# How to explain peak in training history of a convolutional neural network?

I am training a simple convolutional neural network to recognize two types of 1024-point frequency spectra (FFT). This is the model I'm using:

cnn = Sequential()


However I get the following accuracy and loss during training:

Why do I get the large peak in both plots? How can it be explained? Is there a problem with the data I'm using (I mention that I obtain a similar peak when training an autoencoder for denoising using the same data)?

• Are you getting it multiple times assuming you have randomly initialized weights Everytime or is it a one time thing?
– user9947
Dec 24 '19 at 13:12
• @DuttaA I get it multiple times. I'm using Tensorflow to train the model with default weight initializers for each layer, so it automatically randomizes the weights. I mention that I set the seed values for numpy and tensorflow at the beginning of the script to get a more deterministic output. I've changed the seed value and I get a smaller peak at a different epoch. What do you think it is? Dec 24 '19 at 13:36