Instructions to use OneScience-Group/Antibody_deep_learning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- TF-Keras
How to use OneScience-Group/Antibody_deep_learning with TF-Keras:
# Note: 'keras<3.x' or 'tf_keras' must be installed (legacy) # See https://github.com/keras-team/tf-keras for more details. from huggingface_hub import from_pretrained_keras model = from_pretrained_keras("OneScience-Group/Antibody_deep_learning") - Notebooks
- Google Colab
- Kaggle
File size: 1,038 Bytes
fe8e241 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | args <- commandArgs(trailingOnly = FALSE)
script_arg <- grep("^--file=", args, value = TRUE)
if (length(script_arg) > 0) {
script_path <- normalizePath(sub("^--file=", "", script_arg[1]))
setwd(normalizePath(file.path(dirname(script_path), "..")))
}
library(reticulate)
use_python(Sys.getenv("RETICULATE_PYTHON"), required = TRUE)
np <- import("numpy", convert = FALSE)
save_npz <- function(out_file, x_train, y_train, x_test, y_test) {
np$savez_compressed(
out_file,
x_train = np$array(readRDS(x_train), dtype = "float32"),
y_train = np$array(readRDS(y_train), dtype = "float32"),
x_test = np$array(readRDS(x_test), dtype = "float32"),
y_test = np$array(readRDS(y_test), dtype = "float32")
)
}
save_npz("model/CNN/c1_data.npz", "model/CNN/c1_train.RDS", "model/CNN/c1_train_y.RDS", "model/CNN/c1_test.RDS", "model/CNN/c1_test_y.RDS")
save_npz("model/CNN/p1_data.npz", "model/CNN/p1_train.RDS", "model/CNN/p1_train_y.RDS", "model/CNN/p1_test.RDS", "model/CNN/p1_test_y.RDS")
cat("export cnn npz OK\n")
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