ml-networks 0.2.4

A unified deep learning model building library for PyTorch and JAX with shared dataclass-based configuration

ml-networks

JP | EN

A deep learning model building library supporting both PyTorch and JAX (Flax NNX). Build models across frameworks using a unified Config system.

Documentation | JAX Guide

---

Features

• Dual Framework: PyTorch / JAX (Flax NNX) with a shared Config system
• Vision Models: Encoder, Decoder, ConvNet, ResNet (PixelShuffle/Unshuffle), Vision Transformer
• Generative Models: Conditional UNet (1D/2D) for Diffusion Models
• Distributions: Normal, Categorical, Bernoulli, BSQ Codebook
• Loss Functions: Focal Loss, Charbonnier Loss, Focal Frequency Loss, KL Divergence
• Advanced: HyperNetwork, Contrastive Learning, SpatialSoftmax
• Utilities: Custom activations, optimizers, blosc2 I/O, seed control

Installation

# PyTorch modules only
pip install ml-networks

# With JAX support (Python 3.11+ required)
pip install "ml-networks[jax]"

uv / rye

# uv
uv add ml-networks
uv add "ml-networks[jax]"  # with JAX

# rye
rye add ml-networks
rye add "ml-networks[jax]"  # with JAX

Development version (from GitHub)

pip install git+https://github.com/keio-crl/ml-networks.git

Requirements

	PyTorch backend	JAX backend
Python	>= 3.10	>= 3.11
Framework	PyTorch >= 2.0	JAX >= 0.4.30, Flax >= 0.10.0

Quick Start

MLP

from ml_networks.torch import MLPLayer
from ml_networks import MLPConfig, LinearConfig

cfg = MLPConfig(
    hidden_dim=128,
    n_layers=2,
    output_activation="Tanh",
    linear_cfg=LinearConfig(activation="ReLU", bias=True),
)

mlp = MLPLayer(input_dim=16, output_dim=8, cfg=cfg)
y = mlp(torch.randn(32, 16))  # (32, 8)

Encoder

from ml_networks.torch import Encoder
from ml_networks import ConvNetConfig, ConvConfig, LinearConfig

backbone_cfg = ConvNetConfig(
    channels=[16, 32, 64],
    conv_cfgs=[
        ConvConfig(kernel_size=3, stride=2, padding=1, activation="ReLU"),
        ConvConfig(kernel_size=3, stride=2, padding=1, activation="ReLU"),
        ConvConfig(kernel_size=3, stride=2, padding=1, activation="ReLU"),
    ],
)
fc_cfg = LinearConfig(activation="ReLU", bias=True)

encoder = Encoder(feature_dim=64, obs_shape=(3, 64, 64), backbone_cfg=backbone_cfg, fc_cfg=fc_cfg)
z = encoder(torch.randn(32, 3, 64, 64))  # (32, 64)

Backbone options

from ml_networks import ResNetConfig, ViTConfig, TransformerConfig

# ResNet + PixelUnshuffle
backbone_cfg = ResNetConfig(
    conv_channel=64,
    conv_kernel=3,
    f_kernel=3,
    conv_activation="ReLU",
    out_activation="ReLU",
    n_res_blocks=3,
    scale_factor=2,
    n_scaling=3,
    norm="batch",
    norm_cfg={"affine": True},
)

# Vision Transformer
backbone_cfg = ViTConfig(
    patch_size=8,
    transformer_cfg=TransformerConfig(
        d_model=64,
        nhead=8,
        dim_ff=256,
        n_layers=3,
    ),
)

FC layer options

from ml_networks import MLPConfig, LinearConfig, SpatialSoftmaxConfig, AdaptiveAveragePoolingConfig

fc_cfg = MLPConfig(
    hidden_dim=128,
    n_layers=2,
    output_activation="Tanh",
    linear_cfg=LinearConfig(activation="ReLU", bias=True),
)
fc_cfg = LinearConfig(activation="ReLU", bias=True)
fc_cfg = SpatialSoftmaxConfig(temperature=1.0)
fc_cfg = AdaptiveAveragePoolingConfig()
fc_cfg = None  # output feature map directly

Decoder

from ml_networks.torch import Decoder
from ml_networks import ConvNetConfig, ConvConfig, MLPConfig, LinearConfig

backbone_cfg = ConvNetConfig(
    channels=[64, 32, 16],
    conv_cfgs=[
        ConvConfig(kernel_size=4, stride=2, padding=1, activation="ReLU"),
        ConvConfig(kernel_size=4, stride=2, padding=1, activation="ReLU"),
        ConvConfig(kernel_size=4, stride=2, padding=1, activation="Tanh"),
    ],
)
fc_cfg = MLPConfig(
    hidden_dim=256,
    n_layers=2,
    output_activation="ReLU",
    linear_cfg=LinearConfig(activation="ReLU", bias=True),
)

decoder = Decoder(feature_dim=64, obs_shape=(3, 64, 64), backbone_cfg=backbone_cfg, fc_cfg=fc_cfg)
img = decoder(torch.randn(32, 64))  # (32, 3, 64, 64)

Conditional UNet

from ml_networks.torch import ConditionalUnet2d, ConditionalUnet1d
from ml_networks import UNetConfig, ConvConfig

cfg = UNetConfig(
    channels=[64, 128, 256],
    conv_cfg=ConvConfig(kernel_size=3, padding=1, stride=1, activation="ReLU"),
    has_attn=True,
    nhead=8,
    cond_pred_scale=True,
)

