Caffe swish activation8/30/2023 In the cpp files you have only to implement the forward and the backward pass, like this: I think it's possible, so if I will decide to use Swish for real I probably will. I implemented swish both for CPU and for GPU with CUDA, but not for CUDnn. To add a layer in Caffe the fastest way is to follow the instruction in, and in this case:Ĭreate "swish_layer.cpp", "swich_layer.hpp" and "swish_layer.cu". So it's still expressed in an analytical way and using only precalculated values, so our backward pass will be very fast. Forward pass is straitghforward.īackward pass need the derivative of the Swish, that is very simple. The Swish activation function is defined as x * sigmoid(x). An implementation that does allow for in-place computation is easy to do, ask if needed. So you will have to use different blobs for top and bottom of the Swish layer. NOTE: this implementation DOES NOT allow for in-place computation. Let's see how to implement Swish activation function in Caffe framework. I work with Windows, so I used the Windows branch of Caffe but I'm pretty sure it works also with Linux. This is the first time that I use a non-monotonic function, and I was very excited to have a look at it, so I implemented the layer in Caffe ( ) to make some tests. Intuitively this should change the behaviour of the weigths in the zone where the normal ReLU ceases to be active. Except for on thing: it has a zone, just before zero, where the function inverts its derivative. It's defined by x * sigmoid(x), and it's graph looks like the ReLU's one. X = nn.SiLU()(self.pool1(self.bn2(self.A novelty in deep learning seems to be the new "Swish" activation function ( ), a sort of ReLU but with an important feature: it is NOT a monotonic function. Your code is not executable and I cannot reproduce the issue using: class LeNet5(nn.Module): You didn’t explain what the actual issue is so I guess beta is not being updated? But, “beta” parameter is still not training. What am I doing wrong? Why isn’t beta training as expected?ĭuring the epochs, beta is fixed at 1.0, whereas, beta grad shows gradient updates. The problem is that “beta” parameter in LeNet5() instance is not being updated. Torch.save(model.state_dict(), "LeNet5_MNIST_best_val_acc.pth") Model = model, test_loader = test_loader,Ĭurrent_lr = optimizer.param_groups # Get validation metrics after 1 epoch of training. Model = model, train_loader = train_loader, While training the model, I am printing ‘beta’ as: for epoch in range(1, num_epochs + 1): # Python3 dict to contain training metrics. Return val_loss, val_acc.detach().cpu().item() Val_loss = running_loss_val / len(test_dataset) # return (running_loss_val, correct, total) Val_acc = 100 * (correct.cpu().numpy() / total) Val_loss = running_loss_val / len(test_dataset), Running_loss_val += J_val.item() * labels.size(0) With tqdm(test_loader, unit = 'batch') as tepoch: Return train_loss, train_acc.detach().cpu().item()ĭef test_one_step(model, test_loader, test_dataset): Train_acc = (running_corrects.double() / len(train_dataset)) * 100 Train_loss = running_loss / len(train_dataset) Loss = running_loss / len(train_dataset),Īccuracy = (running_corrects.double().cpu().numpy() / len(train_dataset)) * 100 Running_corrects += torch.sum(predicted = labels.data) Running_loss += J.item() * images.size(0) # Compute model's performance statistics. With tqdm(train_loader, unit = 'batch') as tepoch: Optimizer = optimizer, milestones = ,ĭef train_one_step(model, train_loader, train_dataset): # Decay lr at 20th, 40th, 60th and 75th epochs by a factor of 10. # Initialize an instance of LeNet-5 CNN architecture. # Standard initialization for batch normalization. # Do not initialize bias (due to batchnorm). Self.bn4 = nn.BatchNorm1d(num_features = 84) Self.bn3 = nn.BatchNorm1d(num_features = 120) Self.bn2 = nn.BatchNorm2d(num_features = 16) Self.pool = nn.MaxPool2d(kernel_size = 2, stride = 2) Self.bn1 = nn.BatchNorm2d(num_features = 6) The example code is: class LeNet5(nn.Module):ī = torch.tensor(data = beta, dtype = torch.float32) I am using LeNet-5 CNN as a toy example on MNIST to train ‘beta’ instead of using beta = 1 as present in nn.SiLU(). I am using Swish activation function, with trainable □ parameter according to the paper SWISH: A Self-Gated Activation Function paper by Prajit Ramachandran, Barret Zoph and Quoc V.
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