Create GenomeNet Model with Given Architecture Parameters
Source:R/create_model_genomenet.R
create_model_genomenet.Rd
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 thekeras
optimizer that is specified byoptimizer
.- number_of_cnn_layers
(integer
numeric(1)
)
Target number of CNN-layers to use in total. Ifnumber_of_cnn_layers
is greater thanconv_block_count
, then the effective number of CNN layers is set to the closest integer that is divisible byconv_block_count
.- conv_block_count
(integer
numeric(1)
)
Number of convolutional blocks, into which the CNN layers are divided. If this is greater thannumber_of_cnn_layers
, then it is set tonumber_of_cnn_layers
(the convolutional block size will then be 1).
Convolutional blocks are used whenmodel_type
is"gap"
(the output of the lastconv_block_count * (1 - skip_block_fraction)
blocks is fed to global average pooling and then concatenated), and also whenresidual_block
isTRUE
(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 thatnumber_of_cnn_layers
is set to the closest integer divisible byconv_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 betweenkernel_size_0
andkernel_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. ifnumber_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 betweenkernel_size_0
andkernel_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 betweenfilters_0
andfilters_end
).
Note that filters are constant within convolutional blocks whenresidual_block
isTRUE
.- filters_end
(
numeric(1)
)
Target filter number of the last CNN-layer; ignored if only one CNN-layer is used (i.e. ifnumber_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 betweenkernel_size_0
andkernel_size_end
).
Note that filters are constant within convolutional blocks whenresidual_block
isTRUE
.- 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 tomax_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 ofmax_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 oflayer_batch_normalization
layers used in the convolutional part of the network.- leaky_relu_alpha
(
numeric(1)
)alpha
-parameter of thelayer_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 whenmodel_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 whenmodel_type
is"recurrent"
.- recurrent_layers
(integer
numeric(1)
)
Number of recurrent layers. Only used whenmodel_type
is"recurrent"
.- recurrent_bidirectional
(
logical(1)
)
Whether to use bidirectional recurrent layers. Only used whenmodel_type
is"recurrent"
.- recurrent_units
(integer
numeric(1)
)
Number of units in each recurrent layer. Only used whenmodel_type
is"recurrent"
.- vocabulary_size
(integer
numeric(1)
)
Vocabulary size of (one-hot encoded) input strings. This determines the input tensor shape, together withmaxlen
.- last_layer_activation
Either
"sigmoid"
or"softmax"
.- loss_fn
Either
"categorical_crossentropy"
or"binary_crossentropy"
. Iflabel_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.