# Saving and Loading Models

You may wish to save models so that they can be loaded and run in a later session. The easiest way to do this is via BSON.jl.

Save a model:

```
julia> using Flux
julia> model = Chain(Dense(10,5,relu),Dense(5,2),softmax)
Chain(Dense(10, 5, NNlib.relu), Dense(5, 2), NNlib.softmax)
julia> using BSON: @save
julia> @save "mymodel.bson" model
```

Load it again:

```
julia> using Flux
julia> using BSON: @load
julia> @load "mymodel.bson" model
julia> model
Chain(Dense(10, 5, NNlib.relu), Dense(5, 2), NNlib.softmax)
```

Models are just normal Julia structs, so it's fine to use any Julia storage format for this purpose. BSON.jl is particularly well supported and most likely to be forwards compatible (that is, models saved now will load in future versions of Flux).

If a saved model's weights are stored on the GPU, the model will not load later on if there is no GPU support available. It's best to move your model to the CPU with `cpu(model)`

before saving it.

## Saving Model Weights

In some cases it may be useful to save only the model parameters themselves, and rebuild the model architecture in your code. You can use `params(model)`

to get model parameters. You can also use `data.(params)`

to remove tracking.

```
julia> using Flux
julia> model = Chain(Dense(10,5,relu),Dense(5,2),softmax)
Chain(Dense(10, 5, NNlib.relu), Dense(5, 2), NNlib.softmax)
julia> weights = params(model);
julia> using BSON: @save
julia> @save "mymodel.bson" weights
```

You can easily load parameters back into a model with `Flux.loadparams!`

.

```
julia> using Flux
julia> model = Chain(Dense(10,5,relu),Dense(5,2),softmax)
Chain(Dense(10, 5, NNlib.relu), Dense(5, 2), NNlib.softmax)
julia> using BSON: @load
julia> @load "mymodel.bson" weights
julia> Flux.loadparams!(model, weights)
```

The new `model`

we created will now be identical to the one we saved parameters for.

## Checkpointing

In longer training runs it's a good idea to periodically save your model, so that you can resume if training is interrupted (for example, if there's a power cut). You can do this by saving the model in the callback provided to `train!`

.

```
using Flux: throttle
using BSON: @save
m = Chain(Dense(10,5,relu),Dense(5,2),softmax)
evalcb = throttle(30) do
# Show loss
@save "model-checkpoint.bson" model
end
```

This will update the `"model-checkpoint.bson"`

file every thirty seconds.

You can get more advanced by saving a series of models throughout training, for example

`@save "model-$(now()).bson" model`

will produce a series of models like `"model-2018-03-06T02:57:10.41.bson"`

. You could also store the current test set loss, so that it's easy to (for example) revert to an older copy of the model if it starts to overfit.

`@save "model-$(now()).bson" model loss = testloss()`

You can even store optimiser state alongside the model, to resume training exactly where you left off.

```
opt = ADAM()
@save "model-$(now()).bson" model opt
```