I’m trying to translate the below 3layer CNN architecture from keras to pytorch. The usage of the model is to predict expression value(input_shape_val) from dna sequence(input_shape_hot). The sequence is one hot encoded. The architecture orignally meant to train the model consecutively CNN (3 layers)-FC (2 layers) with batch normalization and weight dropout were applied after all layers and max-pooling after CNN layers( ref-paper, ref-code).

```
from typing import List
class DNA_CNN_test2(nn.Module):
def __init__(self,
seq_len: int,
num_filters: List[int] = [32, 64, 128],
kernel_size: int = 3,
p = 0.2):
super().__init__()
self.seq_len = seq_len
# CNN module
self.conv_net = nn.Sequential()
num_filters = [4] + num_filters
for idx in range(len(num_filters) - 1):
self.conv_net.add_module(
f"conv_{idx}",
nn.Conv1d(num_filters[idx], num_filters[idx + 1],
kernel_size=kernel_size, padding='same')
)
self.conv_net.add_module(f"relu_{idx}", nn.ReLU(inplace=True))
self.conv_net.add_module(f"batchNor_{idx}",nn.BatchNorm1d(num_filters[idx + 1]))
self.conv_net.add_module(f"MaxP_{idx}",nn.MaxPool1d(kernel_size=2,stride= 4))
self.conv_net.add_module(f"dropout_{idx}",nn.Dropout(0.2))
self.conv_net.add_module("flatten", nn.Flatten())
self.conv_net.add_module("linear",nn.Linear(num_filters[-1]*seq_len, 1))
#self.conv_net.add_module("linear",nn.Linear(64, 1))
#self.conv_net.add_module("relu", nn.ReLU(inplace=True))
#self.conv_net.add_module("batch_normal",nn.BatchNorm1d(64))
#self.conv_net.add_module("drop",nn.Dropout(0.2))
#self.conv_net.add_module("linear",nn.Linear(num_filters[-1]*seq_len, 1))
def forward(self, xb: torch.Tensor):
"""Forward pass."""
xb = xb.permute(0, 2, 1)
out = self.conv_net(xb)
return out
```

however I am facing challenge on using `MaxPool1d`

after `Conv1d`

. I have already tried many suggestions from answers to similar questions, but none of them worked. Any suggestions about what I should do?

Here is the error code :

`mat1 and mat2 shapes cannot be multiplied (2048x2048 and 128000x1)`