Question

class RBM():
    def __init__(self, nv, nh):
        self.W = torch.randn(nh, nv)
        self.a = torch.randn(1, nh)
        self.b = torch.randn(1, nv)
    def sample_h(self, x):
        wx = torch.mm(x, self.W.t())
        activation = wx + self.a.expand_as(wx)
        p_h_given_v = torch.sigmoid(activation)
        return p_h_given_v, torch.bernoulli(p_h_given_v)
    def sample_v(self, y):
        wy = torch.mm(y, self.W)
        activation = wy + self.b.expand_as(wy)
        p_v_given_h = torch.sigmoid(activation)
        return p_v_given_h, torch.bernoulli(p_v_given_h)
    def train(self, v0, vk, ph0, phk):
        self.W += torch.mm(v0.t(), ph0) - torch.mm(vk.t(), phk)
        self.b += torch.sum((v0 - vk), 0)
        self.a += torch.sum((ph0 - phk), 0)

错误：

火车上的（self，v0，vk，ph0，phk）

     19         return p_v_given_h, torch.bernoulli(p_v_given_h)
     20     def train(self, v0, vk, ph0, phk):
---> 21         self.W += torch.mm(v0.t(), ph0) - torch.mm(vk.t(), phk)
     22         self.b += torch.sum((v0 - vk), 0)
     23         self.a += torch.sum((ph0 - phk), 0)

RuntimeError：张量（1682）的扩展大小必须与非单维度1上的现有大小（100）相匹配

Answer 1

print(rbm.W.size())将向您显示torch.Size（[100，1682]）

print((torch.mm(v0.t(), ph0)-torch.mm(vk.t(), phk)).size())将向您显示torch.Size（[1682，100]）

所以看起来应该像这样：

self.W += (torch.mm(v0.t(), ph0) - torch.mm(vk.t(), phk)).t()

代替self.W += torch.mm(v0.t(), ph0) - torch.mm(vk.t(), phk)

Answer 2

更改此行：

self.W += torch.mm (v0.t(), ph0) - torch.mm (vk.t(), phk)

对此：

self.W += (torch.mm(v0.t(), ph0) - torch.mm(vk.t(), phk)).t()

Answer 3

尝试一下...

reduce

Answer 4

每当出现此类尺寸错误时，请尝试在所有变量的前面编写尺寸。如上所述，您必须在火车功能的第一线进行运输。

class RBM():
    def __init__(self, nv, nh):
        self.W = torch.randn(nh, nv)   #100*1682                 
        self.a = torch.randn(1, nh)    #1*100                       
        self.b = torch.randn(1, nv)    #1*1682                   
    def sample_h(self, x):                                     
        wx = torch.mm(x, self.W.t())    #100*1682 * 1682*100 = 100*100
        activation = wx + self.a.expand_as(wx)                  
        p_h_given_v = torch.sigmoid(activation)     #100*100
        return p_h_given_v, torch.bernoulli(p_h_given_v)
    def sample_v(self, y):                                      
        wy = torch.mm(y, self.W)    #100*100 * 100*1682 = 100*1682
        activation = wy + self.b.expand_as(wy)                  
        p_v_given_h = torch.sigmoid(activation)     #100*1682
        return p_v_given_h, torch.bernoulli(p_v_given_h)
    def train(self, v0, vk, ph0, phk):                         
        self.W += (torch.mm(v0.t(), ph0)- torch.mm(vk.t(), phk)).t()    #100*1682 
                             #!= 1682*100 * 100*100 - 1682*100 * 100*100 = 1682*100
        self.b += torch.sum((v0-vk), 0)                            
        self.a += torch.sum((ph0-phk), 0)

在对Boltzmann机器执行对比发散时，出现以下错误。我正在使用最新版本的pytorch

4 个答案: