mirror of
https://github.com/ml-explore/mlx-examples.git
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19b6167d81
* Implement normalizing flow Real NVP example * Add requirements and basic usage to normalizing flow example * Minor changes to README in normalizing flow example * Remove trailing commas in function arguments for unified formatting in flows example * Fix minor typos, add some annotations * format + nits in README * readme fix * mov, minor changes in main, copywright * remove debug * fix * Simplified class structure in distributions; better code re-use in bijectors * Remove rogue space * change name again * nits --------- Co-authored-by: Awni Hannun <awni@apple.com>
116 lines
3.4 KiB
Python
116 lines
3.4 KiB
Python
# Copyright © 2023-2024 Apple Inc.
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from tqdm import trange
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import numpy as np
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from sklearn import datasets, preprocessing
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import matplotlib.pyplot as plt
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import mlx.core as mx
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import mlx.nn as nn
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import mlx.optimizers as optim
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from flows import RealNVP
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def get_moons_dataset(n_samples=100_000, noise=0.06):
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"""Get two moons dataset with given noise level."""
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x, _ = datasets.make_moons(n_samples=n_samples, noise=noise)
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scaler = preprocessing.StandardScaler()
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x = scaler.fit_transform(x)
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return x
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def main(args):
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x = get_moons_dataset(n_samples=100_000, noise=args.noise)
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model = RealNVP(args.n_transforms, args.d_params, args.d_hidden, args.n_layers)
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mx.eval(model.parameters())
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def loss_fn(model, x):
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return -mx.mean(model(x))
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loss_and_grad_fn = nn.value_and_grad(model, loss_fn)
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optimizer = optim.Adam(learning_rate=args.learning_rate)
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with trange(args.n_steps) as steps:
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for step in steps:
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idx = np.random.choice(x.shape[0], replace=False, size=args.n_batch)
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loss, grads = loss_and_grad_fn(model, mx.array(x[idx]))
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optimizer.update(model, grads)
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mx.eval(model.parameters())
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steps.set_postfix(val=loss)
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# Plot samples from trained flow
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fig, axs = plt.subplots(1, args.n_transforms + 2, figsize=(26, 4))
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cmap = plt.get_cmap("Blues")
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bins = 100
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# Sample from intermediate flow-transformed distributions
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for n_transforms in range(args.n_transforms + 1):
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x_samples = model.sample((100_000, 2), n_transforms=n_transforms)
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axs[n_transforms].hist2d(x_samples[:, 0], x_samples[:, 1], bins=bins, cmap=cmap)
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axs[n_transforms].set_xlim(-2, 2)
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axs[n_transforms].set_ylim(-2, 2)
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axs[n_transforms].set_title(
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f"{n_transforms} transforms" if n_transforms > 0 else "Base distribution"
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)
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axs[n_transforms].set_xticklabels([])
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axs[n_transforms].set_yticklabels([])
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# Plot original data
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axs[-1].hist2d(x[:, 0], x[:, 1], bins=bins, cmap=cmap)
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axs[-1].set_xlim(-2, 2)
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axs[-1].set_ylim(-2, 2)
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axs[-1].set_title("Original data")
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axs[-1].set_xticklabels([])
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axs[-1].set_yticklabels([])
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plt.tight_layout()
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plt.savefig("samples.png")
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if __name__ == "__main__":
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import argparse
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--n_steps", type=int, default=5_000, help="Number of steps to train"
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)
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parser.add_argument("--n_batch", type=int, default=64, help="Batch size")
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parser.add_argument(
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"--n_transforms", type=int, default=6, help="Number of flow transforms"
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)
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parser.add_argument(
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"--d_params", type=int, default=2, help="Dimensionality of modeled distribution"
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)
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parser.add_argument(
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"--d_hidden",
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type=int,
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default=128,
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help="Hidden dimensionality of coupling conditioner",
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)
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parser.add_argument(
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"--n_layers",
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type=int,
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default=4,
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help="Number of layers in coupling conditioner",
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)
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parser.add_argument(
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"--learning_rate", type=float, default=3e-4, help="Learning rate"
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)
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parser.add_argument(
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"--noise", type=float, default=0.06, help="Noise level in two moons dataset"
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)
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parser.add_argument("--cpu", action="store_true")
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args = parser.parse_args()
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if args.cpu:
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mx.set_default_device(mx.cpu)
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main(args)
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