array([-0., -0.999, 0.999], dtype=float32)
Arguments
y_true: Tensor of true targets.
y_pred: Tensor of predicted targets.
axis: Axis along which to determine similarity.
Returns
Cosine similarity tensor.
Huber class
tf.keras.losses.Huber(delta=1.0, reduction="auto", name="huber_loss")
Computes the Huber loss between y_true and y_pred.
For each value x in error = y_true - y_pred:
loss = 0.5 * x^2 if |x| <= d
loss = 0.5 * d^2 + d * (|x| - d) if |x| > d
where d is delta. See: https://en.wikipedia.org/wiki/Huber_loss
Standalone usage:
>>> y_true = [[0, 1], [0, 0]]
>>> y_pred = [[0.6, 0.4], [0.4, 0.6]]
>>> # Using 'auto'/'sum_over_batch_size' reduction type.
>>> h = tf.keras.losses.Huber()
>>> h(y_true, y_pred).numpy()
0.155
>>> # Calling with 'sample_weight'.
>>> h(y_true, y_pred, sample_weight=[1, 0]).numpy()
0.09
>>> # Using 'sum' reduction type.
>>> h = tf.keras.losses.Huber(