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46 lines
1.1 KiB
Markdown
46 lines
1.1 KiB
Markdown
# Normalizing Flow
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Real NVP normalizing flow for density estimation and sampling from [Dinh et al. (2016)](https://arxiv.org/abs/1605.08803), implemented using `mlx`.
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The example is written in a somewhat more object-oriented style than strictly necessary, with an eye towards extension to other use cases that could potentially benefit from the use of distributions and bijectors.
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## Basic usage
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```py
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import mlx.core as mx
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from flows import RealNVP
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model = RealNVP(n_transforms=8, d_params=4, d_hidden=256, n_layers=4)
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x = mx.random.normal(shape=(32, 4))
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# Evaluate log-density
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log_prob = model.log_prob(x=x)
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# Draw samples
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x_samples = model.sample(sample_shape=(32, 4))
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```
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## Running the example
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Install the dependencies:
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```
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pip install -r requirements.txt
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```
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The example can be run with:
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```
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python main.py [--cpu]
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```
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which trains the normalizing flow on the two moons dataset and plots the result in `samples.png`. The optional `--cpu` flag can be used to run the example on the CPU, otherwise it will use the GPU by default.
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For all available options, run:
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```
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python main.py --help
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```
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## Results
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