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training_configs.yml
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---
ops: [train, evaluate]
model: {
# NOTE: update the values here to train a different model
path: /content/gdrive/My Drive/Colab Notebooks/NeuralNetworksForGWAS/models/cnn-variant.py,
class: DeeperDeepSEA,
class_args: {
sequence_length: 1000,
n_targets: 1,
},
non_strand_specific: mean
}
sampler: !obj:selene_sdk.samplers.IntervalsSampler {
reference_sequence: !obj:selene_sdk.sequences.Genome {
# we include relative paths here, but we recommend using absolute
# paths for future configuration files
input_path: /content/gdrive/My Drive/Colab Notebooks/NeuralNetworksForGWAS/data/male.hg19.fasta
},
features: !obj:selene_sdk.utils.load_features_list {
input_path: /content/gdrive/My Drive/Colab Notebooks/NeuralNetworksForGWAS/data/distinct_features.txt
},
target_path: /content/gdrive/My Drive/Colab Notebooks/NeuralNetworksForGWAS/data/sorted_GM12878_CTCF.bed.gz,
intervals_path: /content/gdrive/My Drive/Colab Notebooks/NeuralNetworksForGWAS/data/deepsea_TF_intervals.txt,
seed: 127,
# A positive example is an 1000bp sequence with at least 1 class/feature annotated to it.
# A negative sample has no classes/features annotated to the sequence.
sample_negative: True,
sequence_length: 1000,
center_bin_to_predict: 200,
test_holdout: [chr8, chr9],
validation_holdout: [chr6, chr7],
# The feature must take up 50% of the bin (200bp) for it to be considered
# a feature annotated to that sequence.
feature_thresholds: 0.5,
mode: train,
save_datasets: [validate, test]
}
train_model: !obj:selene_sdk.TrainModel {
batch_size: 64,
max_steps: 10000, # update this value for longer training
report_stats_every_n_steps: 200,
n_validation_samples: 32000,
n_test_samples: 120000,
cpu_n_threads: 32,
use_cuda: True
}
random_seed: 1447
output_dir: /content/gdrive/My Drive/Colab Notebooks/NeuralNetworksForGWAS/training_outputs
create_subdirectory: True
load_test_set: False