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[Eirun_Xu]

Hi, Yujia,
the codes in mlp.py as below:

during training, we use the classifier like this:

why these codes do not use the argument XQ, YQ(query set)?? Thank you

[Yujia Bao]

mlp.py is designed for training MAML and PMAML, so it is only called in train/maml.py and is not used in train/regular.py.

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On Tue, Jun 23, 2020 at 1:53 AM Eirun_Xu ***@***.***> wrote: Hi, Yujia, the codes in mlp.py as below: def forward(self, XS, YS=None, XQ=None, YQ=None, weights=None): ''' if y is specified, return loss and accuracy otherwise, return the transformed x @param <https://github.com/param>: XS: batch_size * input_dim @param <https://github.com/param>: YS: batch_size (optional) @return <https://github.com/return>: XS: batch_size * output_dim ''' # normal training procedure, train stage only use query if weights is None: XS = self.mlp(XS) else: # find weight and bias keys for the mlp module w_keys, b_keys = [], [] for key in weights.keys(): if key[:4] == 'mlp.': if key[-6:] == 'weight': w_keys.append(key) else: b_keys.append(key) for i in range(len(w_keys)-1): XS = F.dropout(XS, self.args.dropout, training=self.training) XS = F.linear(XS, weights[w_keys[i]], weights[b_keys[i]]) XS = F.relu(XS) XS = F.dropout(XS, self.args.dropout, training=self.training) XS = F.linear(XS, weights[w_keys[-1]], weights[b_keys[-1]]) if self.top_layer is not None: XS = self.top_layer(XS) # normal training procedure, compute loss/acc if YS is not None: _, YS = torch.unique(YS, sorted=True, return_inverse=True) loss = F.cross_entropy(XS, YS) acc = BASE.compute_acc(XS, YS) return acc, loss else: return XS Why the codes do not use the argument XQ, YQ(query set). — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub <#12>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ABYZCHDPJXAUJJ2FJ7C7OLLRYA7MPANCNFSM4OFKCJVQ> .

-- Best, Yujia