Question

我有以下定义受MLP Theano教程的启发：

Weights = rng.uniform(low=low, high=high, size=(n_in, n_out))
        W_values = numpy.asarray(Weights, dtype=theano.config.floatX)
        W = theano.shared(value=W_values, name='W', borrow=True)
b_values = numpy.zeros((n_out,), dtype=theano.config.floatX)
        b = theano.shared(value=b_values, name='b', borrow=True)
sigmas = rng.uniform(low=0.001, high=100, size=(n_out,))
        s_values = numpy.asarray(sigmas, dtype=theano.config.floatX)
        s = theano.shared(value=s_values, name='s', borrow=True)
input = T.matrix("input")

我想像这样计算高斯激活：

output = array([[numpy.exp(-numpy.linalg.norm(w_s - x_s, 2) ** 2 / 2*s_s ** 2) for w_s, s_s in zip(W.T, s)] for x_s in X]) + b

然而，这是不可能直接的，因为W和X都不是可迭代的。如何以有效的方式编写Theano版本？

非常感谢。

修改

我已经配置了一个解决方案：

# Declare an intermediate shared variable:
h_values = numpy.zeros((batch_s, n_out), dtype=theano.config.floatX)
dot_H = theano.shared(value=h_values, name='h', borrow=True)
# compute the kernel for an input vector and then for the whole batch 
for i in range(batch_s):
    dot_H=T.set_subtensor(dot_H[i],theano.scan(lambda w, sig, bias: \
                   T.exp(-ops.norm(w - input[i], 2) ** 2 / 2*sig ** 2) 
                        +bias,
                    sequences=[self.W.T, self.s, self.b])[0])
# retrieve the solution as a numpy matrix
output = dot_H

然而..我发现这会在dot_H中返回所有项目零。似乎即使差异w - input[i]也无法正确计算。

EDIT_2 我已经解决了这个问题，不过我觉得我的解决方案不是最有效的解决方案，有人能建议我更好吗？

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)

    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)

Answer 1

T.set_subtensor(x[...], y)将返回＆＃34;符号＆＃34;给定子指定符的变量由y替换。它实际上不会在x内运作。要更改共享变量x，您需要使用theano.function和＆＃34; update＆＃34; x有新值。

theano

1 个答案: