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Why Are He and Xavier Initialization Tied to ReLU and Tanh Activations?
Explore the critical relationship between activation functions and weight initialization strategies in neural networks. Learn why methods like He initialization are designed for ReLU activations and Xavier/Glorot initialization is paired with sigmoid and tanh to prevent exploding or vanishing gradients during training.
Question
Which factor is most critical when choosing weight initialization strategies?
A. The type of optimizer used.
B. The number of output classes.
C. The activation function used in the network.
D. The dataset storage format.
Answer
C. The activation function used in the network.
Explanation
Initialization methods like Xavier or He depend on activation type. The choice of weight initialization is deeply connected to the activation function to ensure that the signal (activations and gradients) propagates properly through the network without vanishing or exploding.
Proper weight initialization is crucial for training deep neural networks effectively. If weights are too small, the signal can shrink as it passes through layers, leading to vanishing gradients. If weights are too large, the signal can grow exponentially, causing exploding gradients. Both scenarios prevent the network from learning.
Different activation functions behave differently and affect the variance of the outputs. Therefore, initialization strategies are specifically designed to counteract the effects of certain activations to keep the signal variance stable across layers.
Xavier (Glorot) Initialization
This method is designed for activation functions that are symmetric around zero and have a mean of zero, such as the sigmoid and tanh functions. It initializes weights from a distribution with a variance that accounts for the number of input and output neurons, keeping the signal variance consistent through the layers when using these specific activations.
He Initialization
This method was developed specifically for the Rectified Linear Unit (ReLU) and its variants (like Leaky ReLU). Since ReLU is not zero-centered and “kills” half of its input (by outputting zero for negative values), it changes the variance of the outputs differently than sigmoid or tanh. He initialization compensates for this by using a different scaling factor that prevents the signal from dying out in deep ReLU networks.
Using the wrong initialization for a given activation can lead to poor convergence and unstable training.
Analysis of Incorrect Options
A. The type of optimizer used: While the optimizer drives learning, it doesn’t directly dictate the initial weight values. The optimizer’s job is to update weights, regardless of their starting point, although a good starting point (from proper initialization) makes the optimizer’s job much easier.
B. The number of output classes: The number of output classes determines the size of the final layer, but it doesn’t influence the initialization strategy for the entire network. Initialization is a layer-by-layer concern based on each layer’s size and activation function.
D. The dataset storage format: The storage format (e.g., TFRecords, CSV, NumPy arrays) is part of the data pipeline and has no bearing on the mathematical principles of initializing network weights.
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