def test_stochasatic_pool_samples():
"""
check if the order of frequency of samples from stochastic max pool
are same as the order of input values.
"""
ds = 3
stride = 3
rng = numpy.random.RandomState(220)
data = rng.uniform(0, 10, size=(1, ds, ds, 1)).astype('float32')
x = theano.tensor.tensor4()
s_max = stochastic_max_pool_c01b(x, (ds, ds), (stride, stride))
f = theano.function([x], s_max, mode=mode_with_gpu)
samples = []
for i in xrange(300):
samples.append(numpy.asarray(f(data))[0, 0, 0, 0])
counts = Counter(samples)
data = data.reshape(ds * ds)
data.sort()
data = data[::-1]
for i in range(len(data) - 1):
assert counts[data[i]] >= counts[data[i + 1]]
def test_stochasatic_pool_samples():
"""
check if the order of frequency of samples from stochastic max pool
are same as the order of input values.
"""
ds = 3
stride = 3
rng = numpy.random.RandomState(220)
data = rng.uniform(0, 10, size=(1, ds, ds, 1)).astype('float32')
x = theano.tensor.tensor4()
s_max = stochastic_max_pool_c01b(x, (ds, ds), (stride, stride))
f = theano.function([x], s_max, mode=mode_with_gpu)
samples = []
for i in xrange(300):
samples.append(numpy.asarray(f(data))[0, 0, 0, 0])
counts = Counter(samples)
data = data.reshape(ds*ds)
data.sort()
data = data[::-1]
for i in range(len(data) - 1):
assert counts[data[i]] >= counts[data[i+1]]