wehub-resource-sync
82 lines
2.5 KiB
Python
82 lines
2.5 KiB
Python
import numpy as np
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import paddle
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import paddle.nn.functional as F
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from paddle.nn import Linear
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from sklearn import preprocessing
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from sklearn.datasets import load_diabetes
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from sklearn.model_selection import train_test_split
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import mlflow.paddle
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def load_data():
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X, y = load_diabetes(return_X_y=True)
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min_max_scaler = preprocessing.MinMaxScaler()
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X_min_max = min_max_scaler.fit_transform(X)
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X_normalized = preprocessing.scale(X_min_max, with_std=False)
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X_train, X_test, y_train, y_test = train_test_split(
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X_normalized, y, test_size=0.2, random_state=42
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)
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y_train = y_train.reshape(-1, 1)
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y_test = y_test.reshape(-1, 1)
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return np.concatenate((X_train, y_train), axis=1), np.concatenate((X_test, y_test), axis=1)
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class Regressor(paddle.nn.Layer):
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def __init__(self):
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super().__init__()
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self.fc = Linear(in_features=13, out_features=1)
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@paddle.jit.to_static
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def forward(self, inputs):
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x = self.fc(inputs)
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return x
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if __name__ == "__main__":
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model = Regressor()
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model.train()
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training_data, test_data = load_data()
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opt = paddle.optimizer.SGD(learning_rate=0.01, parameters=model.parameters())
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EPOCH_NUM = 10
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BATCH_SIZE = 10
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for epoch_id in range(EPOCH_NUM):
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np.random.shuffle(training_data)
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mini_batches = [
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training_data[k : k + BATCH_SIZE] for k in range(0, len(training_data), BATCH_SIZE)
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]
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for iter_id, mini_batch in enumerate(mini_batches):
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x = np.array(mini_batch[:, :-1]).astype("float32")
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y = np.array(mini_batch[:, -1:]).astype("float32")
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house_features = paddle.to_tensor(x)
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prices = paddle.to_tensor(y)
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predicts = model(house_features)
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loss = F.square_error_cost(predicts, label=prices)
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avg_loss = paddle.mean(loss)
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if iter_id % 20 == 0:
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print(f"epoch: {epoch_id}, iter: {iter_id}, loss is: {avg_loss.numpy()}")
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avg_loss.backward()
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opt.step()
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opt.clear_grad()
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with mlflow.start_run() as run:
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mlflow.log_param("learning_rate", 0.01)
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mlflow.paddle.log_model(model, name="model")
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print(f"Model saved in run {mlflow.active_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(test_data).astype("float32")
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print(pd_model(np_test_data[:, :-1]))
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