我刚刚在Theano中实现了一个高斯内核。然而,当我将其作为神经网络的一部分进行测试时,需要的时间太长。似乎内核减法不是并行化的。整个网络培训使用单个处理核心。那么,如何正确地诱导Theano分裂内核操作呢?
import theano.tensor as T
import numpy
import theano
batch_s=5
dims=10
hidd_s=3
out_s=2
missing_param = None #"ignore"
rng = numpy.random.RandomState(1234)
input = T.matrix("input")
X = numpy.asarray(rng.uniform(low=-2.1, high=5.0, size=(batch_s, dims)))
def layer(x):
W=theano.shared(
value=numpy.asarray(
rng.uniform(low=0.001, high=1.0, size=(dims, hidd_s)),
dtype=theano.config.floatX),
name='W', borrow=True)
S=theano.shared(
value=numpy.asarray(
rng.uniform(low=10.0, high=100.0, size=(hidd_s, )),
dtype=theano.config.floatX),
name='S', borrow=True)
dot_H = theano.shared(
value=numpy.zeros((batch_s, hidd_s),
dtype=theano.config.floatX),
name='dot_H', borrow=True)
# This is the kernel operation. I have tested with single scan as well
# as with two nested scans, but operations arenot splitted as in the
# case of the usual dot product T.dot().
for i in range(batch_s):
for j in range(hidd_s):
dot_H = T.set_subtensor(dot_H[i,j],
T.exp(-(W.T[j] - x[i]).norm(2) ** 2) / 2 * S[j] ** 2)
return dot_H
layer_out = theano.function(
inputs=[input],
outputs=layer(input),
on_unused_input=missing_param
)
print layer_out(X)
非常感谢你。
答案 0 :(得分:0)
删除循环将允许Theano优化并行化。
首先,您可以通过执行以下操作来避免内循环:
for i in range(batch_s):
T.exp(-(W.T - X[i]).norm(2,axis=1) ** 2) / 2 * S ** 2)
然后你可以在外循环上使用map:
import theano.tensor as T
import numpy
import theano
import timeit
start = timeit.default_timer()
batch_s=5
dims=10
hidd_s=3
out_s=2
missing_param = None #"ignore"
rng = numpy.random.RandomState(1234)
input = T.matrix("input")
X = numpy.asarray(rng.uniform(low=-2.1, high=5.0, size=(batch_s, dims)))
W=theano.shared(
value=numpy.asarray(
rng.uniform(low=0.001, high=1.0, size=(dims, hidd_s)),
dtype=theano.config.floatX),
name='W', borrow=True)
S=theano.shared(
value=numpy.asarray(
rng.uniform(low=10.0, high=100.0, size=(hidd_s, )),
dtype=theano.config.floatX),
name='S', borrow=True)
f_func,f_updates = theano.map(lambda i : T.exp(-(W.T - i).norm(2,axis=1) ** 2) / 2 * S ** 2,input,[])
layer_out = theano.function([input],
f_func,
updates=f_updates,
on_unused_input=missing_param,
allow_input_downcast=True)
print layer_out(X.astype('float32'))
stop = timeit.default_timer()
print "running time: " + str(stop - start)
原始代码的输出是:
[[ 1.83701953e-25 1.78982216e-26 9.22911484e-27]
[ 1.60078639e-17 9.21553384e-17 7.62476155e-14]
[ 8.13404350e-17 1.88481821e-17 2.44677516e-15]
[ 3.16093011e-29 1.49698827e-27 2.42876079e-27]
[ 9.57103818e-09 3.46683533e-12 6.66103154e-12]]
running time: 1.30477905273
使用新的:
[[ 1.83701953e-25 1.78982216e-26 9.22911484e-27]
[ 1.60078639e-17 9.21553384e-17 7.62476155e-14]
[ 8.13404350e-17 1.88481821e-17 2.44677516e-15]
[ 3.16093011e-29 1.49698827e-27 2.42876079e-27]
[ 9.57103818e-09 3.46683533e-12 6.66103154e-12]]
running time: 0.589275121689