s_TFN: Artificial Neural Network with 'tensorflow' (C, R)

In egenn/rtemis: Machine Learning and Visualization

Artificial Neural Network with tensorflow (C, R)

Description

Train an Artificial Neural Network using keras and tensorflow

Usage

s_TFN(
  x,
  y = NULL,
  x.test = NULL,
  y.test = NULL,
  class.weights = NULL,
  ifw = TRUE,
  ifw.type = 2,
  upsample = FALSE,
  downsample = FALSE,
  resample.seed = NULL,
  net = NULL,
  n.hidden.nodes = NULL,
  initializer = c("glorot_uniform", "glorot_normal", "he_uniform", "he_normal",
    "lecun_uniform", "lecun_normal", "random_uniform", "random_normal",
    "variance_scaling", "truncated_normal", "orthogonal", "zeros", "ones", "constant"),
  initializer.seed = NULL,
  dropout = 0,
  activation = c("relu", "selu", "elu", "sigmoid", "hard_sigmoid", "tanh", "exponential",
    "linear", "softmax", "softplus", "softsign"),
  kernel_l1 = 0.1,
  kernel_l2 = 0,
  activation_l1 = 0,
  activation_l2 = 0,
  batch.normalization = TRUE,
  output = NULL,
  loss = NULL,
  optimizer = c("rmsprop", "adadelta", "adagrad", "adam", "adamax", "nadam", "sgd"),
  learning.rate = NULL,
  metric = NULL,
  epochs = 100,
  batch.size = NULL,
  validation.split = 0.2,
  callback = keras::callback_early_stopping(patience = 150),
  scale = TRUE,
  x.name = NULL,
  y.name = NULL,
  print.plot = FALSE,
  plot.fitted = NULL,
  plot.predicted = NULL,
  plot.theme = rtTheme,
  question = NULL,
  verbose = TRUE,
  outdir = NULL,
  save.mod = ifelse(!is.null(outdir), TRUE, FALSE),
  ...
)

