Interesting question. As @ncasas mentions, for most cases, probably, for all cases, no.
There are many things that impact how fast a network will converge.
- The optimizer and training hyperparameters
Whether you are using SGD, Adam, or another optimizer, it will have a direct impact on convergence speed. These optimizers have hyperparameters including, notably, the learning rate which can make a huge difference.
- The initial state of the network
Needless to say that a pre-trained network might converge faster than a non-converged one. Although small, there is always a probability that a different initialization of the weights can place you closer or further from convergence.
- The architecture of the network itself
A network with
N parameters may be designed in many different ways, with different layer types, the number of layers, and layer sizes. Each architecture will yield different convergence behaviour.
- The difficulty of the problem at hand
What's good to remember is that neural networks typically do not converge to a global optimum but they do have many local optima which you can end up in. Training is essentially trying many combinations of weights and then decide on a configuration that is satisfactory. All of this to say that certain problems may possess more "satisfactory" configurations than others, meaning that some networks might have more ways to converge than others, meaning that they might find one faster.