Here is the keras architecture
model=Sequential()

model.add(Dense(units=255,activation='relu',input_shape=[21]))

model.add(Dense(units=128,activation='relu'))

model.add(Dense(units=64,activation='relu'))

model.add(Dense(units=32,activation='relu'))

model.add(Dense(units=10,activation='softmax'))

Here is the pytorch architecture
class nonseqV2(torch.nn.Module):

    def __init__(self):

        super(nonseqV2,self).__init__()

        self.fcLayer1=torch.nn.Linear(in_features=21, out_features=256) 

        self.fcLayer2=torch.nn.Linear(in_features=256, out_features=128)

        self.fcLayer3=torch.nn.Linear(in_features=128, out_features=64)

        self.fcLayer4=torch.nn.Linear(in_features=64, out_features=10)

        self.relu=torch.nn.ReLU()

        self.softmax=torch.nn.Softmax(dim=1)

        

    def forward(self,x):

        x=self.fcLayer1(x)

        x=self.relu(x)

        x=self.fcLayer2(x)

        x=self.relu(x)

        x=self.fcLayer3(x)

        x=self.relu(x)

        x=self.fcLayer4(x)

        x=self.softmax(x)

        return x

Keras compiling and training
model.compile(optimizer=Adam(0.0001),loss='categorical_crossentropy',metrics=['accuracy'])
model.fit(x=train_modeling,y=train_target_binary,batch_size=200,epochs=50,verbose=0,validation_data=(valid_modeling,valid_target_binary))

Pytoch training
for ep in range(50):

    for xtb,ytb in train_loader:

        xtb,ytb=xtb.cuda(),ytb.cuda()

        tpred=model(xtb)

        loss=crossentropy(tpred,ytb.long())

        opt.zero_grad()

        loss.backward()

        opt.step()

Keras loss
log_loss(valid_target_binary,y_pred)
Pytorch Loss
log_loss(val_labels,yprd.detach().cpu().numpy())
As pytorch accept 1D label, so the variable names are different, but essentially they have same data.
This is how data feeded to keras is converted to pytorch tensor
val_features=torch.Tensor(valid_modeling)

val_labels=torch.Tensor(np.argmax(valid_target_binary,axis=1))

But loss obtained from Keras is 1.4 and PyTorch its 10.43. I know because of initilization factor, result should be bit different, but it should not be much different.