create_model_lstm_cnn_target_middle: Create LSTM/CNN network to predict middle part of a sequence

In GenomeNet/deepG: Deep Learning for Genome Sequence Data

Create LSTM/CNN network to predict middle part of a sequence

Description

Creates a network consisting of an arbitrary number of CNN, LSTM and dense layers. Function creates two sub networks consisting each of (optional) CNN layers followed by an arbitrary number of LSTM layers. Afterwards the last LSTM layers get concatenated and followed by one or more dense layers. Last layer is a dense layer. Network tries to predict target in the middle of a sequence. If input is AACCTAAGG, input tensors should correspond to x1 = AACC, x2 = GGAA and y = T.

Usage

create_model_lstm_cnn_target_middle(
  maxlen = 50,
  dropout_lstm = 0,
  recurrent_dropout_lstm = 0,
  layer_lstm = 128,
  solver = "adam",
  learning_rate = 0.001,
  vocabulary_size = 4,
  bidirectional = FALSE,
  stateful = FALSE,
  batch_size = NULL,
  padding = "same",
  compile = TRUE,
  layer_dense = NULL,
  kernel_size = NULL,
  filters = NULL,
  pool_size = NULL,
  strides = NULL,
  label_input = NULL,
  zero_mask = FALSE,
  label_smoothing = 0,
  label_noise_matrix = NULL,
  last_layer_activation = "softmax",
  loss_fn = "categorical_crossentropy",
  num_output_layers = 1,
  f1_metric = FALSE,
  auc_metric = FALSE,
  bal_acc = FALSE,
  verbose = TRUE,
  batch_norm_momentum = 0.99,
  model_seed = NULL,
  mixed_precision = FALSE,
  mirrored_strategy = NULL
)

Arguments

maxlen	Length of predictor sequence.
dropout_lstm	Fraction of the units to drop for inputs.
recurrent_dropout_lstm	Fraction of the units to drop for recurrent state.
layer_lstm	Number of cells per network layer. Can be a scalar or vector.
solver	Optimization method, options are ⁠"adam", "adagrad", "rmsprop"⁠ or "sgd".
learning_rate	Learning rate for optimizer.
vocabulary_size	Number of unique character in vocabulary.
bidirectional	Use bidirectional wrapper for lstm layers.
stateful	Boolean. Whether to use stateful LSTM layer.
batch_size	Number of samples that are used for one network update. Only used if stateful = TRUE.
padding	Padding of CNN layers, e.g. ⁠"same", "valid"⁠ or "causal".
compile	Whether to compile the model.
layer_dense	Vector specifying number of neurons per dense layer after last LSTM or CNN layer (if no LSTM used).
kernel_size	Size of 1d convolutional layers. For multiple layers, assign a vector. (e.g, rep(3,2) for two layers and kernel size 3)
filters	Number of filters. For multiple layers, assign a vector.
pool_size	Integer, size of the max pooling windows. For multiple layers, assign a vector.
strides	Stride values. For multiple layers, assign a vector.
label_input	Integer or NULL. If not NULL, adds additional input layer of label_input size.
zero_mask	Boolean, whether to apply zero masking before LSTM layer. Only used if model does not use any CNN layers.
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.
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 label_noise_matrix <- matrix(c(0.95, 0.05, 0, 1), nrow = 2, byrow = TRUE )
last_layer_activation	Activation function of output layer(s). For example "sigmoid" or "softmax".
loss_fn	Either "categorical_crossentropy" or "binary_crossentropy". If label_noise_matrix given, will use custom "noisy_loss".
num_output_layers	Number of output layers.
f1_metric	Whether to add F1 metric.
auc_metric	Whether to add AUC metric.
bal_acc	Whether to add balanced accuracy.
verbose	Boolean.
batch_norm_momentum	Momentum for the moving mean and the moving variance.
model_seed	Set seed for model parameters in tensorflow if not NULL.
mixed_precision	Whether to use mixed precision (https://www.tensorflow.org/guide/mixed_precision).
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 two input layers. Consists of LSTN, CNN and dense layers.

Examples

create_model_lstm_cnn_target_middle(
  maxlen = 500,
  vocabulary_size = 4,
  kernel_size = c(8, 8, 8),
  filters = c(16, 32, 64),
  pool_size = c(3, 3, 3),
  layer_lstm = c(32, 64),
  layer_dense = c(128, 4),
  learning_rate = 0.001)
 

GenomeNet/deepG documentation built on Dec. 24, 2024, 12:11 p.m.