Setup of a generative moment matching network (GMMN) model.
GMMN_model(dim, activation = c(rep("relu", length(dim) - 2), "sigmoid"),
batch.norm = FALSE, dropout.rate = 0, nGPU = 0, ...)
numeric
vector of length at least two, giving
the dimensions of the input layer, the hidden layer(s) (if any) and
the output layer (in this order).
character
vector of length
length(dim) - 1
specifying the activation functions
for all hidden layers and the output layer (in this order);
note that the input layer does not have an activation function.
logical
indicating whether batch
normalization layers are to be added after each hidden layer.
numeric
value in [0,1] specifying
the fraction of input to be dropped; see the rate parameter of
layer_dropout()
. Note that only if positive, dropout
layers are added after each hidden layer.
non-negative integer
specifying the number of GPUs
available if the GPU version of TensorFlow is installed.
If positive, a (special) multiple GPU model for data
parallelism is instantiated. Note that for multi-layer perceptrons
on a few GPUs, this model does not yet yield any scale-up computational
factor (in fact, currently very slightly negative scale-ups are likely due
to overhead costs).
additional arguments passed to loss()
.
GMMN_model()
returns a list with components
model
:GMMN model (a keras object inheriting from
the classes "keras.engine.training.Model"
,
"keras.engine.network.Network"
,
"keras.engine.base_layer.Layer"
and "python.builtin.object"
).
type
:character
string indicating
the type of model ("GMMN"
).
dim
:see above.
activation
:see above.
batch.norm
:see above.
dropout.rate
:see above.
dim.train
:dimension of the training data (NA
unless trained).
batch.size
:batch size (NA
unless trained).
nepoch
:number of epochs (NA
unless trained).
Li, Y., Swersky, K. and Zemel, R. (2015). Generative moment matching networks. Proceedings of Machine Learning Research, 37 (International Conference on Maching Learning), 1718--1727. See http://proceedings.mlr.press/v37/li15.pdf (2019-08-24)
Dziugaite, G. K., Roy, D. M. and Ghahramani, Z. (2015). Training generative neural networks via maximum mean discrepancy optimization. AUAI Press, 258--267. See http://www.auai.org/uai2015/proceedings/papers/230.pdf (2019-08-24)
# NOT RUN {
# to avoid win-builder error "Error: Installation of TensorFlow not found"
## Example model with a 5d input, 300d hidden and 4d output layer
str(GMMN_model(c(5, 300, 4)))
# }
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