/ reduction**2),
subsample=(reduction, reduction))
x = x.flatten(ndim=2)
input_dim = (28 / reduction)**2
# input, hidden and output dims
hidden_dims = 32 * [32]
dims = [input_dim, hidden_dims[0]] + hidden_dims + [n_outputs]
# allocate parameters
fs = [activation.tanh for _ in dims[1:-1]] + [activation.logsoftmax]
# layer input means
cs = [util.shared_floatx((m,), initialization.constant(0))
for m in dims[:-1]]
# layer input whitening matrices
Us = [util.shared_floatx((m, m), initialization.identity())
for m in dims[:-1]]
# weight matrices
Ws = [util.shared_floatx((m, n), initialization.orthogonal())
for m, n in util.safezip(dims[:-1], dims[1:])]
# batch normalization diagonal scales
gammas = [util.shared_floatx((n, ), initialization.constant(1))
for n in dims[1:]]
# biases or betas
bs = [util.shared_floatx((n, ), initialization.constant(0))
for n in dims[1:]]
# reparametrization updates
updates = []
# theano graphs with assertions & breakpoints, to be evaluated after
# performing the updates
import theano
import theano.tensor as T
import util, activation, initialization, steprules, whitening, mnist
learning_rate = 1e-3
# use batch normalization in addition to PRONG (i.e. PRONG+)
batch_normalize = False
data = mnist.get_data()
n_outputs = 10
dims = [784, 500, 300, 100, n_outputs]
layers = [
dict(f=activation.tanh,
c=util.shared_floatx((m,), initialization.constant(0)), # input mean
U=util.shared_floatx((m, m), initialization.identity()), # input whitening matrix
W=util.shared_floatx((m, n), initialization.orthogonal()), # weight matrix
g=util.shared_floatx((n,), initialization.constant(1)), # gammas (for batch normalization)
b=util.shared_floatx((n,), initialization.constant(0))) # bias
for m, n in util.safezip(dims[:-1], dims[1:])]
layers[-1]["f"] = activation.logsoftmax
features, targets = T.matrix("features"), T.ivector("targets")
#theano.config.compute_test_value = "warn"
#features.tag.test_value = data["valid"]["features"][:11]
#targets.tag.test_value = data["valid"]["targets"][:11]
# reparametrization updates
reparameterization_updates = []
# theano graphs with assertions & breakpoints, to be evaluated after