05_Working_With_CBOW_Embeddings，loss is always nan

[Arnold]

when I run the demo，output is：

print('Loss at step {} : {}'.format(i+1, loss_val))

Loading Data
Normalizing Text Data
Creating Dictionary
Creating Model
Starting Training
Loss at step 100 : nan
Loss at step 200 : nan
Loss at step 300 : nan
Loss at step 400 : nan
Loss at step 500 : nan
Loss at step 600 : nan
Loss at step 700 : nan
Loss at step 800 : nan
Loss at step 900 : nan
Loss at step 1000 : nan

[Nick]

Thanks for bringing this to my attention. I'm currently working on updating this chapter this week. Hopefully a fix will come out soon.

[Nick]

I'm about to push a fix. I have no idea why this is. But if you lower the learning rate to 0.25 it seems to work.

Change:
model_learning_rate = 0.25

You may also consider adding:
tf.set_random_seed(42)

np.random.seed(42)

I got it to run that way. Look for the code update later today.