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Publish 11 evaluated Astra histories with reproducible checkpoint artifacts (part 53)
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"""A small contextual network reweights hyperedges before exact decoding."""
import numpy as np
import jax
import jax.numpy as jnp
def initialize(seed,input_width,hidden=32):
rng=np.random.default_rng(seed)
return {'w1':jnp.asarray(rng.normal(0,1/np.sqrt(input_width),(input_width,hidden)),jnp.float32),
'b1':jnp.zeros(hidden),'w2':jnp.asarray(rng.normal(0,1/np.sqrt(hidden),(hidden,hidden)),jnp.float32),
'b2':jnp.zeros(hidden),'w3':jnp.asarray(rng.normal(0,.001,(hidden,1)),jnp.float32),'b3':jnp.zeros(1)}
def probabilities(parameters,dets,context_indices,static,base_logits,valid):
observed=dets[:,context_indices].astype(jnp.float32)
features=jnp.concatenate([observed,jnp.broadcast_to(static[None,:,:],(len(dets),*static.shape))],axis=-1)
hidden=jax.nn.silu(features@parameters['w1']+parameters['b1'])
hidden=jax.nn.silu(hidden@parameters['w2']+parameters['b2'])
delta=2*jnp.tanh((hidden@parameters['w3']+parameters['b3'])[...,0])
p=jax.nn.sigmoid(base_logits[None,:]+delta)*valid[None,:]
return p.T