小编imb*_*_nl的帖子

我正在学习深度学习，并试图理解下面给出的 pytorch 代码。我正在努力理解概率计算是如何工作的。可以以某种方式将其分解为外行人的术语。万分感谢。

ps = model.forward(图像[0,:])

# Hyperparameters for our network
input_size = 784
hidden_sizes = [128, 64]
output_size = 10

# Build a feed-forward network
model = nn.Sequential(nn.Linear(input_size, hidden_sizes[0]),
                      nn.ReLU(),
                      nn.Linear(hidden_sizes[0], hidden_sizes[1]),
                      nn.ReLU(),
                      nn.Linear(hidden_sizes[1], output_size),
                      nn.Softmax(dim=1))
print(model)

# Forward pass through the network and display output
images, labels = next(iter(trainloader))
images.resize_(images.shape[0], 1, 784)
print(images.shape)
ps = model.forward(images[0,:])