I am trying to convert a keras model to pytorch for human activity recognition. The keras model could achieve up to 98% accuracy, while Pytorch model could achieve only ~60% accuracy. I couldn’t figure out the problem, first I was thinking of the padding=‘same’ of keras, but I have already adjusted the padding of pytorch already. Can you check what is wrong?

The keras code is as above

```
model = keras.Sequential()
model.add(layers.Input(shape=[100,12]))
model.add(layers.Conv1D(filters=32, kernel_size=3, padding="same"))
model.add(layers.BatchNormalization())
model.add(layers.ReLU())
model.add(layers.Conv1D(filters=64, kernel_size=3, padding="same"))
model.add(layers.BatchNormalization())
model.add(layers.ReLU())
model.add(layers.MaxPool1D(2))
model.add(layers.LSTM(64))
model.add(layers.Dense(units=128, activation='relu'))
model.add(layers.Dense(13, activation='softmax'))
model.summary()
```

and my pytorch model code is as below

```
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
seq_len = 100
# output: [, 32, 100]
self.conv1 = nn.Conv1d(seq_len, 32, kernel_size=3, stride=1, padding=1)
self.bn1 = nn.BatchNorm1d(32)
# output: [, 64, 100]
self.conv2 = nn.Conv1d(32, 64, kernel_size=3, padding=1)
self.bn2 = nn.BatchNorm1d(64)
# output: [, 64, 50]
self.mp = nn.MaxPool1d(kernel_size=2, stride=2)
# output: [, 64]
self.lstm = nn.LSTM(6, 64, 1)
# output: [, 128]
self.fc1 = nn.Linear(64, 128)
# output: [, 13]
self.fc2 = nn.Linear(128, 13)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = F.relu(x)
x = self.conv2(x)
x = self.bn2(x)
x = F.relu(x)
x = self.mp(x)
out, _ = self.lstm(x)
x = out[:, -1, :]
x = self.fc1(x)
x = F.relu(x)
x = self.fc2(x)
return x
```