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---
library_name: pytorch
tags:
- biosignals
- testmodel
metrics:
- validation_loss
---
# Model Card for Testmodel
- Collection: NeuralLib: Deep Learning Models for Biosignals Processing
- Description: This is a Test
```json
{
"architecture": "GRUseq2seq",
"model_name": "ECGPeakDetector",
"train_dataset": "private_gib01",
"biosignal": "ECG",
"sampling_frequency": 360,
"task": "peak detection",
"gpu_model": "NVIDIA GeForce GTX 1080 Ti",
"epochs": 80,
"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)",
"learning_rate": 0.001,
"validation_loss": 0.14879398047924042,
"training_time": 11375.492486476898,
"retraining": false,
"efficiency_flops": 0,
"efficiency_params": 0
}
## Hyperparameters
bidirectional: true
dropout: 0
hid_dim:
- 32
- 64
- 64
learning_rate: 0.001
model_name: ECGPeakDetector
multi_label: true
n_features: 1
n_layers: 3
num_classes: 1
task: classification
# Example
import torch
from production_models import Testmodel
model = Testmodel()
signal = torch.rand(1, 100, 1) # Example input signal
predictions = model.predict(signal)
print(predictions)
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