Create GenomeNet Model with Given Architecture Parameters

Usage

create_model_genomenet(
  maxlen = 300,
  learning_rate = 0.001,
  number_of_cnn_layers = 1,
  conv_block_count = 1,
  kernel_size_0 = 16,
  kernel_size_end = 16,
  filters_0 = 256,
  filters_end = 512,
  dilation_end = 1,
  max_pool_end = 1,
  dense_layer_num = 1,
  dense_layer_units = 100,
  dropout_lstm = 0,
  dropout = 0,
  batch_norm_momentum = 0.8,
  leaky_relu_alpha = 0,
  dense_activation = "relu",
  skip_block_fraction = 0,
  residual_block = FALSE,
  reverse_encoding = FALSE,
  optimizer = "adam",
  model_type = "gap",
  recurrent_type = "lstm",
  recurrent_layers = 1,
  recurrent_bidirectional = FALSE,
  recurrent_units = 100,
  vocabulary_size = 4,
  last_layer_activation = "softmax",
  loss_fn = "categorical_crossentropy",
  auc_metric = FALSE,
  num_targets = 2,
  model_seed = NULL,
  bal_acc = FALSE,
  f1_metric = FALSE,
  mixed_precision = FALSE,
  mirrored_strategy = NULL
)

Arguments

maxlen: (integer numeric(1))
Input sequence length.
learning_rate: (numeric(1))
Used by the keras optimizer that is specified by optimizer.
number_of_cnn_layers: (integer numeric(1))
Target number of CNN-layers to use in total. If number_of_cnn_layers is greater than conv_block_count, then the effective number of CNN layers is set to the closest integer that is divisible by conv_block_count.
conv_block_count: (integer numeric(1))
Number of convolutional blocks, into which the CNN layers are divided. If this is greater than number_of_cnn_layers, then it is set to number_of_cnn_layers (the convolutional block size will then be 1).
Convolutional blocks are used when model_type is "gap" (the output of the last conv_block_count * (1 - skip_block_fraction) blocks is fed to global average pooling and then concatenated), and also when residual_block is TRUE (the number of filters is held constant within blocks). If neither of these is the case, conv_block_count has little effect besides the fact that number_of_cnn_layers is set to the closest integer divisible by conv_block_count.
kernel_size_0: (numeric(1))
Target CNN kernel size of the first CNN-layer. Although CNN kernel size is always an integer, this value can be non-integer, potentially affecting the kernel-sizes of intermediate layers (which are geometrically interpolated between kernel_size_0 and kernel_size_end).
kernel_size_end: (numeric(1))
Target CNN kernel size of the last CNN-layer; ignored if only one CNN-layer is used (i.e. if number_of_cnn_layers is 1). Although CNN kernel size is always an integer, this value can be non-integer, potentially affecting the kernel-sizes of intermediate layers (which are geometrically interpolated between kernel_size_0 and kernel_size_end).
filters_0: (numeric(1))
Target filter number of the first CNN-layer. Although CNN filter number is always an integer, this value can be non-integer, potentially affecting the filter-numbers of intermediate layers (which are geometrically interpolated between filters_0 and filters_end).
Note that filters are constant within convolutional blocks when residual_block is TRUE.
filters_end: (numeric(1))
Target filter number of the last CNN-layer; ignored if only one CNN-layer is used (i.e. if number_of_cnn_layers is 1). Although CNN filter number is always an integer, this value can be non-integer, potentially affecting the filter-numbers of intermediate dilation_rates layers (which are geometrically interpolated between kernel_size_0 and kernel_size_end).
Note that filters are constant within convolutional blocks when residual_block is TRUE.
dilation_end: (numeric(1))
Dilation of the last CNN-layer within each block. Dilation rates within each convolutional block grows exponentially from 1 (no dilation) for the first CNN-layer to each block, to this value. Set to 1 (default) to disable dilation.
max_pool_end: (numeric(1))
Target total effective pooling of CNN part of the network. "Effective pooling" here is the product of the pooling rates of all previous CNN-layers. A network with three CNN-layers, all of which are followed by pooling layers of size 2, therefore has effective pooling of 8, with the effective pooling at intermediate positions being 1 (beginning), 2, and 4. Effective pooling after each layer is set to the power of 2 that is, on a logarithmic scale, closest to max_pool_end ^ (<CNN layer number> / <total number of CNN layers>). Therefore, even though the total effective pooling size of the whole CNN part of the network will always be a power of 2, having different, possibly non-integer values of max_pool_end, will still lead to different networks.
dense_layer_num: (integer numeric(1))
number of dense layers at the end of the network, not counting the output layer.
dense_layer_units: (integer numeric(1))
Number of units in each dense layer, except for the output layer.
dropout_lstm: Fraction of the units to drop for inputs.
dropout: (numeric(1))
Dropout rate of dense layers, except for the output layer.
batch_norm_momentum: (numeric(1))
momentum-parameter of layer_batch_normalization layers used in the convolutional part of the network.
leaky_relu_alpha: (numeric(1))
alpha-parameter of the layer_activation_leaky_relu activation layers used in the convolutional part of the network.
dense_activation: (character(1))
Which activation function to use for dense layers. Should be one of "relu", "sigmoid", or "tanh".
skip_block_fraction: (numeric(1))
What fraction of the first convolutional blocks to skip. Only used when model_type is "gap".
residual_block: (logical(1))
Whether to use residual layers in the convolutional part of the network.
reverse_encoding: (logical(1))
Whether the network should have a second input for reverse-complement sequences.
optimizer: (character(1))
Which optimizer to use. One of "adam", "adagrad", "rmsprop", or "sgd".
model_type: (character(1))
Whether to use the global average pooling ("gap") or recurrent ("recurrent") model type.
recurrent_type: (character(1))
Which recurrent network type to use. One of "lstm" or "gru". Only used when model_type is "recurrent".
recurrent_layers: (integer numeric(1))
Number of recurrent layers. Only used when model_type is "recurrent".
recurrent_bidirectional: (logical(1))
Whether to use bidirectional recurrent layers. Only used when model_type is "recurrent".
recurrent_units: (integer numeric(1))
Number of units in each recurrent layer. Only used when model_type is "recurrent".
vocabulary_size: (integer numeric(1))
Vocabulary size of (one-hot encoded) input strings. This determines the input tensor shape, together with maxlen.
last_layer_activation: Either "sigmoid" or "softmax".
loss_fn: Either "categorical_crossentropy" or "binary_crossentropy". If label_noise_matrix given, will use custom "noisy_loss".
auc_metric: Whether to add AUC metric.
num_targets: (integer numeric(1))
Number of output units to create.
model_seed: Set seed for model parameters in tensorflow if not NULL.
bal_acc: Whether to add balanced accuracy.
f1_metric: Whether to add F1 metric.
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.

A keras model implementing genomenet architecture.

Examples

if (FALSE) { # reticulate::py_module_available("tensorflow")
model <- create_model_genomenet()
model
}