我构建了一个TensorFlow模型,它使用DNNClassifier将输入分为两类.
我的问题是,结果1发生在90-95%以上的时间.因此,TensorFlow为我的所有预测提供了相同的概率.
我试图预测其他结果(例如,结果2的假阳性优于错过可能出现的结果2).我知道在一般的机器学习中,在这种情况下,尝试增加结果2是值得的.
但是,我不知道如何在TensorFlow中执行此操作. documentation暗示它是可能的,但我找不到任何实际外观的例子.有没有人成功地做到了这一点,或者有人知道我在哪里可以找到一些示例代码或彻底解释(我使用的是Python)?
注意:当有人使用TensorFlow的更基本部分而不是估算器时,我看到暴露的权重被操纵.出于维护原因,我需要使用估算器来完成此操作.
解决方法:
tf.estimator.DNNClassifier
构造函数有weight_column参数:
weight_column
: A string or a_NumericColumn
created by
tf.feature_column.numeric_column
defining feature column representing
weights. It is used to down weight or boost examples during training.
It will be multiplied by the loss of the example. If it is a string,
it is used as a key to fetch weight tensor from thefeatures
. If it is
a_NumericColumn
, raw tensor is fetched by keyweight_column.key
, then
weight_column.normalizer_fn
is applied on it to get weight tensor.
weight = tf.feature_column.numeric_column('weight')
...
tf.estimator.DNNClassifier(..., weight_column=weight)
[更新]这是一个完整的工作示例:
import numpy as np
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('mnist', one_hot=False)
train_x, train_y = mnist.train.next_batch(1024)
test_x, test_y = mnist.test.images, mnist.test.labels
x_column = tf.feature_column.numeric_column('x', shape=[784])
weight_column = tf.feature_column.numeric_column('weight')
classifier = tf.estimator.DNNClassifier(feature_columns=[x_column],
hidden_units=[100, 100],
weight_column=weight_column,
n_classes=10)
# Training
train_input_fn = tf.estimator.inputs.numpy_input_fn(x={'x': train_x, 'weight': np.ones(train_x.shape[0])},
y=train_y.astype(np.int32),
num_epochs=None, shuffle=True)
classifier.train(input_fn=train_input_fn, steps=1000)
# Testing
test_input_fn = tf.estimator.inputs.numpy_input_fn(x={'x': test_x, 'weight': np.ones(test_x.shape[0])},
y=test_y.astype(np.int32),
num_epochs=1, shuffle=False)
acc = classifier.evaluate(input_fn=test_input_fn)
print('Test Accuracy: %.3f' % acc['accuracy'])