import theano.tensor as T
import numpy
from DL.models.MLP import MLP
from DL.optimizers import optimize
from DL import datasets
from DL.utils import *
import time
# from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams
# hide warnings
import warnings
warnings.simplefilter("ignore")
print "An MLP with dropout on MNIST."
print "loading MNIST"
mnist = datasets.mnist()
print "loading data to the GPU"
dataset = load_data(mnist)
print "creating the MLP"
x = T.matrix('x') # input
t = T.vector('t') # targets
inputs = [x, t]
# cast to an int. needs to be initially a float to load to the GPU
it = t.astype('int64')
rng = numpy.random.RandomState(int(time.time())) # random number generator
# srng = RandomStreams(int(time.time()))
srng = T.shared_randomstreams.RandomStreams(int(time.time()))
import theano.tensor as T
import numpy
from DL.models.MLP import MLP
from DL.optimizers import optimize
from DL import datasets
from DL.utils import *
import time
# hide warnings
import warnings
warnings.simplefilter("ignore")
print "An MLP on MNIST."
print "loading MNIST"
mnist = datasets.mnist()
print "loading data to the GPU"
dataset = load_data(mnist)
print "creating the MLP"
x = T.matrix('x') # input
t = T.vector('t') # targets
inputs = [x, t]
# cast to an int. needs to be initially a float to load to the GPU
it = t.astype('int64')
rng = numpy.random.RandomState(int(time.time())) # random number generator
# construct the MLP class
mlp = MLP(