Understanding tf.nn.nce_loss()

in TensorFlow

In the realm of Natural Language Processing (NLP), word embeddings play a critical role in capturing the semantics of words. One efficient method to train these word embeddings is through the Noise Contrastive Estimation (NCE). TensorFlow provides a built-in function, tf.nn.nce_loss() , that helps in implementing this technique. This article dives into the technical aspects and application examples of tf.nn.nce_loss() , providing a comprehensive understanding of its usage in building models efficiently.

Theoretical Background

Noise Contrastive Estimation is a method used to convert the problem of estimation in neural networks into a binary classification problem. The key idea is to distinguish between samples from the true data distribution and samples from a noise distribution. This is particularly useful in reducing computational overhead in language models, like Word2Vec, by avoiding the calculation over the entire vocabulary.

Objective of NCE

The objective in NCE is to train a model to correctly classify positive samples (from the true distribution) and negative samples (from the noise distribution). The loss function is defined to increase the log-probability of true samples and decrease the log-probability of false samples.

How tf.nn.nce_loss()

Works

tf.nn.nce_loss() function helps in calculating the NCE loss. Below is the syntax for the function:

• **weights **: The weights of the output layer; dimension [num_classes, dim].
• **biases **: The biases of the output layer; dimension [num_classes].
• **labels **: The target output labels; dimension [batch_size, num_true].
• **inputs **: The input embeddings; dimension [batch_size, dim].
• **num_sampled **: The number of negative samples.
• **num_classes **: The number of classes in the dataset.
• **num_true **: The number of target classes per training example, usually set to 1.
• **sampled_values **: Pre-sampled values for negative classes, if available.
• **remove_accidental_hits **: If set to True, "accidental hits" are removed.
• **partition_strategy **: Strategy to partition the computation ("mod" or "div").