How to handle class weight for multiple outputs in keras?

In TensorFlow's Keras API, weighting classes for multiple outputs is not directly supported out of the box. However, you can implement a workaround by using custom loss functions and sample weights.

Here's a detailed solution with examples to handle class weight for multiple outputs:

1. Define the Custom Loss Function:
   • Create a custom loss function that accepts both the true labels and predicted values for multiple outputs.
   • Apply class weights to the loss calculation manually within the custom loss function.

Example:

import tensorflow as tf
 
def weighted_loss(class_weights):
 
    def loss_function(y_true, y_pred):
        # Compute the individual losses for each output
        loss1 = tf.keras.losses.sparse_categorical_crossentropy(y_true[0], y_pred[0])
        loss2 = tf.keras.losses.mean_squared_error(y_true[1], y_pred[1])
 
        # Apply class weights to the losses
        loss1_weighted = tf.reduce_mean(loss1 * class_weights[0])
        loss2_weighted = tf.reduce_mean(loss2 * class_weights[1])
 
        # Return the overall weighted loss
        return loss1_weighted + loss2_weighted
 
    return loss_function

2. Set Class Weights and Sample Weights:
   • Determine the class weights for each output based on the class imbalance.
   • Create sample weights that apply the appropriate class weights to each sample in the dataset.

Example:

import numpy as np
 
# Define the class weights for each output
class_weights = [np.array([0.5, 0.5]), np.array([1.0, 2.0, 3.0])]
 
# Generate sample weights using the class weights and true labels
sample_weights = [class_weights[0][y_true[0]] for y_true in y_train]
 
# Convert sample weights to a TensorFlow tensor
sample_weights = tf.convert_to_tensor(sample_weights, dtype=tf.float32)

3. Compile the Model with the Custom Loss Function and Sample Weights:
   • Use the custom loss function when compiling the model.
   • Provide the sample weights to the sample_weight parameter in the fit method during training.

Example:

# Compile the model with the custom loss function
model.compile(loss=weighted_loss(class_weights), optimizer='adam')
 
# Train the model with the sample weights
model.fit(x_train, [y1_train, y2_train], sample_weight=sample_weights, epochs=10, batch_size=32)

By following these steps, you can effectively apply class weights when working with multiple outputs in TensorFlow's Keras API. The custom loss function allows you to handle the weighting of classes manually, while the sample weights ensure that the appropriate weights are applied during training.