Learn By Example | How to use RandomNormal initializer to a Deep Learning Model in Keras?
An initializer is a function that sets the initial values of the weights of a deep learning model. The choice of initializer can have a big impact on the performance of the model, as different initializers can lead to different convergence behaviors.
One popular initializer is the RandomNormal initializer, which generates random initial weight values from a normal distribution. This can be useful when you want to break symmetry in your model and make the training process more efficient.
In Keras, a deep learning library for Python, using the RandomNormal initializer is quite easy. First, you will need to import the necessary modules from Keras. Next, you will need to create your model using the Sequential class, which allows you to add layers to your model one by one.
After creating your model, you will need to add a dense layer, which is a type of layer that is fully connected to the previous layer. You can specify the number of neurons in this layer and the activation function to use. The activation function is a mathematical function that is applied to the output of the layer to introduce non-linearity into the model.
To use the RandomNormal initializer, you will need to import the Initializer class from keras.initializers and then create an instance of the RandomNormal class. You can specify the mean and standard deviation of the normal distribution, as well as the seed for reproducibility.
When creating a dense layer, you can use the “kernel_initializer” argument in the layer constructor to set the initializer you just created.
Finally, you will need to compile your model by specifying the optimizer, loss function, and metrics to use. The optimizer is the algorithm used to update the weights of the model based on the cost function. The loss function is the function that measures the difference between the predicted and actual values. The metrics are used to evaluate the performance of the model.
In summary, using the RandomNormal initializer in a deep learning model in Keras is a simple process that involves importing the necessary modules, creating the model, adding a dense layer, creating an instance of the RandomNormal class and specifying the mean, standard deviation, and seed. Using the “kernel_initializer” argument in the layer constructor, the initializer is assigned to the layer and finally the model needs to be compiled by specifying the optimizer, loss function, and metrics.
In this Applied Machine Learning & Data Science Recipe, the reader will find the practical use of applied machine learning and data science in Python & R programming: Learn By Example | How to use RandomNormal initializer to a Deep Learning Model in Keras?
Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.