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--- |
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library_name: pytorch |
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tags: |
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- biosignals |
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- testmodel |
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metrics: |
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- validation_loss |
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--- |
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# Model Card for Testmodel |
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- Collection: NeuralLib: Deep Learning Models for Biosignals Processing |
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- Description: This is a Test |
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```json |
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{ |
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"architecture": "GRUseq2seq", |
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"model_name": "ECGPeakDetector", |
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"train_dataset": "private_gib01", |
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"biosignal": "ECG", |
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"sampling_frequency": 360, |
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"task": "peak detection", |
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"gpu_model": "NVIDIA GeForce GTX 1080 Ti", |
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"epochs": 80, |
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"optimizer": "Adam (\nParameter Group 0\n amsgrad: False\n betas: (0.9, 0.999)\n capturable: False\n differentiable: False\n eps: 1e-08\n foreach: None\n fused: None\n initial_lr: 0.001\n lr: 0.001\n maximize: False\n weight_decay: 1e-05\n)", |
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"learning_rate": 0.001, |
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"validation_loss": 0.14879398047924042, |
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"training_time": 11375.492486476898, |
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"retraining": false, |
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"efficiency_flops": 0, |
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"efficiency_params": 0 |
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} |
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## Hyperparameters |
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bidirectional: true |
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dropout: 0 |
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hid_dim: |
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- 32 |
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- 64 |
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- 64 |
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learning_rate: 0.001 |
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model_name: ECGPeakDetector |
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multi_label: true |
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n_features: 1 |
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n_layers: 3 |
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num_classes: 1 |
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task: classification |
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# Example |
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import torch |
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from production_models import Testmodel |
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model = Testmodel() |
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signal = torch.rand(1, 100, 1) # Example input signal |
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predictions = model.predict(signal) |
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print(predictions) |
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