def check_the_grad(regression=True,
nos=1,
ind=30,
outd=3,
bxe=False,
eps=1e-8,
verbose=False):
"""
Check gradient computation for Neural Networks.
"""
#
from misc import sigmoid, identy, check_grad
from losses import xe, ssd, mia
# number of input samples (nos)
# with dimension ind each
ins = np.random.randn(nos, ind)
#
structure = dict()
if regression:
# Regression
# Network with one hidden layer
structure["layers"] = [(ind, 15), (15, outd)]
# the last layer also needs to be specified.
# it should be a linear layer (see score functions in losses.py).
structure["activs"] = [np.tanh, identy]
structure["score"] = ssd
outs = np.random.randn(nos, outd)
else:
# Classification
# _outd_ is interpreted as number of classes
structure["layers"] = [(ind, 15), (15, outd)]
# the last layer also needs to be specified.
structure["activs"] = [sigmoid, identy]
if bxe:
structure["score"] = mia
outs = 1. * (np.random.rand(nos, outd) > 0.5)
else:
structure["score"] = xe
outs = np.random.random_integers(outd, size=(nos)) - 1
# weight decay
structure["l2"] = 0.1
weights = init_weights(structure)
cg = dict()
cg["inputs"] = ins
cg["targets"] = outs
cg["structure"] = structure
#
delta = check_grad(score, grad, weights, cg, eps=eps, verbose=verbose)
assert delta < 1e-4, "[nn.py] check_the_grad FAILED. Delta is %f" % delta
return True
def check_the_grad(regression=True, nos=1, ind=30, outd=3, bxe=False, eps=1e-8, verbose=False):
"""
Check gradient computation for Neural Networks.
"""
#
from misc import sigmoid, identy, check_grad
from losses import xe, ssd, mia
# number of input samples (nos)
# with dimension ind each
ins = np.random.randn(nos, ind)
#
structure = dict()
if regression:
# Regression
# Network with one hidden layer
structure["layers"] = [(ind, 15), (15, outd)]
# the last layer also needs to be specified.
# it should be a linear layer (see score functions in losses.py).
structure["activs"] = [np.tanh, identy]
structure["score"] = ssd
outs = np.random.randn(nos, outd)
else:
# Classification
# _outd_ is interpreted as number of classes
structure["layers"] = [(ind, 15), (15, outd)]
# the last layer also needs to be specified.
structure["activs"] = [sigmoid, identy]
if bxe:
structure["score"] = mia
outs = 1.0 * (np.random.rand(nos, outd) > 0.5)
else:
structure["score"] = xe
outs = np.random.random_integers(outd, size=(nos)) - 1
# weight decay
structure["l2"] = 0.1
weights = init_weights(structure)
cg = dict()
cg["inputs"] = ins
cg["targets"] = outs
cg["structure"] = structure
#
delta = check_grad(score, grad, weights, cg, eps=eps, verbose=verbose)
assert delta < 1e-4, "[nn.py] check_the_grad FAILED. Delta is %f" % delta
return True
def check_the_grad(eps=1e-6, verbose=False):
"""
"""
from misc import check_grad
from losses import ssd
structure = {}
structure['layers'] = [(1, 1, 2, 2), (1, 1, 2, 2)]
structure['pools'] = [(2, 2), (2, 2)]
structure['activs'] = [misc.identy, misc.identy]
structure['score'] = ssd
structure['l2'] = 0.
weights = init_weights(structure)
inpts = np.random.randn(2, 1, 7, 7)
trgts = np.random.randn(2).reshape(2, 1)
cg = dict()
cg["inputs"] = inpts
cg["targets"] = trgts
cg["structure"] = structure
#
delta = check_grad(score, grad, weights, cg, eps=eps, verbose=verbose)
def check_the_grad(eps=1e-6, verbose=False):
"""
"""
from misc import check_grad
from losses import ssd
structure = {}
structure["layers"] = [(1, 1, 2, 2), (1, 1, 2, 2)]
structure["pools"] = [(2, 2), (2, 2)]
structure["activs"] = [misc.identy, misc.identy]
structure["score"] = ssd
structure["l2"] = 0.0
weights = init_weights(structure)
inpts = np.random.randn(2, 1, 7, 7)
trgts = np.random.randn(2).reshape(2, 1)
cg = dict()
cg["inputs"] = inpts
cg["targets"] = trgts
cg["structure"] = structure
#
delta = check_grad(score, grad, weights, cg, eps=eps, verbose=verbose)
