def train1(self, data, classes, verbose=0):
n = len(data)
testing = array(selection(xrange(n), n / 10), 'i')
training = setdiff1d(array(xrange(n), 'i'), testing)
testset = data[testing, :]
testclasses = classes[testing]
ntrain = min(self.initial_epochs * n, self.initial_ntrain)
pool = []
for nh in self.initial_nhidden:
mlp = MLP(**self.kw)
mlp.eta = log_uniform(*self.initial_eta)
mlp.train(data,
classes,
etas=[(mlp.eta, ntrain)],
nhidden=nh,
verbose=0,
samples=training)
mlp.err = error(mlp, testset, testclasses)
if verbose: print "AutoMLP initial","%.3f"%mlp.eta,nh,\
mlp.err,"%.4f"%(mlp.err*1.0/len(testset))
pool.append(mlp)
for i in range(self.max_rounds):
# if the pool is too large, pick only the best models
errs = [x.err + 0.1 * x.nhidden() for x in pool]
if len(errs) > self.max_pool:
choice = argsort(errs)
pool = list(take(pool, choice[:self.max_pool]))
# pick a random model from the pool
mlp = selection(pool, 1)[0]
mlp = mlp.copy()
# compute random learning rates and number of hidden units
new_eta = exp(log(mlp.eta) + randn() * self.log_eta_var)
new_nh = max(
2, int(exp(log(mlp.nhidden()) + randn() * self.log_nh_var)))
# train with the new parameters
mlp.eta = new_eta
mlp.changeHidden(data, classes, new_nh)
mlp.train(data,
classes,
etas=[(mlp.eta, ntrain)],
verbose=(self.verbose > 1),
samples=training)
# determine error on test set
mlp.err = error(mlp, testset, testclasses)
if verbose:
print "AutoMLP pool",mlp.err,"%.4f"%(mlp.err*1.0/len(testset)),\
"(%.3f,%d)"%(mlp.eta,mlp.nhidden()),\
[x.err for x in pool]
pool += [mlp]
# to allow partial training, update this with the best model so far
best = argmin([x.err + 0.1 * x.nhidden() for x in pool])
mlp = pool[best]
self.assign(mlp)
yield Record(round=i,
rounds=self.max_rounds,
testerr=mlp.err * 1.0 / len(testset))
def train1(self,data,classes,verbose=0):
n = len(data)
testing = array(selection(range(n),n/10),'i')
training = setdiff1d(array(range(n),'i'),testing)
testset = data[testing,:]
testclasses = classes[testing]
ntrain = min(self.initial_epochs*n,self.initial_ntrain)
pool = []
for nh in self.initial_nhidden:
mlp = MLP(**self.kw)
mlp.eta = log_uniform(*self.initial_eta)
mlp.train(data,classes,etas=[(mlp.eta,ntrain)],
nhidden=nh,
verbose=0,
samples=training)
mlp.err = error(mlp,testset,testclasses)
if verbose: print("AutoMLP initial","%.3f"%mlp.eta,nh,\
mlp.err,"%.4f"%(mlp.err*1.0/len(testset)))
pool.append(mlp)
for i in range(self.max_rounds):
# if the pool is too large, pick only the best models
errs = [x.err+0.1*x.nhidden() for x in pool]
if len(errs)>self.max_pool:
choice = argsort(errs)
pool = list(take(pool,choice[:self.max_pool]))
# pick a random model from the pool
mlp = selection(pool,1)[0]
mlp = mlp.copy()
# compute random learning rates and number of hidden units
new_eta = exp(log(mlp.eta)+randn()*self.log_eta_var)
new_nh = max(2,int(exp(log(mlp.nhidden())+randn()*self.log_nh_var)))
# train with the new parameters
mlp.eta = new_eta
mlp.changeHidden(data,classes,new_nh)
mlp.train(data,classes,etas=[(mlp.eta,ntrain)],
verbose=(self.verbose>1),samples=training)
# determine error on test set
mlp.err = error(mlp,testset,testclasses)
if verbose:
print("AutoMLP pool",mlp.err,"%.4f"%(mlp.err*1.0/len(testset)),\
"(%.3f,%d)"%(mlp.eta,mlp.nhidden()),\
[x.err for x in pool])
pool += [mlp]
# to allow partial training, update this with the best model so far
best = argmin([x.err+0.1*x.nhidden() for x in pool])
mlp = pool[best]
self.assign(mlp)
yield Record(round=i,rounds=self.max_rounds,testerr=mlp.err*1.0/len(testset))
def decreaseHidden(self, data, cls, new_nhidden):
"""Decrease the number of hidden units. Data and cls might be used to
pick which hidden units to delete (but currently are unused)."""
ninput, nhidden, noutput = self.shape()
keep = array([True] * nhidden)
for i in selection(xrange(nhidden), nhidden - new_nhidden):
keep[i] = False
self.w1 = array(self.w1[keep, :], dtype="f", order="C")
self.b1 = array(self.b1[keep], dtype="f", order="C")
self.w2 = array(self.w2[:, keep], dtype="f", order="C")
def decreaseHidden(self, data, cls, new_nhidden):
"""Decrease the number of hidden units. Data and cls might be used to
pick which hidden units to delete (but currently are unused)."""
