Network Generality, Training Required, and Precision Required

Part of Neural Information Processing Systems 0 (NIPS 1987)

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John Denker, Ben Wittner


We show how to estimate (1) the number of functions that can be implemented by a particular network architecture, (2) how much analog precision is needed in the con(cid:173) nections in the network, and (3) the number of training examples the network must see before it can be expected to form reliable generalizations.

Generality versus Training Data Required

Consider the following objectives: First, the network should be very powerful and ver(cid:173) satile, i.e., it should implement any function (truth table) you like, and secondly, it should learn easily, forming meaningful generalizations from a small number of training examples. Well, it is information-theoretically impossible to create such a network. We will present here a simplified argument; a more complete and sophisticated version can be found in Denker et al. (1987).

It is customary to regard learning as a dynamical process: adjusting the weights (etc.) in a single network. In order to derive the results of this paper, however, we take a different viewpoint, which we call the ensemble viewpoint. Imagine making a very large number of replicas of the network. Each replica has the same architecture as the original, but the weights are set differently in each case. No further adjustment takes place; the "learning process" consists of winnowing the ensemble of replicas, searching for the one( s) that satisfy our requirements.

Training proceeds as follows: We present each item in the training set to every network in the ensemble. That is, we use the abscissa of the training pattern as input to the network, and compare the ordinate of the training pattern to see if it agrees with the actual output of the network. For each network, we keep a score reflecting how many times (and how badly) it disagreed with a training item. Networks with the lowest score are the ones that agree best with the training data. If we had complete confidence in

lCurrently at NYNEX Science and Technology, 500 Westchester Ave., White Plains, NY 10604

@) American Institute of Physics 1988


the reliability of the training set, we could at each step simply throwaway all networks that disagree.

For definiteness, let us consider a typical network architecture, with No input wires and Nt units in each processing layer I, for I E {I·· ·L}. For simplicity we assume NL = 1. We recognize the importance of networks with continuous-valued inputs and outputs, but we will concentrate for now on training (and testing) patterns that are discrete, with N == No bits of abscissa and N L = 1 bit of ordinate. This allows us to classify the networks into bins according to what Boolean input-output relation they implement, and simply consider the ensemble of bins.

If the network architecture is completely general and There are 22N jossible bins. powerful, all 22 functions will exist in the ensemble of bins. On average, one expects that each training item will throwaway at most half of the bins. Assuming maximal efficiency, if m training items are used, then when m ~ 2N there will be only one bin remaining, and that must be the unique function that consistently describes all the data. But there are only 2N possible abscissas using N bits. Therefore a truly general network cannot possibly exhibit meaningful generalization - 100% of the possible data is needed for training.

N ow suppose that the network is not completely general, so that even with all possible settings of the weights we can only create functions in 250 bins, where So < 2N. We call So the initial entropy of the network. A more formal and general definition is given in Denker et al. (1987). Once again, we can use the training data to winnow the ensemble, and when m ~ So, there will be only one remaining bin. That function will presumably generalize correctly to the remaining 2N - m possible patterns. Certainly that function is the best we can do with the network architecture and the training data we were given.

The usual problem with automatic learning is this: If the network is too general, So will be large, and an inordinate amount of training data will be required. The required amount of data may be simply unavailable, or it may be so large that training would be prohibitively time-consuming. The shows the critical importance of building a network that is not more general than necessary.

Estimating the Entropy

In real engineering situations, it is important to be able to estimate the initial entropy of various proposed designs, since that determines the amount of training data that will be required. Calculating So directly from the definition is prohibitively difficult, but we can use the definition to derive useful approximate expressions. (You wouldn't want to calculate the thermodynamic entropy of a bucket of water directly from the definition, either. )


Suppose that the weights in the network at each connection i were not continuously adjustable real numbers, but rather were specified by a discrete code with bi bits. Then the total number of bits required to specify the configuration of the network is


Now the total number offunctions that could possibly be implemented by such a network architecture would be at most 2B. The actual number will always be smaller than this, since there are various ways in which different settings of the weights can lead to identical functions (bins). For one thing, for each hidden layer 1 E {1··· L-1}, the numbering of the hidden units can be permuted, and the polarity of the hidden units can be flipped, which means that 250 is less than 2B by a factor (among others) of III Nl! 2N ,. In addition, if there is an inordinately large number of bits bi at each connection, there will be many settings where small changes in the connection will be immaterial. This will make 2so smaller by an additional factor. We expect aSO/abi ~ 1 when bi is small, and aSO/abi ~ 0 when bi is large; we must now figure out where the crossover occurs.

The number of "useful and significant" bits of precision, which we designate b, typically scales like the logarithm of number of connections to the unit in question. This can be understood as follows: suppose there are N connections into a given unit, and an input signal to that unit of some size A is observed to be significant (the exact value of A drops out of the present calculation). Then there is no point in having a weight with magnitude much larger than A, nor much smaller than A/N. That is, the dynamic range should be comparable to the number of connections. (This argument is not exact, and it is easy to devise exceptions, but the conclusion remains useful.) If only a fraction 1/ S of the units in the previous layer are active (nonzero) at a time, the needed dynamic range is reduced. This implies b ~ log(N/S).

Note: our calculation does not involve the dynamics of the learning process. Some numerical methods (including versions of back propagation) commonly require a number of temporary "guard bits" on each weight, as pointed out by llichard Durbin (private communication). Another log N bits ought to suffice. These bits are not needed after learning is complete, and do not contribute to So.

If we combine these ideas and apply them to a network with N units in each layer, fully connected, we arrive at the following expression for the number of different Boolean functions that can be implemented by such a network: