使用Tensorflow构建SVM

sd2nnvve 于 2022-11-16 发布在其他

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我目前有两个numpy数组：

X-（157，128）- 157组要素，每组128个要素
Y-（157）-特性集的分类

这是我编写的代码，试图构建这些特征的线性分类模型。
首先，我将数组调整为Tensorflow数据集：

train_input_fn = tf.estimator.inputs.numpy_input_fn(
    x={"x": X},
    y=Y,
    num_epochs=None,
    shuffle=True)

然后我尝试fit一个SVM模型：

svm = tf.contrib.learn.SVM(
    example_id_column='example_id', # not sure why this is necessary
    feature_columns=tf.contrib.learn.infer_real_valued_columns_from_input(X), # create feature columns (not sure why this is necessary) 
    l2_regularization=0.1)

svm.fit(input_fn=train_input_fn, steps=10)

但这只是返回错误：

WARNING:tensorflow:float64 is not supported by many models, consider casting to float32.
WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpf1mwlR
WARNING:tensorflow:tf.variable_op_scope(values, name, default_name) is deprecated, use tf.variable_scope(name, default_name, values)
Traceback (most recent call last):
  File "/var/www/idmy.team/python/train/classifier.py", line 59, in <module>
    svm.fit(input_fn=train_input_fn, steps=10)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/util/deprecation.py", line 316, in new_func
    return func(*args, **kwargs)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 480, in fit
    loss = self._train_model(input_fn=input_fn, hooks=hooks)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 985, in _train_model
    model_fn_ops = self._get_train_ops(features, labels)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 1201, in _get_train_ops
    return self._call_model_fn(features, labels, model_fn_lib.ModeKeys.TRAIN)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 1165, in _call_model_fn
    model_fn_results = self._model_fn(features, labels, **kwargs)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/estimators/linear.py", line 244, in sdca_model_fn
    features.update(layers.transform_features(features, feature_columns))
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/layers/python/layers/feature_column_ops.py", line 656, in transform_features
    transformer.transform(column)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/layers/python/layers/feature_column_ops.py", line 847, in transform
    feature_column.insert_transformed_feature(self._columns_to_tensors)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/layers/python/layers/feature_column.py", line 1816, in insert_transformed_feature
    input_tensor = self._normalized_input_tensor(columns_to_tensors[self.name])
KeyError: ''

我做错了什么？

tensorflow

来源：https://stackoverflow.com/questions/49465891/building-an-svm-with-tensorflow

2条答案

按热度按时间

svdrlsy41#

下面是一个不会引发错误的SVM使用示例：

import numpy
import tensorflow as tf

X = numpy.zeros([157, 128])
Y = numpy.zeros([157], dtype=numpy.int32)
example_id = numpy.array(['%d' % i for i in range(len(Y))])

x_column_name = 'x'
example_id_column_name = 'example_id'

train_input_fn = tf.estimator.inputs.numpy_input_fn(
    x={x_column_name: X, example_id_column_name: example_id},
    y=Y,
    num_epochs=None,
    shuffle=True)

svm = tf.contrib.learn.SVM(
    example_id_column=example_id_column_name,
    feature_columns=(tf.contrib.layers.real_valued_column(
        column_name=x_column_name, dimension=128),),
    l2_regularization=0.1)

svm.fit(input_fn=train_input_fn, steps=10)

传递给SVM估计器need string IDs的示例。您可能可以用infer_real_valued_columns_from_input来替换，但您需要传递一个字典，以便它为该列选择正确的名称。在这种情况下，从概念上讲，自己构造特征列更简单。

赞(0）回复(0）举报 2022-11-16

wfsdck302#

由于错误消息指出self.name是空字符串，并且该空字符串不存在于您要传递给创建_RealValuedColumn对象的infer_real_valued_columns_from_input的字典中
通过调试错误，我发现您传递的tf.contrib.learn.infer_real_valued_columns_from_input(X)必须是一个字典，因此_RealValuedColumn对象的self.name由您传递的字典的键初始化
这就是我所做的

import tensorflow as tf
  import numpy as np

  X = np.array([[1], [0], [0], [1]])
  Y = np.array([[1], [0], [0], [1]])

  dic = {"x": X}

  train_input_fn = tf.estimator.inputs.numpy_input_fn(
      x=dic,
      y=Y,
      num_epochs=None,
      shuffle=True)

  svm = tf.contrib.learn.SVM(example_id_column='x', feature_columns=tf.contrib.learn.infer_real_valued_columns_from_input(dic), l2_regularization=0.1)

  svm.fit(input_fn=train_input_fn, steps=10)

现在，这将删除上述错误，但会产生新的错误TypeError: Input 'input' of 'SdcaFprint' Op has type int64 that does not match expected type of string.

赞(0）回复(0）举报 2022-11-16

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使用Tensorflow构建SVM

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