% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/create_model_set_learning.R
\name{create_model_lstm_cnn_multi_input}
\alias{create_model_lstm_cnn_multi_input}
\title{Create LSTM/CNN network that can process multiple samples for one target}
\usage{
create_model_lstm_cnn_multi_input(
  maxlen = 50,
  dropout_lstm = 0,
  recurrent_dropout_lstm = 0,
  layer_lstm = NULL,
  layer_dense = c(4),
  dropout_dense = NULL,
  solver = "adam",
  learning_rate = 0.001,
  vocabulary_size = 4,
  bidirectional = FALSE,
  batch_size = NULL,
  compile = TRUE,
  kernel_size = NULL,
  filters = NULL,
  strides = NULL,
  pool_size = NULL,
  padding = "same",
  dilation_rate = NULL,
  gap_inputs = NULL,
  use_bias = TRUE,
  zero_mask = FALSE,
  label_smoothing = 0,
  label_noise_matrix = NULL,
  last_layer_activation = "softmax",
  loss_fn = "categorical_crossentropy",
  auc_metric = FALSE,
  f1_metric = FALSE,
  bal_acc = FALSE,
  samples_per_target,
  batch_norm_momentum = 0.99,
  aggregation_method = c("sum"),
  verbose = TRUE,
  model_seed = NULL,
  mixed_precision = FALSE,
  mirrored_strategy = NULL
)
}
\arguments{
\item{maxlen}{Length of predictor sequence.}

\item{dropout_lstm}{Fraction of the units to drop for inputs.}

\item{recurrent_dropout_lstm}{Fraction of the units to drop for recurrent state.}

\item{layer_lstm}{Number of cells per network layer. Can be a scalar or vector.}

\item{layer_dense}{Vector specifying number of neurons per dense layer after last LSTM or CNN layer (if no LSTM used).}

\item{dropout_dense}{Vector of dropout rates between dense layers. No dropout if \code{NULL}.}

\item{solver}{Optimization method, options are \verb{"adam", "adagrad", "rmsprop"} or \code{"sgd"}.}

\item{learning_rate}{Learning rate for optimizer.}

\item{vocabulary_size}{Number of unique character in vocabulary.}

\item{bidirectional}{Use bidirectional wrapper for lstm layers.}

\item{batch_size}{Number of samples that are used for one network update. Only used if \code{stateful = TRUE}.}

\item{compile}{Whether to compile the model.}

\item{kernel_size}{Size of 1d convolutional layers. For multiple layers, assign a vector. (e.g, \code{rep(3,2)} for two layers and kernel size 3)}

\item{filters}{Number of filters. For multiple layers, assign a vector.}

\item{strides}{Stride values. For multiple layers, assign a vector.}

\item{pool_size}{Integer, size of the max pooling windows. For multiple layers, assign a vector.}

\item{padding}{Padding of CNN layers, e.g. \verb{"same", "valid"} or \code{"causal"}.}

\item{dilation_rate}{Integer, the dilation rate to use for dilated convolution.}

\item{gap_inputs}{Global pooling method to apply. Same options as for \code{flatten_method} argument
in \link{create_model_transformer} function.}

\item{use_bias}{Boolean. Usage of bias for CNN layers.}

\item{zero_mask}{Boolean, whether to apply zero masking before LSTM layer. Only used if model does not use any CNN layers.}

\item{label_smoothing}{Float in [0, 1]. If 0, no smoothing is applied. If > 0, loss between the predicted
labels and a smoothed version of the true labels, where the smoothing squeezes the labels towards 0.5.
The closer the argument is to 1 the more the labels get smoothed.}

\item{label_noise_matrix}{Matrix of label noises. Every row stands for one class and columns for percentage of labels in that class.
If first label contains 5 percent wrong labels and second label no noise, then

\code{label_noise_matrix <- matrix(c(0.95, 0.05, 0, 1), nrow = 2, byrow = TRUE )}}

\item{last_layer_activation}{Activation function of output layer(s). For example \code{"sigmoid"} or \code{"softmax"}.}

\item{loss_fn}{Either \code{"categorical_crossentropy"} or \code{"binary_crossentropy"}. If \code{label_noise_matrix} given, will use custom \code{"noisy_loss"}.}

\item{auc_metric}{Whether to add AUC metric.}

\item{f1_metric}{Whether to add F1 metric.}

\item{bal_acc}{Whether to add balanced accuracy.}

\item{samples_per_target}{Number of samples to combine for one target.}

\item{batch_norm_momentum}{Momentum for the moving mean and the moving variance.}

\item{aggregation_method}{At least one of the options \verb{"sum", "mean", "max"}.}

\item{verbose}{Boolean.}

\item{model_seed}{Set seed for model parameters in tensorflow if not \code{NULL}.}

\item{mixed_precision}{Whether to use mixed precision (https://www.tensorflow.org/guide/mixed_precision).}

\item{mirrored_strategy}{Whether to use distributed mirrored strategy. If NULL, will use distributed mirrored strategy only if >1 GPU available.}
}
\value{
A keras model with multiple input layers. Input goes through shared LSTM/CNN layers.
}
\description{
Creates a network consisting of an arbitrary number of CNN, LSTM and dense layers with multiple
input layers. After LSTM/CNN part all representations get aggregated by summation.
Can be used to make single prediction for combination of multiple input sequences.
Implements approach as described \href{https://arxiv.org/abs/1703.06114}{here}
}
\examples{
\dontshow{if (reticulate::py_module_available("tensorflow")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}

# Examples needs keras attached to run 
maxlen <- 50
\donttest{
library(keras)
create_model_lstm_cnn_multi_input(
  maxlen = maxlen,
  vocabulary_size = 4,
  samples_per_target = 7,
  kernel_size = c(10, 10),
  filters = c(64, 128),
  pool_size = c(2, 2),
  layer_lstm = c(32),
  layer_dense = c(64, 2),
  aggregation_method = c("max"),
  learning_rate = 0.001)
 }  
 
\dontshow{\}) # examplesIf}
}