Initializing weights in neural networks is a crucial step that can significantly affect the training speed and stability of your machine learning models. TensorFlow, one of the most popular machine learning libraries, offers a variety of initializers. Among these, the zeros_initializer is a simple yet widely used placeholder for beginners or in certain specialized cases. This article will walk you through the usage, best practices, and scenarios where zeros_initializer can be most effectively utilized.
What is zeros_initializer?
The zeros_initializer is a TensorFlow function used to set initial weights of layers in a neural network to zero. This can be particularly useful for initializing biases where you want no activation bottleneck at the beginning of training.
import tensorflow as tf
initializer = tf.zeros_initializer()
The Basics of Using zeros_initializer
Using zeros_initializer is straightforward. Typically, you’ll define it during the construction of a layer in your neural network model. Here's an example of using it in a fully connected dense layer:
model = tf.keras.Sequential([
tf.keras.layers.Dense(10, kernel_initializer=tf.zeros_initializer(), input_shape=(5,))
])
In the example above, a Keras Sequential model is created. A dense layer with 10 neurons is added, and the tf.zeros_initializer() is applied to the kernel (weights) of this layer.
Why Avoid zeros_initializer for Weights?
Although it might seem easy to initialize weights to zero, using zeros_initializer for weights can lead to poor training outcomes. This is because all neurons will behave identically during training, failing to break the symmetry in learning, which is critical for optimization in deep neural networks.
Thus, zero initialization is generally discouraged for dense units' kernel weights. Popular alternatives include glorot_uniform or he_normal, which introduce non-zero initial states.
var_initializer = tf.keras.initializers.GlorotUniform()
Appropriate Use Cases
The best application for zeros_initializer is in the initialization of biases in networks:
layer = tf.keras.layers.Dense(
units=10,
kernel_initializer='random_normal',
bias_initializer=tf.zeros_initializer()
)
In most circumstances, setting the bias initializer to zero is recommended since biases are often used to shape the function of activation in the hidden layers. Zero initializes a baseline for neuron activation that rapidly adjusts according to gradient-descent during training.
Custom Implementation of zeros_initializer
If you prefer to manually define a zeros initializer, the mechanism implemented by tf.zeros_initializer() can be easily replicated:
def custom_zeros_initializer(shape, dtype=None):
return tf.zeros(shape, dtype=dtype)
custom_layer = tf.keras.layers.Dense(
units=10,
kernel_initializer=custom_zeros_initializer
)
The function custom_zeros_initializer directly uses TensorFlow's tf.zeros function to achieve a similar result to the built-in initializer.
Conclusion
While zeros_initializer is a part of TensorFlow's core ML tooling and useful for educational purposes or specific niche cases, using it requires an understanding of neural network dynamics. Avoid its use in kernel weights to prevent poor learning symmetry, yet do not hesitate to employ it thoughtfully for bias initialization where appropriate.
