Question

由tensorflow计算的rmse与我手动计算的rmse不匹配。相关代码已粘贴到下面：


# Train a linear regression model.
tf.logging.set_verbosity(tf.logging.INFO)
OUTDIR = 'sample_model_metadata'
import shutil
shutil.rmtree(OUTDIR, ignore_errors=True)
model = tf.estimator.LinearRegressor(feature_columns=make_feature_cols(), model_dir=OUTDIR)
model.train(make_train_input_fn(train_data, num_epochs=1))

#Make predictions on the validation data set.
predictions_vals = np.zeros(len(validation_data))
predictions = model.predict(input_fn = make_train_input_fn(validation_data, 1))
i =0 
for items in predictions:
  predictions_vals[i] = items['predictions'][0]
  i += 1


evaluated_rmse = np.sqrt(mean_squared_error(predictions_vals, validation_data['Y']))
print(evaluated_rmse)

def print_rmse(model, df):
  metrics = model.evaluate(input_fn = make_train_input_fn(df, 1))
  print('RMSE on dataset = {}'.format(np.sqrt(metrics['average_loss'])))

print_rmse(model, validation_data)

Answer 1

i = 0 
for items in predictions:
  predictions_vals[i] = items['predictions'][0]

您要将所有predictions保存到np数组predictions_vals的相同位置，即i = 0。您在这里错过了i += 1！除非您在此处错误地复制了代码。

Answer 2

问题出在我的输入功能上，该功能默认情况下启用了改组功能，导致验证数据混乱，从而导致错误的验证得分。

def make_train_input_fn(df, num_epochs=1, shuffle=True):
    return tf.estimator.inputs.pandas_input_fn(
        x=df,
        y=df['Y'],
        batch_size=128,
        num_epochs=num_epochs,
        shuffle=shuffle,
        queue_capacity=2000)

我确保在进行验证时已关闭改组，这已解决了该问题。

predictions = model.predict(input_fn = make_train_input_fn(validation_data, 1, False))

谢谢

如何在张量流中精确计算RMSE？

2 个答案: