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49 lines
848 B
Python
49 lines
848 B
Python
# Copyright © 2023 Apple Inc.
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import mlx.core as mx
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import time
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num_features = 100
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num_examples = 1_000
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num_iters = 10_000
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lr = 0.1
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# True parameters
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w_star = mx.random.normal((num_features,))
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# Input examples
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X = mx.random.normal((num_examples, num_features))
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# Labels
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y = (X @ w_star) > 0
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# Initialize random parameters
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w = 1e-2 * mx.random.normal((num_features,))
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def loss_fn(w):
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logits = X @ w
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return mx.mean(mx.logaddexp(0.0, logits) - y * logits)
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grad_fn = mx.grad(loss_fn)
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tic = time.time()
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for _ in range(num_iters):
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grad = grad_fn(w)
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w = w - lr * grad
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mx.eval(w)
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toc = time.time()
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loss = loss_fn(w)
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final_preds = (X @ w) > 0
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acc = mx.mean(final_preds == y)
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throughput = num_iters / (toc - tic)
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print(
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f"Loss {loss.item():.5f}, Accuracy {acc.item():.5f} "
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f"Throughput {throughput:.5f} (it/s)"
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)
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