nn_embedded_lstm_cnn: Neural Network with a long-term short-term memory and an...
In pchest/simpleNN: Provides Accessable Deep Learning Tools

Description Usage Arguments

This function is a wrapper for a long term short term neural network written using the Keras Package.

nn_embedded_lstm_cnn(Text, Codes, Words = 7000, Seed = 17,
  Train_prop = 0.8, Epochs = 4, Batch = 64, MaxSentencelen = 40,
  WordEmbedDim = 60, ValSplit = 0.1, Dropout_layer_1 = 0.6,
  Dropout_layer_2 = 0.3, Dropout_layer_3 = 0.3, Filter = 48,
  Kernel_size = 5, Pool_size = 4, Units_lstm = 128,
  Metric = "binary_accuracy", Loss = "binary_crossentropy",
  Optimizer = "adam", CM = TRUE, Model = FALSE)

`Text`	The text that will be used as training and test data.
`Codes`	The codes that will be used as outcomes to be predicted by the NN model.
`Words`	The number of top words included in document feature matrixes used as training and testing data.
`Seed`	The seed used in the model. Defaults to 17
`Train_prop`	The proportion of the data used to train the model. The remainder is used as test data.
`Epochs`	The number of epochs used in the NN model.
`Batch`	The number of batches estimated in the NN.
`MaxSentencelen`	All sentences will be truncated to this length to be input into the LSTM model
`WordEmbedDim`	The number of word embedding dimensions to be produced by the LSTM model
`ValSplit`	The validation split of the data used in the training of the LSTM model
`Dropout_layer_1`	A floating variable that determines the rate at which units are dropped in the first layer of the sequential model.
`Dropout_layer_2`	A floating variable bound between 0 and 1. It determines the rate at which units are dropped for the linear tranformation of the inputs.
`Dropout_layer_3`	A floating variable bound between 0 and 1. It determines the fraction of the units to drop for the linear transformation of the recurrent neural network layer.
`Filter`
`Kernel_size`
`Pool_size`
`Units_lstm`	The number of network nodes used in the LSTM layer
`Metric`	Metric used to train algorithm
`Loss`	Metric used to train algorithm
`Optimizer`	Optimizer used to fit model to training data
`CM`	A logical variable that indicates whether a confusion matrix will be output from the function
`Model`	A logical variable that indicates whether the trained model should be included in the output of this function