# 2D (images)
net = ConditionalUnet2d(feature_dim=32, obs_shape=(3, 64, 64), cfg=cfg)
out = net(torch.randn(2, 3, 64, 64), cond=torch.randn(2, 32))  # (2, 3, 64, 64)

# 1D (sequences)
net = ConditionalUnet1d(feature_dim=32, obs_shape=(8, 128), cfg=cfg)
out = net(torch.randn(2, 8, 128), cond=torch.randn(2, 32))  # (2, 8, 128)

Distributions

from ml_networks.torch import Distribution, Encoder, stack_dist, cat_dist

dist = Distribution(in_dim=64, dist="normal")
encoder = Encoder(feature_dim=128, obs_shape=(3, 64, 64), backbone_cfg=backbone_cfg, fc_cfg=fc_cfg)

z = encoder(obs)  # (B, 128) — mean & std concatenated
dist_z = dist(z)  # NormalStoch(mean, std, stoch)

# Convert to torch.distributions for KLD computation
torch_dist = dist_z.get_distribution(independent=1)

# Stack / concatenate distribution objects
stacked = stack_dist(dist_list, dim=0)
catted = cat_dist(dist_list, dim=-1)

# Save to disk (blosc2 format)
dist_z.save("reports/")

Loss Functions

from ml_networks.torch import focal_loss, binary_focal_loss, FocalFrequencyLoss, charbonnier

# Focal loss (classification)
loss = focal_loss(logits, labels, gamma=2.0)

# Charbonnier loss (image reconstruction)
loss = charbonnier(pred, target, epsilon=1e-3)

# Focal frequency loss (frequency-domain reconstruction)
ffl = FocalFrequencyLoss(loss_weight=1.0, alpha=1.0)
loss = ffl(pred, target)

JAX Backend

Switch frameworks without changing your configs. JAX modules require rngs at initialization.

from ml_networks.jax import MLPLayer, Encoder, Decoder
from ml_networks import MLPConfig, LinearConfig, ConvNetConfig, ConvConfig
from flax import nnx
import jax.numpy as jnp

cfg = MLPConfig(
    hidden_dim=128,
    n_layers=2,
    output_activation="Tanh",
    linear_cfg=LinearConfig(activation="ReLU", bias=True),
)

rngs = nnx.Rngs(0)
mlp = MLPLayer(input_dim=16, output_dim=8, cfg=cfg, rngs=rngs)
y = mlp(jnp.ones((32, 16)))  # (32, 8)

JAX modules use NHWC format (channels-last). See the JAX Guide for details.

Data I/O (blosc2)

from ml_networks import save_blosc2, load_blosc2

save_blosc2(data, "dataset/image.blosc2")
loaded = load_blosc2("dataset/image.blosc2")

Utilities

from ml_networks.torch import Activation, get_optimizer, torch_fix_seed
from ml_networks import determine_loader

# Custom activations: REReLU, SiGLU, CRReLU, TanhExp, L2Norm
act = Activation("REReLU")

# Optimizer (supports pytorch-optimizer library)
optimizer = get_optimizer(model.parameters(), "Adam", lr=1e-3)

# Reproducibility
torch_fix_seed(42)
loader = determine_loader(dataset, seed=42, batch_size=32, shuffle=True)

Package Structure

ml_networks/
├── config.py          # Shared config classes (PyTorch/JAX)
├── utils.py           # Shared utilities (blosc2 I/O, conv shape calc)
├── callbacks.py       # PyTorch Lightning callbacks
├── torch/             # PyTorch implementation
│   ├── layers.py      # MLP, Conv, Attention, Transformer
│   ├── vision.py      # Encoder, Decoder, ConvNet, ResNet, ViT
│   ├── unet.py        # ConditionalUnet1d/2d
│   ├── distributions.py
│   ├── loss.py
│   ├── activations.py
│   ├── hypernetworks.py
│   ├── contrastive.py
│   └── torch_utils.py
└── jax/               # JAX (Flax NNX) implementation
    ├── layers.py
    ├── vision.py
    ├── unet.py
    ├── distributions.py
    ├── loss.py
    ├── activations.py
    ├── hypernetworks.py
    ├── contrastive.py
    └── jax_utils.py

Development

git clone https://github.com/keio-crl/ml-networks.git
cd ml-networks
pip install -e ".[dev]"

# Quality checks
ruff check .          # Lint
ruff format .         # Format
mypy src/             # Type check
pre-commit run --all-files  # All checks

Versioning

Semantic Versioning (MAJOR.MINOR.PATCH).

python scripts/bump_version.py patch   # 0.1.0 -> 0.1.1
python scripts/bump_version.py minor   # 0.1.0 -> 0.2.0
python scripts/bump_version.py major   # 0.1.0 -> 1.0.0

Or use the Version Bump workflow on GitHub Actions.

CI/CD

Workflow	Trigger	Checks
CI	Push / PR to main, develop	ruff, mypy, pytest, build
Release	Tag push (vX.Y.Z)	Build + GitHub Release
Docs	Push to main	Deploy documentation

Authors

• oakwood-fujiken ([e-mail])
• nomutin ([e-mail])