Compare Initialization Strategies
Problem Statement
Compare the effect of different initialization strategies on a network's first forward pass.
Background
Zero initialization kills the network (all neurons output the same value). Xavier works for Sigmoid/Tanh. Kaiming is best for ReLU networks.
Your Task
The starter code creates three identical models. Initialize them with three different strategies: zeros (both weights and biases), Xavier uniform (weights only), and Kaiming normal for ReLU (weights only). Apply each to its respective model. The forward passes and comparison are pre-filled.
Output Format
Returns a dictionary with "zeros_output_std", "xavier_output_std", "kaiming_output_std", and "zeros_dead".
Example:
None
{'zeros_output_std': 0.0, 'xavier_output_std': 0.1729, 'kaiming_output_std': 0.6439, 'zeros_dead': True}- The function
compare_init_test()creates three copies of a neural network model withtorch.manual_seed(42)for reproducibility. - It initializes each model with different strategies:
model_zeroswith all weights set to 0,model_xavierwith Xavier uniform initialization, andmodel_kaimingwith Kaiming normal initialization. - The input
torch.ones(1, 10)is passed through each model, and the standard deviation of the output is calculated: sincemodel_zeroshas all weights as 0, its output will be 0, resulting in a standard deviation of 0.0 and a "dead" network. - The standard deviations of
model_xavierandmodel_kaimingoutputs are calculated as 0.1729 and 0.6439, respectively, after passing the input through each model and applying the respective initialization strategies.
Constraints:
- Same architecture for all three
- Use torch.manual_seed(42) before each model
- Compare output statistics
Background Knowledge
Introduction to Weight Initialization
Weight initialization is a crucial step in training neural networks. It involves setting the initial values of the model's weights before the training process begins. The choice of initialization strategy can significantly impact the performance of the network. In this problem, we are comparing the effects of different initialization strategies on a network's first forward pass.
Initialization Strategies
There are several initialization strategies, each with its strengths and weaknesses. Zeros initialization sets all weights to 0, which can lead to dead neurons and slow down the training process. Xavier initialization, also known as Glorot initialization, uses a uniform distribution to initialize the weights, which helps to avoid the problem of vanishing gradients. Kaiming initialization, also known as He initialization, uses a normal distribution to initialize the weights, which is suitable for deep networks with ReLU activation functions.
Importance of Initialization
The choice of initialization strategy can affect the convergence rate and stability of the network. A good initialization strategy can help the network to converge faster and avoid issues like dead neurons or exploding gradients. In this problem, we are comparing the effects of different initialization strategies on a network's first forward pass, which can help us understand how the choice of initialization strategy can impact the network's behavior.
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