Question

我试图实现一个自定义Op，我得到了“theano function的错误输入参数”错误。这是代码。我理解的问题是：如何将PyMC3变量转换为可理解的类型？

import numpy as np
import theano
import theano.tensor as t
from theano import config
config.compute_test_value = 'off'

#true_Data = [1,2]
#values=[]

class trial_Op(theano.Op):
    __props__ = ()
    itypes = [t.dmatrix, t.dmatrix, t.dmatrix]
    otypes = [t.dmatrix]

    def perform(self,node,inputs,output_storage):
        x0 = inputs[0]
        x1 = inputs[1]
        x2 = inputs[2]
        z = output_storage[0]
        z[0] = np.add(x0,x1)
        z[0] = np.add(z[0],x2)

    def grad(self,inputs,output_grads):
        return output_grads[0]
Trial_Op = trial_Op()

x1 = t.dmatrix()
x2 = t.dmatrix()
x3 = t.dmatrix()
f = theano.function( [x1,x2,x3], trial_Op()(x1,x2,x3) )

# the Op works for the 
#inp1 = np.random.rand(3,1)  # a 2d matrix
#inp2 = np.random.rand(3,1)  # a 2d matrix
#inp3 = np.array([[-40]])                # a constant
#print("Op application gives = ", f(inp1,inp2,inp3))


import pymc3 as pm
true_Data = [[1]]

with pm.Model() as model:
    x1 = pm.Normal('x1', mu = 0, sd = 0.1)
    x2 = pm.Normal('x2', mu = 3, sd = 0.5)
    x3 = np.asarray([[4]], dtype='float64')
#    x1 = x1.reshape(1,1)
#    x2 = x2.reshape(1,1)
    sum_of_x1_x2_x3 = f(x1,x2,x3)
    z = pm.Normal('z', sum_of_x1_x2_x3, observed = true_Data)
    start = {'x1':[[0.1]], 'x2':[[0.1]]}
    step = pm.Metropolis()
    trace = pm.sample(100, step, start)

pm.traceplot(trace)

Answer 1

我想我可以回答你的问题。

首先，您不应该使用f = theano.function( [x1,x2,x3], trial_Op()(x1,x2,x3) )。定义后，f将数值作为参数。但是，在pymc3模型中，x1和x2定义为Normal不是数字而是符号。所以它会抛出你刚遇到的错误。如果您熟悉本教程中记录的 @as_op 方法，则解决方案很简单：将sum_of_x1_x2_x3 = f(x1,x2,x3)更改为sum_of_x1_x2_x3 = Trial_Op(x1,x2,x3);

其次，在您的代码中，似乎不需要dmatrix类型。所以我修改了以下代码：

N = 20 #data array length

class Trial_Op(theano.Op):
    __props__ = ()
    itypes = [t.dscalar, t.dscalar]
    otypes = [t.dvector] #if the data has multiple values i.e. data array

    def perform(self,node,inputs,output_storage):
        x0 = inputs[0]
        x1 = inputs[1]

        f = np.add(x0,x1)
        out = np.empty(N)
        out[:] = f
        z = output_storage[0]
        z[0] = out


trial_Op = Trial_Op()


import pymc3 as pm
true_Data = np.random.normal(2,1,N)

with pm.Model() as model:
    x1 = pm.Normal('x1', mu = 0, sd = 0.1)
    x2 = pm.Normal('x2', mu = 3, sd = 0.5)    

    mu = trial_Op(x1, x2)
    z = pm.Normal('z', mu = mu, sd = 1., observed = true_Data)

    step = pm.Metropolis()
    trace = pm.sample(10000, step)

pm.traceplot(trace)

请注意，在自定义函数中，otypes为dvector以满足mu中的z = pm.Normal('z', mu = mu, sd = 1., observed = true_Data)。样本编号扩展为10000.结果图像显示：

result

但是，我不知道如何在自定义theano函数中定义 grad() 方法。也许以后某人或我可以解决它以在模型中启用NUTS采样方法:)。

Theano定制Op for PyMC3

1 个答案: