What fast loss convergence indicates on a CNN? I'm training two CNNs (AlexNet e GoogLeNet) in two differents DL libraries (Caffe e Tensorflow). The networks was implemented by dev teams of each libraries (here and here)

I reduced the original Imagenet dataset to 1024 images of 1 category -- but setted 1000 categories to classify on the networks.

So I trained the CNNs, varying processing unit (CPU/GPU) and batches sizes, and I observed that the losses converges fastly to near zero (in mostly times before 1 epoch be completed), like in this graph (Alexnet on Tensorflow):



In portuguese, 'Épocas' is epochs and 'Perda' is loss.

The weight decays and initial learning rate are the same as used on models that I downloaded, I only changed the dataset and the batch sizes.

Why my networks are converging this way, and not like this way?
Ok, so the fact that you always give the same category means the network only has to output the same value for any given input. This is exceedingly easy to learn (all the weights always shift in the same directions) and this make this graph. 

Once you have more categories (try 10 for example), you'll see the graph will be closer to expected.