Different Learning Rate For A Specific Layer Pytorch Forums

I want to change the learning rate of only one layer of my neural nets to a smaller value. I am aware that one can have per-layer learning rate according to this: https://pytorch.org/docs/0.3.0/optim.html#per-parameter-options However if I have a lot of layers, it is quite tedious to specific learning rate for each of them. Is there a more convenient way to specify one lr for just a specific layer and another lr for all other layers? Many thanks! Yes, as you can see in the example of the docs you’ve linked, model.base.parameters() will use the default learning rate, while the learning rate is explicitly specified for model.classifier.parameters().

In your use case, you could filter out the specific layer and use the same approach. Thanks. So i there a convenient way of filter out a specific layer? Cuz I have been searching for a while and could not fine one It is possible to specify different learning rates for different layers of a PyTorch neural network. I almost never use this technique because the complexity of tuning the additional learning rate parameters usually outweighs the benefit of faster training.

I hadn’t looked at using different learning rates for a long time, so I figured I’d put together a demo to refresh my memory. I used one of my standard synthetic datasets. The goal is to predict a person’s political leaning from sex, age, State, and income. The 240-item tab-delimited raw data looks like: I encoded sex as M = -1, F = 1, and State as Michigan = 100, Nebraska = 010, Oklahoma = 001. I used ordinal encoding on politics: conservative = 0, moderate = 1, liberal = 2 (to sync with my PyTorch implementation), and programmatically encoded as conservative = 100, moderate = 010, liberal = 001.

I normalized the numeric data. I divided age values by 100, and divided income values by 100,000. The resulting encoded and normalized comma-delimited data looks like: I split the data into a 200-item set of training data and a 40-item set of test data. These are the parameters of my Deep Learning model, to the right are their shapes. I want the learning rate of the parameters rho in each layer to be 0.01 initially and 0.001 intially.

How can I do that? I saw other forumns but most just tell about setting different learning rate for specific layers initially. Here are the optimizer and scheduler I’m using You could use the same per-parameter option but could pass the actual parameters manually to the optimizer instead of all parameters from a layer. Can you please show the syntax with few parameters. I saw this documentation but couldn’t figure it out.

Thanks Powered by Discourse, best viewed with JavaScript enabled In the realm of deep learning, the learning rate is a critical hyperparameter that significantly influences the training process of neural networks. A well - chosen learning rate can lead to faster convergence and better generalization, while a poorly selected one may cause the model to diverge or get stuck in local minima. Discriminative learning rates, a technique available in PyTorch, offer a more nuanced approach to setting learning rates. Instead of using a single learning rate for all layers in a neural network, discriminative learning rates allow different layers to have different learning rates.

This is particularly useful when dealing with pre - trained models, as earlier layers often capture general features that should be updated more conservatively, while later layers can be updated more aggressively. In a neural network, different layers learn different types of features. The initial layers typically learn low - level features such as edges and textures, while the later layers learn high - level features specific to the task at hand. When fine - tuning a pre - trained model, it often makes sense to use a lower learning rate for the early layers to preserve the general features they have already learned, and a... In PyTorch, we can set different learning rates for different groups of parameters. Here is a simple example using a pre - trained ResNet model:

In this example, we first split the model's parameters into two groups: one for the early layers (conv1 and bn1) and another for the remaining layers. We then assign a lower learning rate (1e - 4) to the early layers and a higher learning rate (1e - 3) to the later layers. Finally, we create an Adam optimizer with these parameter groups. A common practice is to use a layer - wise learning rate decay. For example, we can assign exponentially decreasing learning rates to successive layers. Communities for your favorite technologies.

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Bring the best of human thought and AI automation together at your work. I am trying to change the learning rate for any arbitrary single layer (which is part of a nn.Sequential block). For example, I use a VGG16 network and wish to control the learning rate of one of the fully connected layers in the classifier. Going by this link: https://pytorch.org/docs/0.3.0/optim.html#per-parameter-options, we can specify the learning rate like this - optim.SGD([ {'params': model.base.parameters()}, {'params': model.classifier.parameters(), 'lr': 1e-3} ], lr=1e-2, momentum=0.9) But here, both base and classifier are entire blocks.

In the VGG16 network for example, I want to change the learning rate for classifier[0] / classifier[3] / classifier[6], which are linear layers. Any ideas as to how that can be accomplished? VGG16 network: VGG( (features): Sequential( (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): ReLU(inplace) (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (3): ReLU(inplace) (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) (5):... In the field of deep learning, the learning rate is a crucial hyperparameter that determines the step size at each iteration while updating the model's parameters during training. A well - chosen learning rate can significantly impact the training process, leading to faster convergence and better model performance. However, using a single learning rate for all layers in a deep neural network may not always be the most effective approach.

This is where the concept of differential learning rates comes in. Differential learning rates allow us to assign different learning rates to different layers or groups of layers in a neural network. In this blog, we will explore the fundamental concepts, usage methods, common practices, and best practices of differential learning rates in PyTorch. Deep neural networks often consist of multiple layers with different functions and levels of abstraction. For example, in a convolutional neural network (CNN), the early layers typically learn low - level features such as edges and textures, while the later layers learn high - level features that are more... The early layers may have learned general patterns that are useful across different tasks and datasets, and we may not want to change their parameters too aggressively.

On the other hand, the later layers are more likely to need larger updates to adapt to the specific task at hand. By using differential learning rates, we can fine - tune the training process for each layer or group of layers. In PyTorch, the optimizer is responsible for updating the model's parameters. When initializing an optimizer, we can pass a list of dictionaries, where each dictionary specifies a different group of parameters and the corresponding learning rate. Let's start with a simple example of a neural network with two linear layers. We will assign different learning rates to these two layers.

In this example, we first define a simple neural network with two linear layers. Then we create an optimizer (SGD in this case) and pass a list of dictionaries. Each dictionary specifies a group of parameters (either the parameters of fc1 or fc2) and the corresponding learning rate.

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