Input: f, g, A, X, n epochs, n epoch f t Compute T as presented in Section 3.1. Compute TCF , ACF by Eqs. (3) and (4). // model training for epoch in range(n epochs) do Z = f(A, X). Get bA and bACF via g with Eqs. (6) and (7). Update Θf and Θg with L. (Eq. (11)) end for // decoder fine-tuning Freeze Θf and re-initialize Θg. Z = f(A, X). for epoch in range(n epochs f t) do Get bA via g with Eq. (6). Update Θg with LF . (Eq. (8)) end for // inference Z = f(A, X). Get bA and bACF via g with Eqs. (6) and (7). Output: bA for link prediction, bACF .
时间: 2023-06-18 12:06:37 浏览: 46
This appears to be a code snippet for a machine learning algorithm, possibly related to link prediction. The algorithm involves training a model (f) and a generator (g) using a dataset (A, X) for a specified number of epochs. The code then performs decoder fine-tuning and inference to output bA and bACF for link prediction.
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for epoch in range(1, args.num_epochs + 1):
这段代码是一个for循环,用来训练模型。具体解释如下:
1. `range(1, args.num_epochs + 1)`: 表示循环的范围,从1到`num_epochs`+1,其中`num_epochs`是训练的epoch数,即整个数据集将被训练的次数;
2. `for epoch in ...`: 表示循环中的每一个元素都被赋值给`epoch`变量,即当前循环所处的epoch数。
在训练过程中,每一个epoch会依次遍历整个训练数据集,对每一个数据样本进行前向传播和反向传播操作,以更新模型的权重参数。循环的次数由`num_epochs`参数决定,每一个epoch的训练过程中会产生一个训练损失和一个验证损失,用来评估当前模型的性能和调整模型的超参数。
def train(self): float2str = lambda x: '%0.4f' % x for i in range(self.epochs): self.current_epoch += 1是什么
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