# Copyright 2021 Google LLC
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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"""Toy regression models."""
from fedjax.core import models
import haiku as hk
import jax.numpy as jnp
import numpy as np
[docs]def create_regression_model() -> models.Model:
"""Creates toy regression model.
Matches the model used in:
Communication-Efficient Agnostic Federated Averaging
Jae Ro, Mingqing Chen, Rajiv Mathews, Mehryar Mohri, Ananda Theertha Suresh
https://arxiv.org/abs/2104.02748
Returns:
Model
"""
def forward_pass(batch):
network = hk.Sequential([hk.Linear(1, with_bias=False)])
return jnp.mean(network(batch['x']))
def train_loss(batch, preds):
return jnp.square(jnp.mean(batch['y']) - preds)
transformed_forward_pass = hk.transform(forward_pass)
sample_batch = {'x': np.zeros((1, 1)), 'y': np.zeros((1,))}
return models.create_model_from_haiku(
transformed_forward_pass=transformed_forward_pass,
sample_batch=sample_batch,
train_loss=train_loss)