tf.nn.sigmoid_cross_entropy_with_logits是否共享权重？能否用于N个独立二分类模型？

Hey there! Let's break down your two questions about tf.nn.sigmoid_cross_entropy_with_logits and building independent binary classification models:

1. Does tf.nn.sigmoid_cross_entropy_with_logits involve weight sharing? #

Short answer: No, it doesn't.

This function is purely a loss calculation utility—it takes your model's raw logits and ground-truth labels, then computes the sigmoid cross-entropy loss. It has no awareness or control over the weights in your model. Weight sharing (or lack thereof) is entirely determined by how you define your model's trainable parameters (like dense layers, convolutional layers, etc.). The loss function only quantifies how wrong your model's predictions are, regardless of your weight structure.

2. Can I use this function to build N independent binary classification models with no shared weights? #

Absolutely! This is totally feasible, and tf.nn.sigmoid_cross_entropy_with_logits works perfectly for this scenario. The key is to define separate, independent sets of weights for each of your N models—each model will generate its own logits, and you'll compute the loss for each model individually using this function.

Here's a practical TensorFlow 2.x example to illustrate this:

import tensorflow as tf

# Configuration
input_feature_dim = 16
num_independent_models = 4
batch_size = 32

# Create N independent dense layers (each has unique weights/kernel + bias)
independent_layers = [tf.keras.layers.Dense(1, activation=None) for _ in range(num_independent_models)]

# Sample input data
inputs = tf.random.normal((batch_size, input_feature_dim))

# Calculate loss for each independent model
total_loss = 0.0
for idx in range(num_independent_models):
    # Get logits from the current model's layer
    logits = independent_layers[idx](inputs)
    # Sample labels for this specific binary classification task
    task_labels = tf.random.uniform((batch_size, 1), minval=0, maxval=2, dtype=tf.int32)
    # Compute loss for this model
    task_loss = tf.nn.sigmoid_cross_entropy_with_logits(
        labels=tf.cast(task_labels, tf.float32),
        logits=logits
    )
    total_loss += tf.reduce_mean(task_loss)

# Example training step
optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)
with tf.GradientTape() as tape:
    # Recompute loss within the tape to track gradients
    total_loss = 0.0
    for idx in range(num_independent_models):
        logits = independent_layers[idx](inputs)
        task_labels = tf.random.uniform((batch_size, 1), minval=0, maxval=2, dtype=tf.int32)
        task_loss = tf.nn.sigmoid_cross_entropy_with_logits(
            labels=tf.cast(task_labels, tf.float32),
            logits=logits
        )
        total_loss += tf.reduce_mean(task_loss)

# Get gradients for each model's weights and apply updates
gradients = tape.gradient(total_loss, [layer.trainable_variables for layer in independent_layers])
for idx in range(num_independent_models):
    optimizer.apply_gradients(zip(gradients[idx], independent_layers[idx].trainable_variables))

In this code:

• Each Dense layer represents a separate binary classification model, with its own unique trainable weights (no sharing at all).
• We compute the sigmoid cross-entropy loss for each model individually using tf.nn.sigmoid_cross_entropy_with_logits.
• You can either sum all losses for joint training (as shown) or optimize each model's loss separately if needed.

内容的提问来源于stack exchange，提问作者Ravikrn