Datasets:
HenryAI
/
KerasBERTv1-Data

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Use this cross-entropy loss for binary (0 or 1) classification applications. The loss function requires the following inputs:
y_true (true label): This is either 0 or 1.
y_pred (predicted value): This is the model's prediction, i.e, a single floating-point value which either represents a logit, (i.e, value in [-inf, inf] when from_logits=True) or a probability (i.e, value in [0., 1.] when from_logits=False).
Recommended Usage: (set from_logits=True)
With tf.keras API:
model.compile(
loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
....
)
As a standalone function:
>>> # Example 1: (batch_size = 1, number of samples = 4)
>>> y_true = [0, 1, 0, 0]
>>> y_pred = [-18.6, 0.51, 2.94, -12.8]
>>> bce = tf.keras.losses.BinaryCrossentropy(from_logits=True)
>>> bce(y_true, y_pred).numpy()
0.865
>>> # Example 2: (batch_size = 2, number of samples = 4)
>>> y_true = [[0, 1], [0, 0]]
>>> y_pred = [[-18.6, 0.51], [2.94, -12.8]]
>>> # Using default 'auto'/'sum_over_batch_size' reduction type.
>>> bce = tf.keras.losses.BinaryCrossentropy(from_logits=True)
>>> bce(y_true, y_pred).numpy()
0.865
>>> # Using 'sample_weight' attribute
>>> bce(y_true, y_pred, sample_weight=[0.8, 0.2]).numpy()
0.243
>>> # Using 'sum' reduction` type.
>>> bce = tf.keras.losses.BinaryCrossentropy(from_logits=True,
... reduction=tf.keras.losses.Reduction.SUM)
>>> bce(y_true, y_pred).numpy()
1.730
>>> # Using 'none' reduction type.
>>> bce = tf.keras.losses.BinaryCrossentropy(from_logits=True,
... reduction=tf.keras.losses.Reduction.NONE)
>>> bce(y_true, y_pred).numpy()
array([0.235, 1.496], dtype=float32)
Default Usage: (set from_logits=False)
>>> # Make the following updates to the above "Recommended Usage" section
>>> # 1. Set `from_logits=False`
>>> tf.keras.losses.BinaryCrossentropy() # OR ...('from_logits=False')
>>> # 2. Update `y_pred` to use probabilities instead of logits
>>> y_pred = [0.6, 0.3, 0.2, 0.8] # OR [[0.6, 0.3], [0.2, 0.8]]
CategoricalCrossentropy class
tf.keras.losses.CategoricalCrossentropy(
from_logits=False,
label_smoothing=0,
reduction="auto",
name="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 in a one_hot representation. If you want to provide labels as integers, please use SparseCategoricalCrossentropy loss. There should be # classes floating point values per feature.
In the snippet below, there is # classes floating pointing values per example. The shape of both y_pred and y_true are [batch_size, num_classes].
Standalone usage:
>>> y_true = [[0, 1, 0], [0, 0, 1]]
>>> y_pred = [[0.05, 0.95, 0], [0.1, 0.8, 0.1]]
>>> # Using 'auto'/'sum_over_batch_size' reduction type.
>>> cce = tf.keras.losses.CategoricalCrossentropy()
>>> cce(y_true, y_pred).numpy()
1.177
>>> # Calling with 'sample_weight'.
>>> cce(y_true, y_pred, sample_weight=tf.constant([0.3, 0.7])).numpy()
0.814
>>> # Using 'sum' reduction type.
>>> cce = tf.keras.losses.CategoricalCrossentropy(
... reduction=tf.keras.losses.Reduction.SUM)
>>> cce(y_true, y_pred).numpy()
2.354
>>> # Using 'none' reduction type.
>>> cce = tf.keras.losses.CategoricalCrossentropy(
... reduction=tf.keras.losses.Reduction.NONE)
>>> cce(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.CategoricalCrossentropy())
SparseCategoricalCrossentropy class
tf.keras.losses.SparseCategoricalCrossentropy(