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I just read about the Keras weight initializers in here. In the documentation, only different initializers has been introduced. Such as:
model.add(Dense(64, kernel_initializer='random_normal'))
I want to know what is the default weight when I don't specify the kernel_initializer argument.
Is there a way to access it?
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Initializers define the way to set the initial random weights of Keras layers. The keyword arguments used for passing initializers to layers depends on the layer. Usually, it is simply kernel_initializer and bias_initializer : from tensorflow.keras import layers from tensorflow.keras import initializers layer = layers.
Use the get_weights() function to get the weights and biases of the layers before training the model. These are the weights and biases with which the layers will be initialized.
Xavier/Glorot Initialization Xavier/Glorot Initialization often termed as Xavier Uniform Initialization, is suitable for layers where the activation function used is Sigmoid. Xavier/Gorat initialization can be implemented in Keras layers in Python as follows: Python3.
Weight initialization is a procedure to set the weights of a neural network to small random values that define the starting point for the optimization (learning or training) of the neural network model.
Each layer has its own default value for initializing the weights. For most of the layers, such as Dense, convolution and RNN layers, the default kernel initializer is 'glorot_uniform' and the default bias intializer is 'zeros' (you can find this by going to the related section for each layer in the documentation; for example here is the Dense layer doc). You can find the definition of glorot_uniform initializer here in the Keras documentation.
As for accessing the weights of each layer, it has already been answered here.
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