How to use RandomNormal initializer in a Deep Learning Model in Keras

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How to use RandomNormal initializer in a Deep Learning Model in Keras

 

In deep learning, the initializer is a method used to set the initial values of the weights of the neural network. The initial values of the weights play a crucial role in the training process, as they determine how the network will learn from the data. There are different types of initializers available in Keras, one of which is the RandomNormal initializer.

The RandomNormal initializer initializes the weights of the neural network with random values drawn from a normal distribution. This means that the values of the weights will be randomly chosen from a Gaussian distribution with a mean of zero and a standard deviation specified by the user.

To use RandomNormal initializer in a deep learning model in Keras, you first need to import the library, then create a new model using the Sequential() function. Next, you can add layers to the model using the Dense() function, in which you can set the kernel_initializer argument to RandomNormal.

The kernel_initializer argument takes an instance of an initializer class, such as RandomNormal from keras.initializers. The RandomNormal initializer class takes two arguments, mean and stddev, which are the mean and standard deviation of the normal distribution respectively. By default the mean is set to 0 and standard deviation is set to 0.05

Once you have added the RandomNormal initializer to your model, you can then compile and train the model as usual.

In summary, to use RandomNormal initializer in a deep learning model in Keras, you need to import the library, create a new model using the Sequential() function, add layers to the model using the Dense() function, in which you can set the kernel_initializer argument to RandomNormal, and then compile and train the model as usual. The kernel_initializer argument takes an instance of initializer class with two arguments, mean and stddev, to define the mean and standard deviation of the normal distribution respectively.

 

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