Arguments

x	Numeric vector or matrix / data frame of features i.e. independent variables
y	Numeric vector of outcome, i.e. dependent variable
x.test	Numeric vector or matrix / data frame of testing set features Columns must correspond to columns in x
y.test	Numeric vector of testing set outcome
class.weights	Numeric vector: Class weights for training.
ifw	Logical: If TRUE, apply inverse frequency weighting (for Classification only). Note: If weights are provided, ifw is not used.
ifw.type	Integer 0, 1, 2 1: class.weights as in 0, divided by min(class.weights) 2: class.weights as in 0, divided by max(class.weights)
upsample	Logical: If TRUE, upsample cases to balance outcome classes (for Classification only) Note: upsample will randomly sample with replacement if the length of the majority class is more than double the length of the class you are upsampling, thereby introducing randomness
downsample	Logical: If TRUE, downsample majority class to match size of minority class
resample.seed	Integer: If provided, will be used to set the seed during upsampling. Default = NULL (random seed)
net	Pre-defined keras network to be trained (optional)
n.hidden.nodes	Integer vector: Length must be equal to the number of hidden layers you wish to create. Can be zero, in which case you get a linear model. Default = N of features, i.e. NCOL(x)
initializer	Character: Initializer to use for each layer: "glorot_uniform", "glorot_normal", "he_uniform", "he_normal", "cun_uniform", "lecun_normal", "random_uniform", "random_normal", "variance_scaling", "truncated_normal", "orthogonal", "zeros", "ones", "constant". Glorot is also known as Xavier initialization.
initializer.seed	Integer: Seed to use for each initializer for reproducibility.
dropout	Floar, vector, (0, 1): Probability of dropping nodes. Can be a vector of length equal to N of layers, otherwise will be recycled. Default = 0
activation	String vector: Activation type to use: "relu", "selu", "elu", "sigmoid", "hard_sigmoid", "tanh", "exponential", "linear", "softmax", "softplus", "softsign". Defaults to "relu" for Classification and "tanh" for Regression
kernel_l1	Float: l1 penalty on weights.
kernel_l2	Float: l2 penalty on weights.
activation_l1	Float: l1 penalty on layer output.
activation_l2	Float: l2 penalty on layer output.
batch.normalization	Logical: If TRUE, batch normalize after each hidden layer.
output	Character: Activation to use for output layer. Can be any as in activation. Default = "linear" for Regression, "sigmoid" for binary classification, "softmax" for multiclass
loss	Character: Loss to use: Default = "mean_squared_error" for regression, "binary_crossentropy" for binary classification, "sparse_categorical_crossentropy" for multiclass
optimizer	Character: Optimization to use: "rmsprop", "adadelta", "adagrad", "adam", "adamax", "nadam", "sgd". Default = "rmsprop"
learning.rate	Float: learning rate. Defaults depend on optimizer used and are: ⁠rmsprop = .01, adadelta = 1, adagrad = .01, adamax = .002, adam = .001, nadam = .002, sgd = .1⁠
metric	Character: Metric used for evaluation during train. Default = "mse" for regression, "accuracy" for classification.
epochs	Integer: Number of epochs. Default = 100
batch.size	Integer: Batch size. Default = N of cases
validation.split	Float (0, 1): proportion of training data to use for validation. Default = .2
callback	Function to be called by keras during fitting. Default = keras::callback_early_stopping(patience = 150) for early stopping.
scale	Logical: If TRUE, scale featues before training. column means and standard deviation will be saved in rtMod$extra field to allow scaling ahead of prediction on new data
x.name	Character: Name for feature set
y.name	Character: Name for outcome
print.plot	Logical: if TRUE, produce plot using mplot3 Takes precedence over plot.fitted and plot.predicted.
plot.fitted	Logical: if TRUE, plot True (y) vs Fitted
plot.predicted	Logical: if TRUE, plot True (y.test) vs Predicted. Requires x.test and y.test
plot.theme	Character: "zero", "dark", "box", "darkbox"
question	Character: the question you are attempting to answer with this model, in plain language.
verbose	Logical: If TRUE, print summary to screen.
outdir	Path to output directory. If defined, will save Predicted vs. True plot, if available, as well as full model output, if save.mod is TRUE
save.mod	Logical: If TRUE, save all output to an RDS file in outdir save.mod is TRUE by default if an outdir is defined. If set to TRUE, and no outdir is defined, outdir defaults to paste0("./s.", mod.name)
...	Additional parameters

Details

For more information on arguments and hyperparameters, see (https://keras.rstudio.com/) and (https://keras.io/) It is important to define network structure and adjust hyperparameters based on your problem. You cannot expect defaults to work on any given dataset.

Author(s)

E.D. Gennatas

See Also

train_cv for external cross-validation

Other Supervised Learning: s_AdaBoost(), s_AddTree(), s_BART(), s_BRUTO(), s_BayesGLM(), s_C50(), s_CART(), s_CTree(), s_EVTree(), s_GAM(), s_GAM.default(), s_GAM.formula(), s_GBM(), s_GLM(), s_GLMNET(), s_GLMTree(), s_GLS(), s_H2ODL(), s_H2OGBM(), s_H2ORF(), s_HAL(), s_KNN(), s_LDA(), s_LM(), s_LMTree(), s_LightCART(), s_LightGBM(), s_MARS(), s_MLRF(), s_NBayes(), s_NLA(), s_NLS(), s_NW(), s_PPR(), s_PolyMARS(), s_QDA(), s_QRNN(), s_RF(), s_RFSRC(), s_Ranger(), s_SDA(), s_SGD(), s_SPLS(), s_SVM(), s_XGBoost(), s_XRF()

Other Deep Learning: d_H2OAE(), s_H2ODL()

egenn/rtemis documentation built on May 4, 2024, 7:40 p.m.