NaN loss when training regression network

Solution 1:

Regression with neural networks is hard to get working because the output is unbounded, so you are especially prone to the exploding gradients problem (the likely cause of the nans).

Historically, one key solution to exploding gradients was to reduce the learning rate, but with the advent of per-parameter adaptive learning rate algorithms like Adam, you no longer need to set a learning rate to get good performance. There is very little reason to use SGD with momentum anymore unless you're a neural network fiend and know how to tune the learning schedule.

Here are some things you could potentially try:

1. Normalize your outputs by quantile normalizing or z scoring. To be rigorous, compute this transformation on the training data, not on the entire dataset. For example, with quantile normalization, if an example is in the 60th percentile of the training set, it gets a value of 0.6. (You can also shift the quantile normalized values down by 0.5 so that the 0th percentile is -0.5 and the 100th percentile is +0.5).

2. Add regularization, either by increasing the dropout rate or adding L1 and L2 penalties to the weights. L1 regularization is analogous to feature selection, and since you said that reducing the number of features to 5 gives good performance, L1 may also.

3. If these still don't help, reduce the size of your network. This is not always the best idea since it can harm performance, but in your case you have a large number of first-layer neurons (1024) relative to input features (35) so it may help.

4. Increase the batch size from 32 to 128. 128 is fairly standard and could potentially increase the stability of the optimization.

Solution 2:

The answer by 1" is quite good. However, all of the fixes seems to fix the issue indirectly rather than directly. I would recommend using gradient clipping, which will clip any gradients that are above a certain value.

In Keras you can use clipnorm=1 (see https://keras.io/optimizers/) to simply clip all gradients with a norm above 1.

Solution 3:

I faced the same problem before. I search and find this question and answers. All those tricks mentioned above are important for training a deep neural network. I tried them all, but still got NAN.

I also find this question here. https://github.com/fchollet/keras/issues/2134. I cited the author's summary as follows：

I wanted to point this out so that it's archived for others who may experience this problem in future. I was running into my loss function suddenly returning a nan after it go so far into the training process. I checked the relus, the optimizer, the loss function, my dropout in accordance with the relus, the size of my network and the shape of the network. I was still getting loss that eventually turned into a nan and I was getting quite fustrated.

Then it dawned on me. I may have some bad input. It turns out, one of the images that I was handing to my CNN (and doing mean normalization on) was nothing but 0's. I wasn't checking for this case when I subtracted the mean and normalized by the std deviation and thus I ended up with an exemplar matrix which was nothing but nan's. Once I fixed my normalization function, my network now trains perfectly.

I agree with the above viewpoint: the input is sensitive for your network. In my case, I use the log value of density estimation as an input. The absolute value could be very huge, which may result in NaN after several steps of gradients. I think the input check is necessary. First, you should make sure the input does not include -inf or inf, or some extremely large numbers in absolute value.

Solution 4:

I faced the same problem with using LSTM, the problem is my data has some nan value after standardization, therefore, we should check the input model data after the standarization if you see you will have nan value:

print(np.any(np.isnan(X_test)))
print(np.any(np.isnan(y_test)))

you can solve this by adding a small value(0.000001) to Std like this,

def standardize(train, test):


    mean = np.mean(train, axis=0)
    std = np.std(train, axis=0)+0.000001

    X_train = (train - mean) / std
    X_test = (test - mean) /std
    return X_train, X_test

Solution 5:

I faced a very similar problem, and this is how I got it to run.

The first thing you can try is changing your activation to LeakyReLU instead of using Relu or Tanh. The reason is that often, many of the nodes within your layers have an activation of zero, and backpropogation doesn't update the weights for these nodes because their gradient is also zero. This is also called the 'dying ReLU' problem (you can read more about it here: https://datascience.stackexchange.com/questions/5706/what-is-the-dying-relu-problem-in-neural-networks).

To do this, you can import the LeakyReLU activation using:

from keras.layers.advanced_activations import LeakyReLU

and incorporate it within your layers like this:

model.add(Dense(800,input_shape=(num_inputs,)))
model.add(LeakyReLU(alpha=0.1))

Additionally, it is possible that the output feature (the continuous variable you are trying to predict) is an imbalanced data set and has too many 0s. One way to fix this issue is to use smoothing. You can do this by adding 1 to the numerator of all your values in this column and dividing each of the values in this column by 1/(average of all the values in this column)

This essentially shifts all the values from 0 to a value greater than 0 (which may still be very small). This prevents the curve from predicting 0s and minimizing the loss (eventually making it NaN). Smaller values are more greatly impacted than larger values, but on the whole, the average of the data set remains the same.