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---
ops: [train, evaluate]
model: {
path: /absolute/path/to/model/architecture.py,
class: ModelArchitectureClassName,
class_args: {
arg1: val1,
arg2: val2
},
non_strand_specific: mean
}
sampler: !obj:selene_sdk.samplers.IntervalsSampler {
reference_sequence: !obj:selene_sdk.sequences.Genome {
input_path: /path/to/reference_sequence.fa,
blacklist_regions: hg19 # only hg19 and hg38, remove if not applicable
},
target_path: /path/to/tabix/indexed/targets.bed.gz,
features: !obj:selene_sdk.utils.load_features_list {
input_path: /path/to/distinct_features.txt
},
intervals_path: /path/to/intervals.bed,
sample_negative: True, # generate samples with no targets present
seed: 127,
test_holdout: [chr8, chr9], # can also be proportional, e.g. 0.10
validation_holdout: [chr6, chr7], # can also be proportional, e.g. 0.10
sequence_length: 1000,
center_bin_to_predict: 200,
feature_thresholds: 0.5,
mode: train,
save_datasets: [test]
}
train_model: !obj:selene_sdk.TrainModel {
batch_size: 64,
max_steps: 80000,
report_stats_every_n_steps: 16000,
n_validation_samples: 32000,
n_test_samples: 640000,
cpu_n_threads: 32,
use_cuda: True,
data_parallel: True, # multiple GPUs
logging_verbosity: 2,
# if resuming training, replace `False` below with the path to the trained
# model weights file created in a previous training run with Selene
checkpoint_resume: False
}
random_seed: 133
output_dir: /path/to/output_dir
...
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