def test_pooling(backend_cpu64, poolargs):
nin, nifm, fshape, batch_size, op = poolargs
NervanaObject.be.bsz = NervanaObject.be.batch_size = batch_size
sz = nin * nin * nifm * batch_size
epsilon = 1.0e-5
# make sure perturnationc an never change the max element
inp = np.arange(sz) * 2.5 * epsilon
# shuffle
np.random.shuffle(inp)
inp = inp.reshape((nin * nin * nifm, batch_size))
lshape = (nifm, nin, nin)
layer = PoolingWithReset(fshape)
layer.op = op
pert_frac = 0.1 # test 10% of the inputs
# select pert_frac fraction of inps to perturb
pert_cnt = int(np.ceil(inp.size * pert_frac))
pert_inds = np.random.permutation(inp.size)[0:pert_cnt]
(max_abs, max_rel) = general_gradient_comp(layer,
inp,
epsilon=epsilon,
lshape=lshape,
pert_inds=pert_inds)
assert max_abs < 1.0e-7
def test_batchnorm(backend_cpu64, bnargs):
n, batch_size = bnargs
NervanaObject.be.bsz = NervanaObject.be.batch_size = batch_size
layer = BatchNormWithReset()
inp_shape = None
inp_size = n
if isinstance(n, tuple):
inp_shape = n
from operator import mul
inp_size = reduce(mul, n)
inp = np.random.randn(inp_size, batch_size)
epsilon = 1.0e-5
pert_frac = 0.1 # test 10% of the inputs
# select pert_frac fraction of inps to perturb
pert_cnt = int(np.ceil(inp.size*pert_frac))
pert_inds = np.random.permutation(inp.size)[0:pert_cnt]
(max_abs, max_rel) = general_gradient_comp(layer,
inp,
epsilon=epsilon,
lshape=inp_shape,
pert_inds=pert_inds)
assert max_abs < 1.0e-7
def test_pooling(backend_cpu64, poolargs):
nin, nifm, fshape, batch_size, op = poolargs
NervanaObject.be.bsz = NervanaObject.be.batch_size = batch_size
sz = nin * nin * nifm * batch_size
epsilon = 1.0e-5
# make sure perturnationc an never change the max element
inp = np.arange(sz)*2.5*epsilon
# shuffle
np.random.shuffle(inp)
inp = inp.reshape((nin * nin * nifm, batch_size))
lshape = (nifm, nin, nin)
layer = PoolingWithReset(fshape, op=op)
pert_frac = 0.1 # test 10% of the inputs
# select pert_frac fraction of inps to perturb
pert_cnt = int(np.ceil(inp.size*pert_frac))
pert_inds = np.random.permutation(inp.size)[0:pert_cnt]
(max_abs, max_rel) = general_gradient_comp(layer,
inp,
epsilon=epsilon,
lshape=lshape,
pert_inds=pert_inds)
assert max_abs < 1.0e-7
def test_batchnorm(cpu64_only, bnargs):
n, batch_size = bnargs
NervanaObject.be.bsz = NervanaObject.be.batch_size = batch_size
layer = BatchNormWithReset()
inp_shape = None
inp_size = n
if isinstance(n, tuple):
inp_shape = n
from operator import mul
inp_size = reduce(mul, n)
inp = np.random.randn(inp_size, batch_size)
epsilon = 1.0e-5
pert_frac = 0.1 # test 10% of the inputs
# select pert_frac fraction of inps to perturb
pert_cnt = int(np.ceil(inp.size * pert_frac))
pert_inds = np.random.permutation(inp.size)[0:pert_cnt]
(max_abs, max_rel) = general_gradient_comp(layer,
inp,
epsilon=epsilon,
lshape=inp_shape,
pert_inds=pert_inds)
assert max_abs < 1.0e-7
def test_pooling(cpu64_only, poolargs):
nin, nifm, fshape, batch_size, op = poolargs
NervanaObject.be.bsz = NervanaObject.be.batch_size = batch_size
inp = np.random.randn(nin * nin * nifm, batch_size)
lshape = (nifm, nin, nin)
layer = PoolingWithReset(fshape)
layer.op = op
epsilon = 1.0e-5
pert_frac = 0.1 # test 10% of the inputs
# select pert_frac fraction of inps to perturb
pert_cnt = int(np.ceil(inp.size * pert_frac))
pert_inds = np.random.permutation(inp.size)[0:pert_cnt]
(max_abs, max_rel) = general_gradient_comp(layer, inp, epsilon=epsilon, lshape=lshape, pert_inds=pert_inds)
assert max_abs < 1.0e-7
def test_bias(backend_cpu64, biasargs):
n, batch_size = biasargs
NervanaObject.be.bsz = NervanaObject.be.batch_size = batch_size
init = Gaussian()
layer = BiasWithReset(init=init)
inp = np.random.randn(n, batch_size)
epsilon = 1.0e-5
pert_frac = 0.1 # test 10% of the inputs
# select pert_frac fraction of inps to perturb
pert_cnt = int(np.ceil(inp.size*pert_frac))
pert_inds = np.random.permutation(inp.size)[0:pert_cnt]
(max_abs, max_rel) = general_gradient_comp(layer,
inp,
epsilon=epsilon,
lshape=inp.shape,
pert_inds=pert_inds)
assert max_abs < 1.0e-7
def test_bias(cpu64_only, biasargs):
n, batch_size = biasargs
NervanaObject.be.bsz = NervanaObject.be.batch_size = batch_size
init = Gaussian()
layer = BiasWithReset(init=init)
inp = np.random.randn(n, batch_size)
epsilon = 1.0e-5
pert_frac = 0.1 # test 10% of the inputs
# select pert_frac fraction of inps to perturb
pert_cnt = int(np.ceil(inp.size*pert_frac))
pert_inds = np.random.permutation(inp.size)[0:pert_cnt]
(max_abs, max_rel) = general_gradient_comp(layer,
inp,
epsilon=epsilon,
lshape=inp.shape,
pert_inds=pert_inds)
assert max_abs < 1.0e-7
def test_pooling(cpu64_only, poolargs):
nin, nifm, fshape, batch_size, op = poolargs
NervanaObject.be.bsz = NervanaObject.be.batch_size = batch_size
inp = np.random.randn(nin * nin * nifm, batch_size)
lshape = (nifm, nin, nin)
layer = PoolingWithReset(fshape)
layer.op = op
epsilon = 1.0e-5
pert_frac = 0.1 # test 10% of the inputs
# select pert_frac fraction of inps to perturb
pert_cnt = int(np.ceil(inp.size * pert_frac))
pert_inds = np.random.permutation(inp.size)[0:pert_cnt]
(max_abs, max_rel) = general_gradient_comp(layer,
inp,
epsilon=epsilon,
lshape=lshape,
pert_inds=pert_inds)
assert max_abs < 1.0e-7
def test_mlp(backend_cpu64, mlpargs):
nin, nout, batch_size = mlpargs
# run the gradient check on an mlp
batch_size = batch_size
NervanaObject.be.bsz = NervanaObject.be.batch_size = batch_size
init = Gaussian()
layer = LinearWithReset(nout=nout, init=init)
inp = np.random.randn(nin, batch_size)
epsilon = 1.0e-5
pert_frac = 0.1 # test 10% of the inputs
# select pert_frac fraction of inps to perturb
pert_cnt = int(np.ceil(inp.size*pert_frac))
pert_inds = np.random.permutation(inp.size)[0:pert_cnt]
(max_abs, max_rel) = general_gradient_comp(layer,
inp,
epsilon=epsilon,
pert_inds=pert_inds)
assert max_abs < 1.0e-7
