Adam and AdamW step · Ingénierie IA

Adam keeps two running averages for each parameter: the mean of the gradient and the mean of the squared gradient. It divides the first by the square root of the second, so every parameter gets its own step size. AdamW adds weight decay that does not pass through these averages. You write one Adam step as a pure function: it takes the state and returns a new state.

Task

Write two functions.

init_adam_state(params) returns the start state: a dict with "m" and "v" as float zero arrays of the shape of params, and "t" equal to the int 0.
adam_step(params, grads, state, lr, beta1, beta2, epsilon, weight_decay) does one update and returns (new_params, new_state).

One step computes, in this order:

t \leftarrow t + 1

m \leftarrow \beta_1 m + (1 - \beta_1) g, \qquad v \leftarrow \beta_2 v + (1 - \beta_2) g^2

\hat m = \frac{m}{1 - \beta_1^t}, \qquad \hat v = \frac{v}{1 - \beta_2^t}

\theta \leftarrow \theta (1 - \eta \lambda) - \eta \frac{\hat m}{\sqrt{\hat v} + \epsilon}

The counter grows before the bias correction, so the first step uses t = 1. All operations are element by element. The decay factor multiplies the old parameters, as in the AdamW class of the lesson code. With weight_decay = 0 the step is plain Adam.

Input and output

Name	Type	Shape	Meaning
`params`	float array	any shape	the parameters \theta before the step
`grads`	float array	same as `params`	the gradient g
`state`	dict		`"m"` and `"v"`: float arrays of the shape of `params`. `"t"`: int, the number of steps done
`lr`	float	scalar	the learning rate \eta, default `0.001`
`beta1`	float	scalar	default `0.9`
`beta2`	float	scalar	default `0.999`
`epsilon`	float	scalar	default `1e-8`, added after the square root
`weight_decay`	float	scalar	the decoupled decay \lambda, default `0.0`

adam_step returns a tuple. The first item is a float array with the shape of params. The second item is a new dict with the keys "m", "v" and "t".

Constraints

Arrays have at most 100 elements.
Do not change params, grads or the arrays in state. Return new arrays.
Use numpy only.

Examples

python

params = np.array([1.0, -2.0, 0.5])
grads = np.array([0.1, -0.3, 2.0])
state = init_adam_state(params)
params, state = adam_step(params, grads, state, lr=0.1)
params
state

text

array([ 0.90000001, -1.9       ,  0.4       ])
{'m': array([ 0.01, -0.03,  0.2 ]), 'v': array([1.e-05, 9.e-05, 4.e-03]), 't': 1}

python

params, state = adam_step(params, np.array([0.2, 0.1, -1.0]), state, lr=0.1)
params
state

text

array([ 0.80348181, -1.85997815,  0.3733663 ])
{'m': array([ 0.029, -0.017,  0.08 ]), 'v': array([4.999e-05, 9.991e-05, 4.996e-03]), 't': 2}

python

params = np.array([1.0, -2.0, 0.5])
adam_step(params, np.array([0.1, -0.3, 2.0]), init_adam_state(params), lr=0.1, weight_decay=0.5)[0]

text

array([ 0.85000001, -1.8       ,  0.375     ])