python train.py --problem=mnist --save_path=./mnist
Command-line flags:
save_path
: If present, the optimizer will be saved to the specified path
every time the evaluation performance is improved.num_epochs
: Number of training epochs.log_period
: Epochs before mean performance and time is reported.evaluation_period
: Epochs before the optimizer is evaluated.evaluation_epochs
: Number of evaluation epochs.problem
: Problem to train on. See Problems section below.num_steps
: Number of optimization steps.unroll_length
: Number of unroll steps for the optimizer.learning_rate
: Learning rate.second_derivatives
: If true
, the optimizer will try to compute second
derivatives through the loss function specified by the problem.python evaluate.py --problem=mnist --optimizer=L2L --path=./mnist
Command-line flags:
optimizer
: Adam
or L2L
.path
: Path to saved optimizer, only relevant if using the L2L
optimizer.learning_rate
: Learning rate, only relevant if using Adam
optimizer.num_epochs
: Number of evaluation epochs.seed
: Seed for random number generation.problem
: Problem to evaluate on. See Problems section below.num_steps
: Number of optimization steps.The training and evaluation scripts support the following problems (see
util.py
for more details):
simple
: One-variable quadratic function.simple-multi
: Two-variable quadratic function, where one of the variables
is optimized using a learned optimizer and the other one using Adam.quadratic
: Batched ten-variable quadratic function.mnist
: Mnist classification using a two-layer fully connected network.cifar
: Cifar10 classification using a convolutional neural network.cifar-multi
: Cifar10 classification using a convolutional neural network,
where two independent learned optimizers are used. One to optimize
parameters from convolutional layers and the other one for parameters from
fully connected layers.New problems can be implemented very easily. You can see in train.py
that
the meta_minimize
method from the MetaOptimizer
class is given a function
that returns the TensorFlow operation that generates the loss function we want
to minimize (see problems.py
for an example).
It's important that all operations with Python side effects (e.g. queue
creation) must be done outside of the function passed to meta_minimize
. The
cifar10
function in problems.py
is a good example of a loss function that
uses TensorFlow queues.
Disclaimer: This is not an official Google product.