<jupyter_start><jupyter_text>Original: 92 (33, 59),Conv256: 93 (34, 59),Emb16: 94 (34, 60),Win64: 94 (34, 60)<jupyter_code>model = MalConvBase(16, 4096, 128, 64).to(device)
train(model, train_loader, val_loader, device, "malconv_base_emb16win64", patience=5)
plot_confusion_matrix(model, test_loader, "malconv_base_emb16win64", device)
model = MalConvPlus(16, 4096, 128, 32).to(device)
train(model, train_loader, val_loader, device, "malconv_plus_emb16")
plot_confusion_matrix(model, test_loader, "malconv_plus_emb16", device)
model = RCNN(8, 128, 32, torch.nn.GRU, 256, 1, False, False).to(device)
train(model, train_loader, val_loader, device, "gru_base")
plot_confusion_matrix(model, test_loader, "gru_base", device)
model = RCNN(8, 128, 32, torch.nn.GRU, 256, 1, True, False).to(device)
train(model, train_loader, val_loader, device, "gru_bidir")
plot_confusion_matrix(model, test_loader, "gru_bidir", device)
model = RCNN(8, 128, 32, torch.nn.GRU, 256, 1, False, True).to(device)
train(model, train_loader, val_loader, device, "gru_res")
plot_confusion_matrix(model, test_loader, "gru_res", device)
model = RCNN(8, 128, 64, torch.nn.GRU, 256, 1, False, True).to(device)
train(model, train_loader, val_loader, device, "gru_wind64res")
plot_confusion_matrix(model, test_loader, "gru_win64res", device)
model = RCNN(8, 128, 32, torch.nn.GRU, 128, 1, False, False).to(device)
train(model, train_loader, val_loader, device, "gru_hid128")
plot_confusion_matrix(model, test_loader, "gru_hid128", device)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1495754408883083, 'fraction_numerical': 0.08229915088177661, 'mean_word_length': 8.883870967741936, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '507946', 'license': 'MIT', 'n_tokens_mistral': 683, 'n_tokens_neox': 583, 'n_words': 131, 'path': 'run.ipynb', 'repo_name': 'Dovermore/deep-malware-detection', 'size': '1531'}