Jacob's numpy library for machine learning

📰 Attention: You can now purchase jnumpy-themed apparel here.

Getting started

1. Install from pip or clone locally:

$ pip install jnumpy
# or
$ git clone https://github.com/JacobFV/jnumpy.git
$ cd jnumpy
$ pip install .

2. Import the jnumpy module.

import jnumpy as jnp

Examples

Low-level stuff

import jnumpy as jnp

W = jnp.Var(np.random.randn(5, 3), trainable=True, name='W')
b_const = jnp.Var(np.array([1., 2., 3.]), name='b')  # trainable=False by default

def model(x):
    return x @ W + b_const

def loss(y, y_pred):
    loss = (y - y_pred)**2
    loss = jnp.ReduceSum(loss, axis=1)
    loss = jnp.ReduceSum(loss, axis=0)
    return loss

opt = jnp.SGD(0.01)

for _ in range(10):
    # make up some data
    x = jnp.Var(np.random.randn(100, 5))
    y = jnp.Var(np.random.randn(100, 3))

    # forward pass
    y_pred = model(x)
    loss_val = loss(y, y_pred)

    # backpropagation
    opt.minimize(loss)

Neural networks

import jnumpy as jnp
import jnumpy.nn as jnn

conv_net = jnn.Sequential(
    [
        jnn.Conv2D(32, 3, 2, activation=jnp.Relu),
        jnn.Conv2D(64, 3, 2, activation=jnp.Relu),
        jnn.Flatten(),
        jnn.Dense(512, jnp.Sigm),
        jnn.Dense(1, jnp.Linear),
    ]
)

Reinforcement learning

import jnumpy as jnp
import jnumpy.rl as jrl

shared_encoder = conv_net  # same archiecture as the conv_net above

# agents
agentA_hparams = {...}
agentB_hparams = {...}
agentC_hparams = {...}

# categorical deep Q-network:
#   <q0,q1,..,qn> = dqn(o)
#   a* = arg_i max qi
agentA = jrl.agents.CategoricalDQN(
    num_actions=agentA_hparams['num_actions'],
    encoder=shared_encoder,
    hparams=agentA_hparams,
    name='agentA'
    )

# standard deep Q-network:
#   a* = arg_a max dqn(o, a)
agentB = jrl.agents.RealDQN(
    num_actions=agentB_hparams['num_actions'],
    encoder=shared_encoder,
    hparams=agentB_hparams,
    name='agentB'
    )

# random agent:
#   pick a random action
agentC = jrl.agents.RandomAgent(agentC_hparams['num_actions'], name='agentC')

# init enviroments
train_env = jrl.ParallelEnv(
    batch_size=32,
    env_init_fn=lambda: MyEnv(...),  # `jrl.Environment` subclass. Must have `reset` and `step` methods.
)
dev_env = jrl.ParallelEnv(
    batch_size=8,
    env_init_fn=lambda: MyEnv(...),
)
test_env = jrl.ParallelEnv(
    batch_size=8,
    env_init_fn=lambda: MyEnv(...),
)

# train
trainer = jrl.ParallelTrainer(callbacks=[
    jrl.PrintCallback(['epoch', 'agent', 'collect_reward', 'q_train', 'q_test']),
    jrl.QEvalCallback(eval_on_train=True, eval_on_test=True),
])
trainer.train(
    agents={'agentA': agentA, 'agentB': agentB},
    all_hparams={'agentA': agentA_hparams, 'agentB': agentB_hparams},
    env=train_env,
    test_env=dev_env,
    training_epochs=10,
)

# test
driver = ParallelDriver()
trajs = driver.drive(
    agents={'agentA': agentA, 'agentB': agentB},
    env=test_env
)
per_agent_rewards = {
    agent_name: sum(step.reward for step in traj)
    for agent_name, traj in trajs.items()}
print('cumulative test rewards:', per_agent_rewards)

Limitations and Future Work

Future versions will feature:

• add fit, evaluate, and predict to jnp.Sequential
• recurrent network layers
• static execution graphs allowing breadth-first graph traversal
• more optimizers, metrics, and losses
• io loaders for csv's, images, and models (maybe also for graphs)
• more examples

Also maybe for the future:

• custom backends (i.e.: tensorflow or pytorch instead of numpy)

License

All code in this repository is licensed under the MIT license. No restrictions, but no warranties. See the LICENSE file for details.

Contributing

This is a small project, and I don't plan on growing it much. You are welcome to fork and contribute or email me jacob [dot] valdez [at] limboid [dot] ai if you would like to take over. You can add your name to the copyright if you make a PR or your own branch.

The codebase is kept in only a few files, and I have tried to minimize the use of module prefixes because my CSE 4308/4309/4392 classes require the submissions to be stitched togethor in a single file.