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Adagrad, short for Adaptive Gradient Algorithm, is an optimization algorithm that adapts the learning rate of each parameter based on the historical gradient information. It was proposed by Duchi et al. in 2011 and is commonly used in training deep neural networks.
Here are some key concepts related to the Adagrad optimizer:
$$\theta_{t+1,i} = \theta_{t,i} - \frac{\alpha}{\sqrt{G_{t,ii}+\epsilon}} \cdot g_{t,i}$$
where:
Adagrad has several advantages and disadvantages:
Here is an example of how Adagrad can be implemented in Python using NumPy:
```python import numpy as np class AdagradOptimizer: def __init__(self, learning_rate=0.01, epsilon=1e-8): self.learning_rate = learning_rate self.epsilon = epsilon self.gradient_squared = None def update(self, params, gradients): if self.gradient_squared is None: self.gradient_squared = np.zeros_like(params) self.gradient_squared += gradients ** 2 params -= self.learning_rate / (np.sqrt(self.gradient_squared) + self.epsilon) * gradients ```
In this implementation, the `AdagradOptimizer` class keeps track of the accumulated squared gradients and updates the parameters using the Adagrad update rule.
Here is an example of how to use the `AdagradOptimizer` class to optimize a simple function:
```python # Define the function to optimize def f(x): return x ** 2 # Initialize the optimizer optimizer = AdagradOptimizer(learning_rate=0.1) # Optimize the function x = 10.0 for _ in range(100): gradient = 2 * x optimizer.update(x, gradient) x = x - optimizer.learning_rate / (np.sqrt(optimizer.gradient_squared) + optimizer.epsilon) * gradient print("Opt.