AttributeError: Can't gather from tf tensor.

[nestyme]

Hi! Thanks for implementing this necessary conversion 👍
I am stuck with error 'AttributeError: Can't gather from tf tensor.' when I'm trying to export my PyTorch model to Keras.

'k_model = pytorch_to_keras(model, input_var, (1,None),
verbose=True, name_policy='short',
change_ordering=True)'

PyTorch model:

class SentenceEmbeddingsModel(nn.Module):
    def __init__(self,
                 vocab_size,embedding_dim,
                 max_length=40,
                 word_vectors=None,
                 device=device,
                 C=0.001,d_a=10,r_a=4,
                 hidden_size=100):
        super(SentenceEmbeddingsModel, self).__init__()

        self.embeddings = nn.Embedding(vocab_size, embedding_dim)

        self.d_a = d_a
        self.C = C
        self.r_a = r_a
        self.rnn_hidden_size = hidden_size

        w =torch.FloatTensor(word_vectors)
        self.embeddings = self.embeddings.from_pretrained(w)
        self.embeddings.weight.requires_grad = False
        ws_d = embedding_dim

        self.ws1 = nn.Parameter(torch.FloatTensor(1, self.d_a, ws_d))
        nn.init.xavier_uniform_(self.ws1)
        self.ws1.requires_grad = True

        self.ws2 = nn.Parameter(torch.FloatTensor(1, self.r_a, self.d_a))
        nn.init.xavier_uniform_(self.ws2)
        self.ws2.requires_grad = True

        self.dropout1 = nn.Dropout(0.1)

        self.device = device

        self.dense = nn.Sequential(
            nn.Linear(ws_d, 20, bias=True),
            nn.ReLU(),
            nn.Dropout(0.1),
            nn.Linear(20, num_classes, bias=True),
        )

        self.linear = nn.Linear(ws_d * self.r_a, ws_d)

    def forward(self, inputs):
        e = self.embeddings(inputs)
        mask = (inputs != 0)[:, :, None].float().to(self.device)
        masked = e.mul(mask)
        r = self.dropout1(masked)
        z = r

        a1 =torch.tanh(self.ws1.matmul(z.transpose(dim0=1, dim1=2)))

        attention = F.softmax(self.ws2.matmul(a1), dim=2)  # n_batch - r_a - max_lentgh
        m = attention.matmul(z)  # n_batch - r_a - ws_d

        # here we get r_a * ws_d embedding matrix per sentence
        flatten = m.view(z.shape[0], -1, 1)[:, :, 0]

        m = self.linear(flatten)

        out = torch.sigmoid(self.dense(m))
        return out
```

Is anyone able to help me?
Thanks

[sampathpagolu]

any update on this error?

[mAkeddar]

Hello,

Is there any update on this issue ?

[ovshake]

facing this issue, any updates?