can i use the tf-idf matrix as input to the conventional auto-encoder composed of conv1d layers?
here is the form of my df-idf data
>>> tfidf.shape
>>>(3047,500)

I use a batch of size 64  , and i reshape the input  by this line
x = x.reshape(x.shape[0], x.shape[1],1)

the input is become of this form
input[64, 500, 1]

my class are as follows
class Encoder(nn.Module):
    def __init__(self, input_size, intermediate_size, encoding_size):
        super().__init__()
        self.encoder = nn.Sequential(
            nn.Conv1d(500, 100, 1),
            nn.BatchNorm1d(100),
            nn.ReLU(True),
            nn.MaxPool1d(kernel_size=1, stride=1),
            nn.Dropout(0.2),
          
            nn.Conv1d(100, 50, 1),
            nn.BatchNorm1d(50),
            nn.ReLU(True),
            nn.MaxPool1d(kernel_size=1, stride=1),
            nn.Dropout(0.2))

    def forward(self, x):
        x = x.reshape(x.shape[0], x.shape[1],1)
        x = self.encoder(x)
        return x


class Decoder(nn.Module):
    def __init__(self, output_size, intermediate_size, encoding_size):
        super().__init__()
        self.decoder = nn.Sequential(
            nn.ConvTranspose1d(50,100, 1),
           
            nn.BatchNorm1d(100),
            nn.ReLU(True),
            nn.Dropout(0.2),
            
            nn.ConvTranspose1d(100, 500, 1),
            nn.BatchNorm1d(500),
            nn.Sigmoid())

    def forward(self, x):
        #x = x.view(1, 1, -1)
        x = self.decoder(x)
        return x


but it shows me this error
RuntimeError: The size of tensor a (500) must match the size of tensor b (64) at non-singleton dimension 1

Sincerely, I do not know what’s my problem here? ? can you help me please ?