Question

 import graphlab as gl
 from graphlab import mxnet as mx

# Define the network symbol, equivalent to linear regression
 net = mx.symbol.Variable('data')
 net = mx.symbol.FullyConnected(data=net, name='fc1', num_hidden=1)
 net = mx.symbol.LinearRegressionOutput(data=net, name='lr')

# Load data into SFrame and normalize features
 sf = gl.SFrame.read_csv('https://static.turi.com/datasets/regression/houses.csv')
 features = ['tax', 'bedroom', 'bath', 'size', 'lot']
 for f in features:
    sf[f] = sf[f] - sf[f].mean()
    sf[f] = sf[f] / sf[f].std()

# Prepare the input iterator from SFrame
# `data_name` must match the first layer's name of the network.
# `label_name` must match the last layer's name plus "_label".
 dataiter = mx.io.SFrameIter(sf, data_field=features, label_field='price',
                        data_name='data', label_name='lr_label',
                        batch_size=1)

# Train the network
 model = mx.model.FeedForward.create(symbol=net, X=dataiter, num_epoch=20,
                                learning_rate=1e-2,
                                eval_metric='rmse')

# Make prediction
 model.predict(dataiter)

enter image description here

我编写了几行代码来预测数据集中的参数，但它只为列车数据提供了RMSE，如图所示。有什么方法可以显示测试数据的RMSE？ model.evaluate(dataiter)不起作用，需要帮助

Answer 1

mx.model已被mx.module取代，最终mx.gluon接口是首选。使用mx.module时，您可以使用eval_metric指定评估指标，使用eval_data指定评估数据（应该是DataIter）。例子类似于;

mod = mx.mod.Module(symbol=net,
                    context=mx.cpu(),
                    data_names=['data'],
                    label_names=['softmax_label'])

mod.fit(train_data=train_iter,
        eval_data=val_iter,
        eval_metric='rmse',
        num_epoch=10)

这将为您提供输出中所需的指标;

INFO:root:Epoch[0] Train-RMSE=0.364625
INFO:root:Epoch[0] Time cost=0.388
INFO:root:Epoch[0] Validation-RMSE=0.557250
...

如何在mxnet graphlab中预测测试rmse？ model.evaluate（dataiter）不起作用

1 个答案: