How do you handle negative outputs in GANs when training on text generation tasks

Question

With the help of Python programming, can you tell me how you handle negative outputs in GANs when training on text generation tasks?

Answer 1

To handle negative outputs in GANs when training on text generation tasks, you can follow the following:

• Use Proper Output Activation: Apply a softmax activation function to ensure outputs are probabilities (non-negative and sum to 1).
• Adjust Loss Function: Use cross-entropy loss suitable for discrete text tokens.
• Gumbel-Softmax Trick: Use the Gumbel-Softmax distribution to approximate discrete token sampling.
• Clip Outputs: Clip values to ensure outputs remain in the valid range.

Here is the code snippet you can refer to:

In the above code, we are using the following key points:

• Softmax/Gumbel-Softmax: Ensures non-negative, normalized outputs for text generation.
• Embedding-to-Probability Mapping: Maps embeddings to vocab probabilities.
• Clip Outputs: If necessary, apply F.relu or clipping to remove negative values.

Hence, these methods ensure the generator's outputs are valid for text-generation tasks.