python – 增加一个类别

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.

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 the features. If it is
a _NumericColumn, raw tensor is fetched by key weight_column.key, then
weight_column.normalizer_fn is applied on it to get weight tensor.

所以只需添加一个新列并为稀有类填充一些重量：

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'])

python – 增加一个类别

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