Tensorflow无效参数:断言失败[标签ID必须<n_classes]

Question

我在使用Python 2.7在Tensorflow 1.3.0中实现DNNClassifier时遇到错误.我从Tensorflow tf.estimator Quickstart教程中获取了示例代码,我想用自己的数据集运行它:3D坐标和10个不同的类(int标签).这是我的实现:

#!/usr/bin/env python
# -*- coding: utf-8 -*-

def ReadLabels(file):
    #load the labels from test file here
    labelFile = open(file, "r")
    Label = labelFile.readlines();
    returnL = [[Label[i][j+1] for j in range(len(Label[0])-3)] for i in range(len(Label))]
    returnLint = list();
    for i in range(len(returnL)):
        tmp = ''
        for j in range(len(returnL[0])):
            tmp += str(returnL[i][j])
        returnLint.append(int(tmp))
    return returnL, returnLint

def NumpyReadBin(file,numcols,type):
    #load the data from binary file here
    import numpy as np
    trainData = np.fromfile(file,dtype=type)
    numrows = len(trainData)/numcols
    #print trainData[0:100]
    result = [[trainData[i+j*numcols] for i in range(numcols)] for j in range(numrows)]
    return result

def TensorflowDNN():
    #load sample dataset
    trainData = NumpyReadBin('data/TrainingData.dat',3,'float32')
    valData = NumpyReadBin('data/ValidationData.dat',3,'float32')
    testData = NumpyReadBin('data/TestingData.dat',3,'float32')
    #load sample labels
    trainL, trainLint = ReadLabels('data/TrainingLabels.txt')
    validateL, validateLint = ReadLabels('data/ValidationLabels.txt')
    testL, testLint = ReadLabels('data/TestingLabels.txt')

    import tensorflow as tf
    import numpy as np

    #get unique labels
    uniqueTrain = set()
    for l in trainLint:
        uniqueTrain.add(l)
    uniqueTrain = list(uniqueTrain)
    numClasses = len(uniqueTrain)
    numDims = len(trainData[0])

    #All features have real-value data
    feature_columns = [tf.feature_column.numeric_column("x", shape=[3])]

    # Build 3 layer DNN with 10, 20, 10 units respectively.
    classifier = tf.estimator.DNNClassifier(feature_columns=feature_columns,
                                              hidden_units=[10, 20, 10],
                                              n_classes=numClasses,
                                              model_dir="../Classification/tmp")

    # Define training inputs
    train_input_fn = tf.estimator.inputs.numpy_input_fn(
                                                x={"x": np.array(trainData)},y=np.array(trainLint),
                                                num_epochs = None, shuffle = True)

    #Train the model
    classifier.train(input_fn = train_input_fn, steps = 2000)

    #Define Validation inputs
    val_input_fn = tf.estimator.inputs.numpy_input_fn(
                                                x={"x": np.array(valData)},y=np.array(validateLint),
                                                num_epochs = 1, shuffle = False)

    # Evaluate accuracy.
    accuracy_score = classifier.evaluate(input_fn=val_input_fn)["accuracy"]
    print("\nTest Accuracy: {0:f}\n".format(accuracy_score))

if __name__ == '__main__':
    TensorflowDNN()

函数RedLabels(...)并将NumpyReadBin(...)我保存的数据集加载到张量中.由于标签是我从文本文件中读取的整数,因此该函数有点奇怪,但我最终得到的是一个带有来自tese标签的整数的数组:[11,12,21,22,23,31,32 ,33,41,42].

但是我无法对任何内容进行分类,因为在调用时classifier.train(input_fn = train_input_fn, steps = 2000),我收到以下错误:

...Traceback and stuff like that...
InvalidArgumentError (see above for traceback): assertion failed: [Label IDs must < n_classes] [Condition x < y did not hold element-wise:x (dnn/head/labels:0) = ] [[21][32][42]...] [y (dnn/head/assert_range/Const:0) = ] [10]
[[Node: dnn/head/assert_range/assert_less/Assert/AssertGuard/Assert = Assert[T=[DT_STRING, DT_STRING, DT_INT64, DT_STRING, DT_INT64], summarize=3, _device="/job:localhost/replica:0/task:0/cpu:0"](dnn/head/assert_range/assert_less/Assert/AssertGuard/Assert/Switch/_117, dnn/head/assert_range/assert_less/Assert/AssertGuard/Assert/data_0, dnn/head/assert_range/assert_less/Assert/AssertGuard/Assert/data_1, dnn/head/assert_range/assert_less/Assert/AssertGuard/Assert/Switch_1/_119, dnn/head/assert_range/assert_less/Assert/AssertGuard/Assert/data_3, dnn/head/assert_range/assert_less/Assert/AssertGuard/Assert/Switch_2/_121)]]

有没有人遇到过这个错误或有想法如何解决它？我想它在某种程度上抱怨我的数据集中标签的类/格式数量,但我知道trainLint包含10个不同的类标签,这就是它的值numClasses.它可能是我trainLint阵列的格式吗？

Answer 1

因此Ishant Mrinal的解决方案指出:

Tensorflow期望整数从0到类的数量作为类标签(range(0, num_classes)),而不是像我的情况那样的"任意"数字.谢谢!:)

...我刚遇到的另一个选项是label_vocabulary在分类器定义中添加一个:

classifier = tf.estimator.DNNClassifier(feature_columns=feature_columns,
                                          hidden_units=[10, 20, 10],
                                          n_classes=numClasses,
                                          model_dir=saveAt,
                                          label_vocabulary=uniqueTrain)

使用此选项,我可以像以前一样定义标签,转换为字符串.