**What is the Difference Between `tf.nn.softmax` and `tf.nn.softmax_cross_entropy_with_logits` in TensorFlow?**

Understanding Logits in TensorFlow

In TensorFlow's API documentation, the term "logits" is frequently encountered. Logits refer to unscaled activation values produced by neural network layers. They are interpreted as log-probabilities before being converted into probabilities using the softmax function.

Distinction between tf.nn.softmax and tf.nn.softmax_cross_entropy_with_logits

tf.nn.softmax

This function applies the softmax function element-wise to an input tensor. Softmax normalizes the input values so that they sum to 1, making them suitable for representing probabilities. The shape of the output remains the same as the input.

tf.nn.softmax_cross_entropy_with_logits

This function combines the softmax operation with the calculation of cross-entropy loss. It internally performs the softmax transformation and then computes the cross-entropy between the predicted probabilities and the true labels. The output is a summary metric with shape [batch_size, 1].

Key Difference

tf.nn.softmax_cross_entropy_with_logits is designed to calculate both softmax and cross-entropy loss in a single step. It handles numerical stability issues more effectively than manually applying softmax followed by cross-entropy calculation.

When to use tf.nn.softmax_cross_entropy_with_logits

• When performing classification tasks where predicting probabilities is needed.
• When minimizing cross-entropy as the loss function and softmax is used in the final layer.
• When working with single-class labels, tf.nn.sparse_softmax_cross_entropy_with_logits is recommended.

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