How to choose cross-entropy loss in TensorFlow?

Choosing the appropriate cross-entropy loss function in TensorFlow primarily depends on two factors: the type of output classes (binary or multi-class classification) and the format of the labels (whether they are one-hot encoded). Below are several common scenarios and how to select the suitable cross-entropy loss function:

1. Binary Classification

For binary classification problems, use tf.keras.losses.BinaryCrossentropy. This loss function is suitable when each class has a single probability prediction. There are two scenarios:

• Labels are not one-hot encoded (i.e., labels are directly 0 or 1):

  python
  

loss = tf.keras.losses.BinaryCrossentropy(from_logits=False)

shell
If the model output has not been processed by an activation function (e.g., Sigmoid), meaning it outputs logits, set `from_logits=True`.

- **Labels are one-hot encoded**:
```python
loss = tf.keras.losses.CategoricalCrossentropy(from_logits=False)

For binary classification with one-hot encoded labels, use CategoricalCrossentropy, and ensure the model output has been processed by a Sigmoid or Softmax activation function.

2. Multi-class Classification

For multi-class classification problems, use tf.keras.losses.CategoricalCrossentropy or tf.keras.losses.SparseCategoricalCrossentropy depending on the label format:

• Labels are one-hot encoded:

  python
  

loss = tf.keras.losses.CategoricalCrossentropy(from_logits=False)

shell
If the model output is logits (i.e., not processed by Softmax), set `from_logits=True`.

- **Labels are not one-hot encoded**:
```python
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False)

For cases where labels are direct class indices (e.g., 0, 1, 2), use SparseCategoricalCrossentropy. Similarly, if the output is logits, set from_logits=True.

Example

Suppose we have a multi-class classification problem where the model's task is to select the correct class from three categories, and the labels are not one-hot encoded:

python
import tensorflow as tf

# Simulate some data
num_samples = 1000
num_classes = 3

inputs = tf.random.normal((num_samples, 20))
labels = tf.random.uniform((num_samples,), minval=0, maxval=num_classes, dtype=tf.int32)

# Build model
model = tf.keras.Sequential([
    tf.keras.layers.Dense(64, activation='relu'),
    tf.keras.layers.Dense(num_classes)  # Note: no activation function
])

# Compile model, choose loss function
model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

# Train model
model.fit(inputs, labels, epochs=10)

In this example, we use SparseCategoricalCrossentropy with from_logits=True because the model output has not been processed by Softmax. This is a common practice when handling multi-class classification problems.