Created
July 18, 2022 14:58
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| with mlp.run(): | |
| mlp.register_artifact("churndata.csv",df) | |
| mlp.log_params({ | |
| "lr":0.01, | |
| "optimizer":"SGD", | |
| "loss_fuction":"BCEloss" | |
| }) | |
| for epoch in range(epochs): | |
| loss_batch=0 | |
| for batch in range(1000,5000,1000): | |
| optimizer.zero_grad() | |
| train_data=train_x[batch-1000:batch] | |
| output=model(train_data) | |
| loss=criterion(output,train_y[batch-1000:batch].unsqueeze(1)) | |
| loss.backward() | |
| optimizer.step() | |
| loss_batch+=loss.item() | |
| metrics=validate(model,test_x,test_y) | |
| metrics["loss"]=loss_batch | |
| metrics["epoch"]=epoch | |
| mlp.log_metrics_continious(metrics) | |
| mlp.pytorch.register_model("pytorch_example1", model) |
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