RBM我们添加了将可见输入转换为隐藏表示和将隐藏表示转换回重构可见输入的方法。这两种方法都返回激活概率，而sample_h方法还返回观察到的隐藏状态。
<pre><code>
class RBM():
def __init__(self, visible_dim, hidden_dim, gaussian_hidden_distribution=False):
self.visible_dim = visible_dim
self.hidden_dim = hidden_dim
self.gaussian_hidden_distribution = gaussian_hidden_distribution
# intialize parameters
self.W = torch.randn(visible_dim, hidden_dim) * 0.1
self.h_bias = torch.zeros(hidden_dim) # visible --> hidden
self.v_bias = torch.zeros(visible_dim) # hidden --> visible
# parameters for learning with momentum
self.W_momentum = torch.zeros(visible_dim, hidden_dim)
self.h_bias_momentum = torch.zeros(hidden_dim)
self.v_bias_momentum = torch.zeros(visible_dim)
def sample_h(self, v):
activation = torch.mm(v, self.W) + self.h_bias
if self.gaussian_hidden_distribution:
return activation, torch.normal(activation, torch.tensor([1]))
else:
p = torch.sigmoid(activation)
return p, torch.bernoulli(p)
def sample_v(self, h):
"""Get visible activation probabilities"""
activation = torch.mm(h, self.W.t()) + self.v_bias
p = torch.sigmoid(activation)
return p
def update_weights(self, v0, vk, ph0, phk, lr,
momentum_coef, weight_decay, batch_size):
self.W_momentum *= momentum_coef
self.W_momentum += torch.mm(v0.t(), ph0) - torch.mm(vk.t(), phk)
self.h_bias_momentum *= momentum_coef
self.h_bias_momentum += torch.sum((ph0 - phk), 0)
self.v_bias_momentum *= momentum_coef
self.v_bias_momentum += torch.sum((v0 - vk), 0)
self.W += lr*self.W_momentum/batch_size
self.h_bias += lr*self.h_bias_momentum/batch_size
self.v_bias += lr*self.v_bias_momentum/batch_size
self.W -= self.W * weight_decay # L2 weight decay
</code></pre>
训练RBM 在训练模型时我遇到了" RuntimeError: self must be a matrix"的问题，有人能帮我解决一下并告诉我需要在代码中做出哪些修改吗？
<pre><code>
models = [] # store trained RBM models
visible_dim = 784
rbm_train_dl = train_dl_flat
for hidden_dim in [1000, 500, 250, 2]:
# configs - we have a different configuration for the last layer
num_epochs = 30 if hidden_dim == 2 else 10
lr = 1e-3 if hidden_dim == 2 else 0.1
use_gaussian = hidden_dim == 2
# train RBM
rbm = RBM(visible_dim=visible_dim, hidden_dim=hidden_dim,
gaussian_hidden_distribution=use_gaussian)
for epoch in range(num_epochs):
for i, data_list in enumerate(train_dl):
v0 = data_list[0]
# get reconstructed input via Gibbs sampling with k=1
_, hk = rbm.sample_h(v0)
pvk = rbm.sample_v(hk)
# update weights
rbm.update_weights(v0, pvk, rbm.sample_h(v0)[0], rbm.sample_h(pvk)[0], lr,
momentum_coef=0.5 if epoch < 5 else 0.9,
weight_decay=2e-4,
batch_size=sample_data.shape[0])
models.append(rbm)
# rederive new data loader based on hidden activations of trained model
new_data = [model.sample_h(data_list[0])[0].detach().numpy() for data_list in rbm_train_dl]
rbm_train_dl = DataLoader(
TensorDataset(torch.Tensor(np.concatenate(new_data))),
batch_size=64, shuffle=False
)
visible_dim = hidden_dim
</code></pre>
ERROR
<pre><code>
RuntimeError Traceback (most recent call last)
<ipython-input-3-53fe4223334d> in <module>()
16
17 # get reconstructed input via Gibbs sampling with k=1
---> 18 _, hk = rbm.sample_h(v0)
19 pvk = rbm.sample_v(hk)
20 # update weights
<ipython-input-1-49d2abc1da92> in sample_h(self, v)
15 def sample_h(self, v):
16 """Get sample hidden values and activation probabilities"""
---> 17 activation = torch.mm(v, self.W) + self.h_bias
18 if self.gaussian_hidden_distribution:
19 return activation, torch.normal(activation, torch.tensor([1]))
RuntimeError: self must be a matrix
</code></pre>
sample_h。您在RBM的实例上调用它，但RBM既不是矩阵，也没有从矩阵继承。换句话说，您正在对一个没有该方法的对象使用方法，因此没有理由期望它能正常工作。 - undefined