Usage
create_model_wavenet(
filters = 16,
kernel_size = 2,
residual_blocks,
maxlen,
input_tensor = NULL,
initial_kernel_size = 32,
initial_filters = 32,
output_channels = 4,
output_activation = "softmax",
solver = "adam",
learning_rate = 0.001,
compile = TRUE,
verbose = TRUE,
model_seed = NULL,
mixed_precision = FALSE,
mirrored_strategy = NULL
)Arguments
- filters
Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).
- kernel_size
An integer or list of a single integer, specifying the length of the 1D convolution window.
- residual_blocks
if a single integer: the number of residual blocks in the model - the dilation rate of each block `i` is calculated by `2^i`. If a vector, then it's used as the dilation rate of each residual block.
- maxlen
Length of predictor sequence.
- input_tensor
Tensor to use as input for the model. Usually a 3d tensor.
- initial_kernel_size
kernel size of the first causal convolution.
- initial_filters
number of filters in the initial causal convolution.
- output_channels
number of channels in the output.
- output_activation
activation function for the last layer. Default: 'softmax'
- solver
Optimization method, options are
"adam", "adagrad", "rmsprop"or"sgd".- learning_rate
Learning rate for optimizer.
- compile
Whether to compile the model.
- verbose
Boolean.
- 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.
Examples
if (FALSE) { # reticulate::py_module_available("tensorflow")
model <- create_model_wavenet(residual_blocks = 2^rep(1:4, 2), maxlen = 1000)
}