- Getting started
- Introduction
- Overview of topics
- How to contribute
- General best practices
- Computer Vision tasks
- Classification / Tagging
- Object Detection
- Semantic Segmentation
- Instance Segmentation
- Panoptic Segmentation
- Attribute Prediction
- Model architectures
- ResNet
- Faster R-CNN
- Mask R-CNN
- DeepLabv3+
- U-Net
- FBNetV3
- U-Net++
- Efficient Net
- PAN
- PSPNet
- LinkNet
- FPN
- RetinaNet
- Cascade RCNN
- FBNetV3IS
- FBNetV3OD
- CascadeMask R-CNN
- HybridTask Cascade
- Metrics
- Confusion Matrix
- Intersection over Union (IoU)
- Accuracy
- Hamming score
- Precision
- Recall
- Precision-Recall curve and AUC-PR
- F-beta score
- Average Precision
- mean Average Precision (mAP)
- Average Loss
- Loss
- Cross-Entropy Loss
- Binary Cross-Entropy Loss
- Focal loss
- Bounding Box Regression Loss
- CrossEntropyIoULoss2D
- Solver / Optimizer
- Epsilon Coefficient
- Momentum (SGD)
- Adam
- Adadelta
- Adagrad
- AdaMax
- Adamw
- ASGD
- Base Learning Rate
- Rprop
- RMSprop
- SGD
- Weight Decay
- Training Parameters
- Patience
- Min delta
- Seed
- Batch Size
- Iterations
- Epoch
- Scheduler
- Overview of learning rate schedulers in ML
- ExponentialLR
- CyclicLR
- StepLR
- MultiStepLR
- ReduceLROnPlateau
- CosineAnnealingLR
- Augmentations
- Overview of augmentations in ML
- Horizontal Flip
- Vertical Flip
- Random Crop
- Random Sized Crop
- Rotate
- Resize
- Blur
- Smallest max size
- Center Crop
- Color Jitter
- Gaussian Noise
- Shift Scale Rotate
- Longest max size
- Equalize
- To gray
- Shear
- Mosaic
- Copy Paste
- Extrapolation methods
- Interpolation methods
- Deployment
- Primitive deployment using web frameworks
- Commonly used web frameworks
- Containerized Deployment
- Orchestrated Deployment
- Challenges of Deployment
- Splits
- General Information
- Random
- Stratification
- MP WIKI
- SOLVER / OPTIMIZER
- ADAM
- AMSGRAD VARIANT (ADAM)
AMSgrad Variant (Adam)
While the Adam optimizer, which made use of momentum as well as the RMS prop, was efficient in adjusting the learning rates and finding the optimal solution, we have found that certain convergence issues with it. Research has been able to show that there are simple one dimensional convex functions for which the Adam is not able to converge.
Adam makes use of adaptive gradient and updates the parameters separately. The updates might increase or decrease depending upon the calculated exponential moving average of the gradients. But sometimes, the updates of the the parameters is large which doesn't result in convergence. Citing the paper "On The Convergence of ADAM and beyond",
The key difference between AMSGRAD and ADAM is that it maintains the maximum of all until the present time step and uses this maximum value for normalizing the running average of the gradient instead of in ADAM
Hence, the difference between the AMSgrad and Adam is the calculated second moment vector which is used to update the parameters. To put it simply, AMSgrad uses the maximum second moment up until the iteration to update the parameters.
Performance of ADAM and AMSgrad has been presented on a synthetic function to outline the convergence problem of the ADAM. Source
AMSgrad is code example
Hello, thank you for using the code provided by Hasty. Please note that some code blocks might not be 100% complete and ready to be run as is. This is done intentionally as we focus on implementing only the most challenging parts that might be tough to pick up from scratch. View our code block as a LEGO block - you can’t use it as a standalone solution, but you can take it and add to your system to complement it. If you have questions about using the tool, please get in touch with us to get direct help from the Hasty team.
import torch
# N is batch size; D_in is input dimension;
# H is hidden dimension; D_out is output dimension.
N, D_in, H, D_out = 64, 1000, 100, 10
# Create random Tensors to hold inputs and outputs.
x = torch.randn(N, D_in)
y = torch.randn(N, D_out)
# Use the nn package to define our model and loss function.
model = torch.nn.Sequential(
torch.nn.Linear(D_in, H),
torch.nn.ReLU(),
torch.nn.Linear(H, D_out),
)
loss_fn = torch.nn.MSELoss(reduction='sum')
# Use the optim package to define an Optimizer that will update the weights of
# the model for us. Here we will use Adam; the optim package contains many other
# optimization algorithms. The first argument to the Adam constructor tells the
# optimizer which Tensors it should update.
learning_rate = 1e-4
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate,amsgrad=true)
#setting the amsgrad to be true
#note that we are using Adam in our example
for t in range(500):
# Forward pass: compute predicted y by passing x to the model.
y_pred = model(x)
# Compute and print loss.
loss = loss_fn(y_pred, y)
print(t, loss.item())
# Before the backward pass, use the optimizer object to zero all of the
# gradients for the Tensors it will update (which are the learnable weights
# of the model)
optimizer.zero_grad()
# Backward pass: compute gradient of the loss with respect to model parameters
loss.backward()
# Calling the step function on an Optimizer makes an update to its parameters
optimizer.step()