使用 Tensorflow 构建 SVM

Question

我目前有两个 numpy 数组：

X - (157, 128) - 157 组 128 个特征
Y - (157) - 特征集分类

这是我为尝试构建这些特征的线性分类模型而编写的代码。

首先，我将数组改编为 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 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 returns 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？

Answer 1

键 self.name 不存在于 column_to_tensors 字典中，这就是错误的意思，self.name 的值是一个空字符串
我认为你在给 tf.estimator.inputs.numpy_input_fn

解决方案可能是将 train_input_fn 行更改为

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

我认为 x 参数必须是一个 numpy 数组，你给它一个字典

我会坚持他们的tutorial不做任何花哨

real_feature_column = real_valued_column(...)
sparse_feature_column = sparse_column_with_hash_bucket(...)

estimator = SVM(
    example_id_column='example_id',
    feature_columns=[real_feature_column, sparse_feature_column],
    l2_regularization=10.0)

# Input builders
def input_fn_train: # returns x, y
    ...
def input_fn_eval: # returns x, y
    ...

estimator.fit(input_fn=input_fn_train)
estimator.evaluate(input_fn=input_fn_eval)
estimator.predict(x=x)

===============已更新==============

我更新的答案将针对您的错误
正如错误所述，self.name 是一个空字符串，并且您要传递给创建 _RealValuedColumn 对象的 infer_real_valued_columns_from_input 的字典中不存在该空字符串
所以我通过调试发现错误是tf.contrib.learn.infer_real_valued_columns_from_input(X)你传递的X必须是一个字典，这样_RealValuedColumn对象的self.name被初始化您传递的字典的键

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)

现在这消除了上面的错误，但它给出了一个新的错误TypeError: Input 'input' of 'SdcaFprint' Op has type int64 that does not match expected type of string.
希望你删除你的 down-vote

Answer 2

这是一个不会引发错误的 SVM 用法示例：

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 估计器的示例 need string IDs。您可能可以替换回 infer_real_valued_columns_from_input，但您需要向它传递一个字典，以便它为该列选择正确的名称。在这种情况下，自己构建特征列在概念上更简单。

使用 Tensorflow 构建 SVM

Building an SVM with Tensorflow

python

machine-learning

svm

tensorflow