On Wed, Mar 7, 2018 at 1:10 PM, Marko Asplund <marko.aspl...@gmail.com> wrote: > > However, the results look very different when using random initialization. > With respect to exact cost this is course expected, but what I find troublesome > is that after N training iterations the cost starts approaching zero with the NumPy > code (most of of the time), whereas with the Scala based implementations cost fails > to converge (most of the time). > > With NumPy I'm simply using the following random initilization code: > > np.random.randn(n_h, n_x) * 0.01 > > I'm trying to emulate the same behaviour in my Scala code by sampling from a > Gaussian distribution with mean = 0 and std dev = 1.
`np.random.randn(n_h, n_x) * 0.01` gives a Gaussian distribution of mean=0 and stdev=0.01 -- Robert Kern
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