Datasets:
HenryAI
/
KerasBERTv1-Data

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from_logits=False, reduction="auto", name="sparse_categorical_crossentropy"
)
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 as integers. If you want to provide labels using one-hot representation, please use CategoricalCrossentropy loss. There should be # classes floating point values per feature for y_pred and a single floating point value per feature for y_true.
In the snippet below, there is a single floating point value per example for y_true and # classes floating pointing values per example for y_pred. The shape of y_true is [batch_size] and the shape of y_pred is [batch_size, num_classes].
Standalone usage:
>>> y_true = [1, 2]
>>> y_pred = [[0.05, 0.95, 0], [0.1, 0.8, 0.1]]
>>> # Using 'auto'/'sum_over_batch_size' reduction type.
>>> scce = tf.keras.losses.SparseCategoricalCrossentropy()
>>> scce(y_true, y_pred).numpy()
1.177
>>> # Calling with 'sample_weight'.
>>> scce(y_true, y_pred, sample_weight=tf.constant([0.3, 0.7])).numpy()
0.814
>>> # Using 'sum' reduction type.
>>> scce = tf.keras.losses.SparseCategoricalCrossentropy(
... reduction=tf.keras.losses.Reduction.SUM)
>>> scce(y_true, y_pred).numpy()
2.354
>>> # Using 'none' reduction type.
>>> scce = tf.keras.losses.SparseCategoricalCrossentropy(
... reduction=tf.keras.losses.Reduction.NONE)
>>> scce(y_true, y_pred).numpy()
array([0.0513, 2.303], dtype=float32)
Usage with the compile() API:
model.compile(optimizer='sgd',
loss=tf.keras.losses.SparseCategoricalCrossentropy())
Poisson class
tf.keras.losses.Poisson(reduction="auto", name="poisson")
Computes the Poisson loss between y_true and y_pred.
loss = y_pred - y_true * log(y_pred)
Standalone usage:
>>> y_true = [[0., 1.], [0., 0.]]
>>> y_pred = [[1., 1.], [0., 0.]]
>>> # Using 'auto'/'sum_over_batch_size' reduction type.
>>> p = tf.keras.losses.Poisson()
>>> p(y_true, y_pred).numpy()
0.5
>>> # Calling with 'sample_weight'.
>>> p(y_true, y_pred, sample_weight=[0.8, 0.2]).numpy()
0.4
>>> # Using 'sum' reduction type.
>>> p = tf.keras.losses.Poisson(
... reduction=tf.keras.losses.Reduction.SUM)
>>> p(y_true, y_pred).numpy()
0.999
>>> # Using 'none' reduction type.
>>> p = tf.keras.losses.Poisson(
... reduction=tf.keras.losses.Reduction.NONE)
>>> p(y_true, y_pred).numpy()
array([0.999, 0.], dtype=float32)
Usage with the compile() API:
model.compile(optimizer='sgd', loss=tf.keras.losses.Poisson())
binary_crossentropy function
tf.keras.losses.binary_crossentropy(
y_true, y_pred, from_logits=False, label_smoothing=0
)
Computes the binary crossentropy loss.
Standalone usage:
>>> y_true = [[0, 1], [0, 0]]
>>> y_pred = [[0.6, 0.4], [0.4, 0.6]]
>>> loss = tf.keras.losses.binary_crossentropy(y_true, y_pred)
>>> assert loss.shape == (2,)
>>> loss.numpy()
array([0.916 , 0.714], dtype=float32)
Arguments
y_true: Ground truth values. shape = [batch_size, d0, .. dN].
y_pred: The predicted values. shape = [batch_size, d0, .. dN].
from_logits: Whether y_pred is expected to be a logits tensor. By default, we assume that y_pred encodes a probability distribution.