wehub-resource-sync
35 lines
965 B
Python
35 lines
965 B
Python
import numpy as np
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import paddle
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import mlflow.paddle
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train_dataset = paddle.text.datasets.UCIHousing(mode="train")
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eval_dataset = paddle.text.datasets.UCIHousing(mode="test")
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class UCIHousing(paddle.nn.Layer):
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def __init__(self):
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super().__init__()
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self.fc_ = paddle.nn.Linear(13, 1, None)
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def forward(self, inputs):
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pred = self.fc_(inputs)
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return pred
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model = paddle.Model(UCIHousing())
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optim = paddle.optimizer.Adam(learning_rate=0.01, parameters=model.parameters())
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model.prepare(optim, paddle.nn.MSELoss())
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model.fit(train_dataset, epochs=6, batch_size=8, verbose=1)
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with mlflow.start_run() as run:
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mlflow.paddle.log_model(model, name="model")
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print(f"Model saved in run {run.info.run_id}")
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# load model
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model_path = mlflow.get_artifact_uri("model")
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pd_model = mlflow.paddle.load_model(model_path)
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np_test_data = np.array([x[0] for x in eval_dataset])
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print(pd_model(np_test_data))
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