Initializing Weight Matrices with [-0.1, 0.1] - Lipontseng

  • Thread starter chamrik
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In summary, initializing weight matrices with values between [-0.1, 0.1] is important in neural networks to prevent issues such as vanishing or exploding gradients. This method is a form of uniform initialization and differs from other methods such as normal, Xavier, and He initialization. While this range is commonly used, it may not be suitable for all networks and datasets. The drawbacks of this method include potential issues with convergence and non-uniform data distributions. Careful consideration and experimentation is necessary to choose an appropriate initialization method for each individual case.
  • #1
chamrik
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0
hi everyone,

can you guys pls help out.

I want to initialise my weight matrices based on the following:

I have 357*8324 inputs


net = newff(minmax(x),[2,3],{'logsig','purelin'},'traingd');

I want to initialise the weight matrices with numbers between
[-0.1,0.1]. how do I go about it?

thanks
Lipontseng
 
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  • #2
What language are you programming in?
 
  • #3


Hi Lipontseng,

Initializing weight matrices with numbers between [-0.1, 0.1] is a common practice in neural network training. To do this, you can use the "initnw" function in MATLAB, which initializes the weight matrices of a neural network with random values within a given range. In your case, you can use the following code:

net = newff(minmax(x), [2,3], {'logsig', 'purelin'}, 'traingd'); % create neural network
net = initnw(net, -0.1, 0.1); % initialize weight matrices with values between -0.1 and 0.1

Alternatively, you can also manually initialize the weight matrices by using the "rand" function in MATLAB, which generates a random matrix of a given size. For example, you can use the following code:

net = newff(minmax(x), [2,3], {'logsig', 'purelin'}, 'traingd'); % create neural network
net.IW{1} = rand(2, 357) * 0.2 - 0.1; % initialize input-to-hidden weights
net.LW{2,1} = rand(3, 2) * 0.2 - 0.1; % initialize hidden-to-output weights

I hope this helps. Happy coding!
 

1. What is the purpose of initializing weight matrices with values between [-0.1, 0.1]?

The purpose of initializing weight matrices with values between [-0.1, 0.1] is to prevent the weights from becoming too large or too small, which can lead to issues such as vanishing or exploding gradients during the training process. By limiting the range of values, we can ensure that the weights are within a reasonable range, making it easier for the model to learn and converge.

2. Why is it important to initialize weight matrices in a neural network?

Initializing weight matrices in a neural network is important because it sets the starting point for the parameters that will be updated during the training process. If the weights are not properly initialized, the model may struggle to learn and converge to a good solution. Additionally, proper initialization can help prevent issues such as vanishing or exploding gradients, which can hinder the training process.

3. How does initializing weight matrices with [-0.1, 0.1] differ from other initialization methods?

Initializing weight matrices with values between [-0.1, 0.1] is a type of uniform initialization, where the weights are randomly sampled from a uniform distribution within the specified range. Other initialization methods include normal initialization, where the weights are randomly sampled from a normal distribution, and Xavier and He initialization, which use specific formulas to scale the weights based on the size of the previous and current layers.

4. Can weight matrices be initialized with values outside of [-0.1, 0.1]?

Yes, weight matrices can be initialized with values outside of the range [-0.1, 0.1]. However, this range is often chosen as a default because it has been found to work well in many cases. Different ranges may be more suitable for specific types of networks or datasets, so it is important to experiment and choose an appropriate range for each individual case.

5. Are there any drawbacks to initializing weight matrices with [-0.1, 0.1]?

One potential drawback of initializing weight matrices with values between [-0.1, 0.1] is that it may not be suitable for all types of networks or datasets. For example, if the weights are initialized too small, it may take longer for the model to learn and converge, while initializing them too large may lead to issues such as exploding gradients. Additionally, this range may not be optimal for non-uniform distributions of data. It is important to choose an appropriate initialization method based on the specific needs of the network and dataset.

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