ninput, nhidden, noutput = self.shape()
keep = array([True] * nhidden)
for i in selection(xrange(nhidden), nhidden - new_nhidden):
keep[i] = False
self.w1 = array(self.w1[keep, :], dtype='f', order="C")
self.b1 = array(self.b1[keep], dtype='f', order="C")
self.w2 = array(self.w2[:, keep], dtype='f', order="C")
def init(self, data, cls, nhidden=None, eps=1e-2):
data = data.reshape(len(data), prod(data.shape[1:]))
scale = max(abs(amax(data)), abs(amin(data)))
ninput = data.shape[1]
if nhidden is None: nhidden = len(set(cls))
noutput = amax(cls) + 1
# print ninput,nhidden,noutput
self.w1 = array(
data[selection(xrange(len(data)), nhidden)] * eps / scale, 'f')
self.b1 = array(uniform(-eps, eps, (nhidden, )), 'f')
self.w2 = array(uniform(-eps, eps, (noutput, nhidden)), 'f')
self.b2 = array(uniform(-eps, eps, (noutput, )), 'f')
def init(self,data,cls,nhidden=None,eps=1e-2):
"""Initialize the network but perform no training yet. The network units
are initialized using the data, and the classes are used to determine the number
of output units and (if no number of hidden units is given) the number of
hidden units."""
data = data.reshape(len(data),prod(data.shape[1:]))
scale = max(abs(amax(data)),abs(amin(data)))
# ninput = data.shape[1]
if nhidden is None: nhidden = len(set(cls))
noutput = amax(cls)+1
self.w1 = array(data[selection(range(len(data)),nhidden)] * eps/scale,'f')
self.b1 = array(uniform(-eps,eps,(nhidden,)),'f')
self.w2 = array(uniform(-eps,eps,(noutput,nhidden)),'f')
self.b2 = array(uniform(-eps,eps,(noutput,)),'f')
def change_nhidden(self, data, cls, new_nhidden, subset=None):
ninput, nhidden, noutput = self.shape()
if nhidden == new_nhidden:
pass
elif nhidden > new_nhidden:
keep = array([True] * nhidden)
for i in selection(xrange(nhidden), nhidden - new_nhidden):
keep[i] = False
self.w1 = array(self.w1[keep, :], dtype='f', order="C")
self.b1 = array(self.b1[keep], dtype='f', order="C")
self.w2 = array(self.w2[:, keep], dtype='f', order="C")
else:
vs = []
bs = []
delta = new_nhidden - nhidden
for i in range(delta):
a, b = selection(xrange(nhidden), 2)
l = 0.8 * rand(1)[0] + 0.1
v = l * self.w1[a] + (1 - l) * self.w1[b]
vs.append(v)
b = l * self.b1[a] + (1 - l) * self.b1[b]
bs.append(b)
self.w1 = array(1.0 * vstack([self.w1, array(vs)]),
dtype='f',
order="C")
self.b1 = array(1.0 * hstack([self.b1, array(bs)]),
dtype='f',
order="C")
scale = 0.01 * mean(abs(self.w2))
self.w2 = array(
1.0 * hstack([self.w2, scale * randn(len(self.w2), delta)]),
dtype='f',
order="C")
assert c_order(self.w1)
assert c_order(self.b1)
assert c_order(self.w2)
assert c_order(self.b2)
def init(self, data, cls, nhidden=None, eps=1e-2):
"""Initialize the network but perform no training yet. The network units
are initialized using the data, and the classes are used to determine the number
of output units and (if no number of hidden units is given) the number of
hidden units."""
data = data.reshape(len(data), prod(data.shape[1:]))
scale = max(abs(amax(data)), abs(amin(data)))
ninput = data.shape[1]
if nhidden is None: nhidden = len(set(cls))
noutput = amax(cls) + 1
self.w1 = array(
data[selection(xrange(len(data)), nhidden)] * eps / scale, 'f')
self.b1 = array(uniform(-eps, eps, (nhidden, )), 'f')
self.w2 = array(uniform(-eps, eps, (noutput, nhidden)), 'f')
self.b2 = array(uniform(-eps, eps, (noutput, )), 'f')
def increaseHidden(self, data, cls, new_nhidden):
"""Increase the number of hidden units. Data and cls are used to pick
initial values for new hidden units."""
nhidden = self.nhidden()
vs = []
bs = []
delta = new_nhidden - nhidden
for i in range(delta):
a, b = selection(xrange(nhidden), 2)
l = 0.8 * rand(1)[0] + 0.1
v = l * self.w1[a] + (1 - l) * self.w1[b]
vs.append(v)
b = l * self.b1[a] + (1 - l) * self.b1[b]
bs.append(b)
self.w1 = array(1.0 * vstack([self.w1, array(vs)]), dtype="f", order="C")
self.b1 = array(1.0 * hstack([self.b1, array(bs)]), dtype="f", order="C")
scale = 0.01 * mean(abs(self.w2))
vecs = [self.w2, scale * randn(len(self.w2), delta)]
self.w2 = array(1.0 * hstack(vecs), dtype="f", order="C")
def increaseHidden(self, data, cls, new_nhidden):
"""Increase the number of hidden units. Data and cls are used to pick
initial values for new hidden units."""
nhidden = self.nhidden()
vs = []
bs = []
delta = new_nhidden - nhidden
for i in range(delta):
a, b = selection(xrange(nhidden), 2)
l = 0.8 * rand(1)[0] + 0.1
v = l * self.w1[a] + (1 - l) * self.w1[b]
vs.append(v)
b = l * self.b1[a] + (1 - l) * self.b1[b]
bs.append(b)
self.w1 = array(1.0 * vstack([self.w1, array(vs)]),
dtype='f',
order="C")
self.b1 = array(1.0 * hstack([self.b1, array(bs)]),
dtype='f',
order="C")
scale = 0.01 * mean(abs(self.w2))
vecs = [self.w2, scale * randn(len(self.w2), delta)]
self.w2 = array(1.0 * hstack(vecs), dtype='f', order="C")
