Transformers for Computer Vision Applications/

...

/

Attention: General Deep Learning Idea

Attention: General Deep Learning Idea

Discover the power of attention mechanisms in deep learning, and understand how they differ from fully connected layers in capturing relationships between features.

We'll cover the following...

Let’s explore attention mechanisms as a general concept in deep learning that can be integrated with various models, whether they possess strong or weak inductive biases. Models with strong inductive biases include recurrent neural networks and others.

Press + to interact
Fully connected layer vs. attention mechanism
Fully connected layer vs. attention mechanism

  • Fully connected: Each output is a nonlinear mapping function of all inputs.

  • Attention mechanism: Each output is a “weighted”, nonlinear function of all inputs.

  • No inductive bias in either case.

When determining how to assign weights in a given context, it's crucial to consider the impact of various inputs on specific outputs. How can one discern which inputs wield more influence on particular outcomes?

Distinguishing attention mechanisms from fully connected layers

In a fully connected layer, each output is a nonlinear transformation of all the inputs. In contrast, attention mechanisms produce outputs as weighted nonlinear functions of all inputs. Neither case incorporates inductive bias or modeling assumptions, which means they lack spatial graph connectivity. One might wonder if attention mechanisms are equivalent to fully connected layers. The answer is no, they’re not the same. The key distinction lies in how importance weights are assigned to inputs.

Compare fully connected and attention layer implementation

Let's go through the code step by step and explain each part.

Press + to interact
import numpy as np
# Toy dataset with 3 input features
input_data = np.array([1.0, 2.0, 3.0])
# Fully connected layer
def fully_connected_layer(input_data, weights):
    return np.dot(input_data, weights)
# Attention mechanism
def attention_mechanism(input_data, attention_weights):
    weighted_input = input_data * attention_weights
    return np.sum(weighted_input)
# Weights for fully connected layer
fc_weights = np.array([0.1, 0.2, 0.3])
# Attention weights for attention mechanism
attention_weights = np.array([0.2, 0.5, 0.3])
# Calculate output using fully connected layer
fc_output = fully_connected_layer(input_data, fc_weights)
# Calculate output using attention mechanism
attention_output = attention_mechanism(input_data, attention_weights)
print("Input Data:", input_data) # Input Data: [1. 2. 3.]
print("Fully Connected Output:", fc_output)
print("Attention Mechanism Output:", attention_output)

Let's break down the code, explain each part, and discuss the output and the difference between the two outputs. ...

Access this course and 1400+ top-rated courses and projects.