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I have trained 2 neural networks with SGD and then I have taken a linear path between their weights. Say W_0 and W_1 are the weight matrices of network 1 and network 2, respectively.

Then I compute W_new = (1-t)W_0+tW_1 for t ranging from -1 to 2, with small increments within this range. I then compute the loss for W_new at each t value. I get the following loss (it displays the cross-entropy and the accuracy), shown in the image below enter image description here.

As you can see, the local minima of N_0 and N_1 happen at t=0 and t=1 as expected. However, there seem to be 2 local minima at t=-0.5 and t=0.5. I find this very strange and it has occurred over many pairs of neural networks.

This is a simple toy network with one hidden layer with 512 neurons and a relu activation.

Any idea as to why this is happening?

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