---
type: learning
area: learning
status: learning
date: 2026-05-03
created: 2026-05-03
updated: 1980-01-01
tags:
  - learning
---
### 1. **`RandomNormal` and `RandomUniform`**

- **Use Case:**
    
    - These initializers can be used when you want to experiment with custom initialization schemes or when you do not have strong prior knowledge of your data distribution.
    - They are not typically the best choice for deeper networks since they do not account for scaling based on layer dimensions and may result in vanishing or exploding gradients.
- **`RandomNormal`**: Initializes weights with values drawn from a normal distribution (mean=0, stddev=0.05 by default).
    
- **`RandomUniform`**: Initializes weights with values drawn from a uniform distribution between a specified range (usually [-0.05, 0.05] by default).
    
- **Good for:**
    
    - Shallow networks or networks where layers don't need specific scaling.
    - When you want to implement a custom range of initial weights.

### 2. **`Zeros` or `Ones`**

- **Use Case:**
    - These initializers are usually not used for the kernel weights of hidden layers in neural networks. Initializing all weights as zeros (or ones) makes the network symmetric, which prevents effective learning because all neurons would behave identically during training.
- **Good for:**
    - Initializing **bias terms** (rather than weights).
    - Simpler models like linear regression where bias can be initialized to zeros without symmetry issues.

### 3. **`GlorotUniform` (also known as Xavier Uniform Initialization)**

- **Use Case:**
    
    - This is often a **good default initializer** for networks with activations like **sigmoid**, **tanh**, or **softmax**.
    - It helps to maintain the variance of activations and gradients across layers, reducing the chance of vanishing or exploding gradients.
- **Good for:**
    
    - **Deep networks** with **sigmoid** or **tanh** activations.
    - **Classification tasks** with softmax output layers.
    - When you don’t have specific requirements for initialization.

### 4. **`HeNormal` or `HeUniform` (He Initialization)**

- **Use Case:**
    
    - He initialization is specifically designed for layers with **ReLU** (Rectified Linear Units) or its variants (**Leaky ReLU**, **ELU**, etc.).
    - The ReLU activation function can cause dead neurons if the weights are initialized too small, so He initialization compensates by scaling the initialization appropriately.
- **Good for:**
    
    - Networks with **ReLU** or its variants (common in CNNs and deep neural networks).
    - **Deep architectures** where you want to ensure that neurons are appropriately activated.

### 5. **`VarianceScaling`**

- **Use Case:**
    - This is a general-purpose initializer that scales the weights based on the number of input and output units, like Glorot and He initializers, but with more flexibility to set the scaling factor directly.
- **Good for:**
    - When you need more fine-grained control over the scaling behavior.

### 6. **`Orthogonal`**

- **Use Case:**
    - This is useful for layers where maintaining orthogonality between the weights is important, such as in certain types of recurrent neural networks (RNNs).
- **Good for:**
    - **Recurrent networks** (e.g., LSTMs, GRUs) to help with long-term dependency learning.
    - Networks where orthogonality in the weights can improve training stability and performance.

### 7. **`LecunNormal` or `LecunUniform`**

- **Use Case:**
    
    - These initializers are particularly suited for **sigmoid** or **softsign** activation functions, especially in deeper networks.
    - They are derived from the LeCun initialization method which scales the initialization according to the number of input units.
- **Good for:**
    
    - **Sigmoid activations**.
    - Networks where the vanishing gradient problem is a concern, but ReLU is not used.

### Summary of When to Use Each Initializer

- **`RandomNormal`/`RandomUniform`**: Good for small networks or when testing different initialization strategies.
- **`Zeros`/`Ones`**: Avoid for weights; can be useful for bias initialization.
- **`GlorotUniform`**: A good default for networks using activations like sigmoid, tanh, and softmax.
- **`HeNormal`/`HeUniform`**: Best for ReLU and its variants, especially in deep networks.
- **`VarianceScaling`**: Flexible initializer for scaling weight initialization based on network size.
- **`Orthogonal`**: Useful for RNNs to preserve gradient flow over time steps.
- **`LecunNormal`/`LecunUniform`**: Optimal for sigmoid or softsign activations.

The choice of initializer affects the convergence and performance of your neural network, so it's essential to select the one that aligns with the characteristics of your network architecture and activation functions.