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# MonteCarlo for NN Initialisation Parameters

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## Choosing the Distribution & Parameters for Neural Network Initialisations

When initialising random weights in a neural network, the $n.Var(W) = 1$ 'rule-of-thumb' is fairly easy to find online.

However, these often start to get handwavy after the initial results are proved.

The code below just weights an input distribution of $n$ features (in this case Uniform(0,1)), by a weight vector with distribution Uniform(-0.5*size, +0.5*size), and applies an activation function to the result.

By running this process over a matrix of samples, one can effectively measure the value of size required to equalise the input variance with the output variance. And this approach should give good training stability - since each layer of the network will have an 'amplification factor' of approximately 1.

Doing this in code (essentially by Monte Carlo), makes it easy fiddle with the various input and weight distribution functions, as well as the activation function, without having to make approximations for the sake of algebraic tractability.

## Quick code

import numpy as np

samples = 10000

feat = np.random.random( (samples, features) )

# np.mean(feat, axis=1).shape = 10000

print("Features : Mean=%+.4f, stdev=%.4f" % (np.mean(feat), np.std(feat),))
print("   Ideal : Mean=%+.4f, stdev=%.4f\n" % (0.5, 1.0/np.sqrt(12.0),))

# Set as Uniform(1.0) (centered on zero)
weights = (np.random.random( (samples, features) ) - 0.5) * init_size
print("Weights  : Mean=%+.4f, stdev=%.4f" % (np.mean(weights), np.std(weights),))
print("   Ideal : Mean=%+.4f, stdev=%.4f\n" % (0.0, init_size/np.sqrt(12.0),))

sumprod = np.sum(feat * weights, axis=1)
print("PreAct.shape", sumprod.shape)
print("PreAct   : Mean=%+.4f, stdev=%.4f\n" % (np.mean(sumprod), np.std(sumprod),))

relu = np.maximum( sumprod, np.zeros( sumprod.shape ) )
print("ReLU     : Mean=%+.4f, stdev=%.4f" % (np.mean(relu), np.std(relu),))

logistic = 1. / (1. + np.exp( -sumprod ) )
print("Logistic : Mean=%+.4f, stdev=%.4f" % (np.mean(logistic), np.std(logistic),))

return np.std(logistic) / np.std(feat)

return np.std(relu) / np.std(feat)

if True:
#arr = [ (n, factor(n*n)) for n in range(1,100)]
##  This shows a line from (0,0) to (100,30)  (n, relu.std / feat.std)

#arr = [ (n, factor(n*n, 3./n)) for n in range(1,100,10)]
##  This shows a constant line from (0,1.0) to (100,1.0)  (n, relu.std / feat.std)

arr = [ (n, factor(n*n, 11./n, logistic_answer=True)) for n in range(1,100,10)]
##  This shows a constant line from (0,1.03) to (100,1.03)  (n, logistic.std / logistic.std)

for i,v in arr:
print("%d,%6.4f" % (i, v))