TensorFlow代码运行时Train cost持续输出NaN问题求助

Hey there, let's break down why you're seeing all NaN values for your train cost every 100 steps in the AT-LSTM code. I've run into similar numerical stability issues with sequence models for POI recommendation before, so here are the most likely causes to check:

• Gradient Explosion/Numerical Overflow
  LSTMs can easily suffer from gradient explosion, especially when dealing with long user behavior sequences or sparse POI features. If gradients get too large, weight updates can push values to infinity, which then turn into NaNs in subsequent calculations.

  • Quick checks: Does the code include gradient clipping? If not, add a clip operation (like tf.clip_by_norm for TensorFlow or torch.nn.utils.clip_grad_norm_ for PyTorch) before updating weights. Also, try lowering your learning rate—even cutting it to 1/10 of the current value can often fix this.

• Invalid or Missing Values in Input Data
  POI datasets often have messy data: missing coordinates, empty user sequences, or invalid POI IDs that weren't filtered during preprocessing. These anomalies can propagate through the model and result in NaN losses.

  • Quick checks: Trace back your data loading/preprocessing steps. Print a batch of input sequences and labels to look for NaNs, infinite values, or out-of-range POI IDs. Make sure you're filtering out sequences shorter than 2 (since you need at least one historical POI to predict the next one).

• Loss Function Edge Cases
  For multi-class POI recommendation, cross-entropy loss is common—but if your model outputs a probability of 0 or 1, taking the logarithm will produce infinity, which turns into NaN. This happens more often when dealing with a huge number of POI categories.

  • Quick checks: Look at your loss calculation code. Add a small epsilon (like 1e-8) to the model's predictions before taking the log—e.g., change tf.log(pred) to tf.log(pred + 1e-8). Also confirm that all labels are valid category IDs that exist in your model's output layer.

• Poor Weight Initialization
  If your LSTM or attention layer weights are initialized to values that are too large, the model's initial calculations can become numerically unstable, leading to NaNs right from the start.

  • Quick checks: Review the weight initialization logic for your model layers. Switch to more stable methods like Xavier or He initialization if you're using custom initializers, and try scaling down initial weight values if they're set manually.

• Malformed Batch Sequences
  POI sequences vary a lot in length, and improper padding or handling of short sequences can break LSTM computations. For example, if a batch contains sequences with all padding tokens, the model might produce meaningless outputs that lead to NaN losses.

  • Quick checks: Verify your sequence padding code—are you using a valid padding token that's excluded from loss calculations? Print the length of sequences in each batch to ensure there are no zero-length entries slipping through.

内容的提问来源于stack exchange，提问作者drorhun