Troubleshooting NaNs in NN.Sequential with ReLU

I recently encountered a problem while working on a neural network model in PyTorch, where after training on a significant amount of data (about 275,000 iterations out of approximately 300,000), the network suddenly started outputting NaN values. The model includes a fully connected neural network (nn.Sequential) with two hidden layers and a final output layer that uses a softmax function for classification.

Initially, I thought the issue could be related to the input data; however, after verifying that, it appeared normal and well-formed, matching the expected structure perfectly. My network architecture looks something like this:

“`

self.actor = nn.Sequential(

nn.Linear(*input_dims, fc1_dims),

nn.ReLU(),

nn.Linear(fc1_dims, fc2_dims),

nn.ReLU(),

nn.Linear(fc2_dims, n_actions),

nn.Softmax(dim=-1)

)

“`

In this setup, input_dims is the dimensionality of the input data, fc1_dims and fc2_dims are both set to 256 for the dimensions of the first and second fully connected layers, respectively, and n_actions is the number of possible actions or output classes.

One common cause might be related to what is known in the machine learning community as “gradient exploding,” where large error gradients accumulate and result in overly large updates to the weights during training. This can cause the weights to become NaN or Inf, which propagates through the network resulting in NaN outputs.

To address this issue, I considered several steps:

First, I would suggest implementing gradient clipping, a technique used to counter the exploding gradient problem by simply clipping the gradients during backpropagation to ensure they do not exceed a predefined threshold. This can be easily done in PyTorch as follows:

torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1)

Here, max_norm is the maximum norm value for the gradients, which you can adjust based on your specific needs.

Second, if you haven’t already, consider adjusting the learning rate. Sometimes, a too-high learning rate might make the training unstable, leading to issues like this. Reducing the learning rate can sometimes help stabilize the training process.

Third, inspecting the initialization of your network weights might give some clues. If the weights are initialized with excessively high values, it might also lead to exploding gradients. Ensuring that you are using a proper initialization scheme like Xavier or Kaiming initialization might help:

nn.init.xavier_uniform_(self.actor[0].weight)
nn.init.xavier_uniform_(self.actor[2].weight)
nn.init.xavier_uniform_(self.actor[4].weight)

Lastly, if the problem persists, it might be beneficial to add batch normalization layers after each activation layer. This normalizes the output of the activation functions, which can prevent values from becoming too extreme and help stabilize training.

self.actor = nn.Sequential(
    nn.Linear(*input_dims, fc1_dims),
    nn.ReLU(), 
    nn.BatchNorm1d(fc1_dims),
    nn.Linear(fc1_dims, fc2_dims),
    nn.ReLU(),
    nn.BatchNorm1d(fc2_dims),             
    nn.Linear(fc2_dims, n_actions),
    nn.Softmax(dim=-1)
)

Remember, encountering NaNs in network training is not uncommon, especially in complex models or large datasets. It often requires systematic debugging and tweaking of model hyperparameters and configurations. Each of these recommendations should be tested independently to understand its effect, hopefully leading you to a stable and robust training process.