hi Olaf,
thanks for the suggestion but that didn’t get over the error.
        self.decode = nn.Sequential(OrderedDict([
            ('fc1',nn.Linear(z_dim, 672)),
            ('relu1',nn.ReLU()),
            ('conv1',nn.ConvTranspose1d(32, 32, 8, 2, padding=3)),
            ('bn1',nn.BatchNorm1d(32)),
            ('conv2',nn.ConvTranspose1d(32, 32, 8, 2, padding=3)),
            ('bn2',nn.BatchNorm1d(32)),
            ('conv3',nn.ConvTranspose1d(32, 16, 8, 2, padding=3)),
            ('bn3',nn.BatchNorm1d(32)),
            ('conv4',nn.ConvTranspose1d(16, 16, 8, 2, padding=3)),
            ('bn4',nn.BatchNorm1d(32)),
            ('conv5',nn.ConvTranspose1d(16, 1, 7, 1, padding=3)),
        ]))
        
        
    def forward(self, z):
        z = self.decode.relu1(self.decode.fc1(z))
        print(z)
        print(z.shape)
        z = z.view(-1, 32, 21)
        print(z)
        print(z.shape)
        z = self.decode(z)
        print(z)
        print(z.shape)
        #z = self.relu(self.fc1(z))
        #z = F.dropout(z, 0.3)
        recon = torch.sigmoid(z)
        return recon

gives the following error:
tensor([[[0.3054, 0.0000, 0.0000,  ..., 0.0000, 0.4342, 0.0000]],

        [[0.4361, 0.0000, 0.0000,  ..., 0.0206, 0.5194, 0.0000]]],
       device='cuda:0', grad_fn=<ReluBackward0>)
torch.Size([2, 1, 672])
tensor([[[0.3054, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000],
         [0.4938, 0.0000, 0.0000,  ..., 0.0000, 0.2481, 0.9124],
         [0.5702, 0.0000, 0.0000,  ..., 0.4131, 0.1147, 0.0842],
         ...,
         [0.0000, 0.1210, 0.5313,  ..., 0.6218, 0.3895, 0.0000],
         [0.0000, 0.0000, 0.0000,  ..., 0.0850, 0.5332, 0.1528],
         [0.2276, 0.0021, 0.0000,  ..., 0.0000, 0.4342, 0.0000]],

        [[0.4361, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000],
         [0.3129, 0.0000, 0.0000,  ..., 0.0000, 0.3701, 1.3570],
         [0.8684, 0.0000, 0.0000,  ..., 0.2555, 0.4145, 0.0000],
         ...,
         [0.0000, 0.4524, 0.8189,  ..., 0.8206, 0.2301, 0.0000],
         [0.0000, 0.0000, 0.0000,  ..., 0.4690, 0.3480, 0.0000],
         [0.0000, 0.0000, 0.0000,  ..., 0.0206, 0.5194, 0.0000]]],
       device='cuda:0', grad_fn=<ViewBackward>)
torch.Size([2, 32, 21])
Traceback (most recent call last):

RuntimeError: size mismatch, m1: [64 x 21], m2: [2 x 672] at C:/w/1/s/tmp_conda_3.7_044431/conda/conda-bld/pytorch_1556686009173/work/aten/src\THC/generic/THCTensorMathBlas.cu:268