create_model_twin_network: Create twin network

In GenomeNet/deepG: Deep Learning for Genome Sequence Data

Create twin network

Description

Twin network can be trained to maximize the distance between embeddings of inputs. Implements approach as described here.

Usage

create_model_twin_network(
  maxlen = 50,
  dropout_lstm = 0,
  recurrent_dropout_lstm = 0,
  layer_lstm = NULL,
  layer_dense = c(4),
  dropout_dense = NULL,
  kernel_size = NULL,
  filters = NULL,
  strides = NULL,
  pool_size = NULL,
  solver = "adam",
  learning_rate = 0.001,
  vocabulary_size = 4,
  bidirectional = FALSE,
  compile = TRUE,
  padding = "same",
  dilation_rate = NULL,
  gap_inputs = NULL,
  use_bias = TRUE,
  residual_block = FALSE,
  residual_block_length = 1,
  size_reduction_1Dconv = FALSE,
  zero_mask = FALSE,
  verbose = TRUE,
  batch_norm_momentum = 0.99,
  distance_method = "euclidean",
  last_layer_activation = "sigmoid",
  loss_fn = loss_cl(margin = 1),
  metrics = "acc",
  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.
layer_dense	Vector containing number of neurons per dense layer, before euclidean distance layer.
dropout_dense	Dropout rates between dense layers. No dropout if NULL.
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.
strides	Stride values. For multiple layers, assign a vector.
pool_size	Integer, size of the max pooling windows. For multiple layers, assign a 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.
compile	Whether to compile the model.
padding	Padding of CNN layers, e.g. ⁠"same", "valid"⁠ or "causal".
dilation_rate	Integer, the dilation rate to use for dilated convolution.
gap_inputs	Global pooling method to apply. Same options as for flatten_method argument in create_model_transformer function.
use_bias	Boolean. Usage of bias for CNN layers.
residual_block	Boolean. If true, the residual connections are used in CNN. It is not used in the first convolutional layer.
residual_block_length	Integer. Determines how many convolutional layers (or triplets when size_reduction_1D_conv is TRUE) exist
size_reduction_1Dconv	Boolean. When TRUE, the number of filters in the convolutional layers is reduced to 1/4 of the number of filters of
zero_mask	Boolean, whether to apply zero masking before LSTM layer. Only used if model does not use any CNN layers.
verbose	Boolean.
batch_norm_momentum	Momentum for the moving mean and the moving variance.
distance_method	Either "euclidean" or "cosine".
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".
metrics	Vector or list of metrics.
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 implementing twin network architecture.

Examples

maxlen <- 50

library(keras)
model <- create_model_twin_network(
  maxlen = maxlen,
  layer_dense = 16,
  kernel_size = 12,
  filters = 4,
  pool_size = 3,
  learning_rate = 0.001)
   

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