luz_model_sequential | R Documentation |

`luz`

model composed of a linear stack of layersHelper function to build `luz`

models as a sequential model, by feeding
it a stack of `luz`

layers.

```
luz_model_sequential(...)
```

`...` |
Sequence of modules to be added. |

This step is needed so we can get the activation functions and
layers and neurons architecture easily with `nn2poly:::get_parameters()`

.
Furthermore, this step is also needed to be able to impose the needed
constraints when using the `luz/torch`

framework.

A `nn_sequential`

module.

`add_constraints()`

```
## Not run:
if (requireNamespace("luz", quietly=TRUE)) {
# Create a NN using luz/torch as a sequential model
# with 3 fully connected linear layers,
# the first one with input = 5 variables,
# 100 neurons and tanh activation function, the second
# one with 50 neurons and softplus activation function
# and the last one with 1 linear output.
nn <- luz_model_sequential(
torch::nn_linear(5,100),
torch::nn_tanh(),
torch::nn_linear(100,50),
torch::nn_softplus(),
torch::nn_linear(50,1)
)
nn
# Check that the nn is of class nn_squential
class(nn)
}
## End(Not run)
```

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