Multivariate LSTM Fully Convolutional Networks for Time Series Classification - MLSTM-FCN/japanese_vowels_model.py at master · hypocker/MLSTM-FCN
parser.add_argument("--model_name", type=str, default='mlstm_fcn', help="模型名称,例如 'model1', 'model2', 'model3'") parser.add_argument("--run_dir", type=str, default='results', help="运行目录,用于保存评估结果") parser.add_argument("--seed", type=int, default=42, help...
To use the MLSTM FCN model :model = generate_model() To use the MALSTM FCN model :model = generate_model_2() To use the LSTM FCN model :model = generate_model_3() To use the ALSTM FCN model :model = generate_model_4()
Multivariate LSTM Fully Convolutional Networks for Time Series Classification - MLSTM-FCN/walk_vs_run_model.py at master · hypocker/MLSTM-FCN
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Multivariate LSTM Fully Convolutional Networks for Time Series Classification - MLSTM-FCN/japanese_vowels_model.py at master · kanesp/MLSTM-FCN
Multivariate LSTM Fully Convolutional Networks for Time Series Classification - MLSTM-FCN/shapes_random_model.py at master · houshd/MLSTM-FCN
Multivariate LSTM Fully Convolutional Networks for Time Series Classification - MLSTM-FCN/README.md at master · titu1994/MLSTM-FCN
To use the MLSTM FCN model :model = generate_model() To use the MALSTM FCN model :model = generate_model_2() To use the LSTM FCN model :model = generate_model_3() To use the ALSTM FCN model :model = generate_model_4()
MLSTM FCN models, from the paper Multivariate LSTM-FCNs for Time Series Classification, augment the squeeze and excitation block with the state of the art univariate time series model, LSTM-FCN and ALSTM-FCN from the paper LSTM Fully Convolutional Networks for Time Series Classification. The code...