使用Tensorflow构建SVM [英] Building an SVM with Tensorflow
问题描述
我目前有两个numpy数组:
I currently have two numpy arrays:
-
X
-(157,128)-157组128个功能 -
Y
-(157)-功能集的分类
X
- (157, 128) - 157 sets of 128 featuresY
- (157) - classifications of the feature sets
这是我为构建这些功能的线性分类模型而编写的代码.
This is the code I have written to attempt to build a linear classification model of these features.
首先,我将数组调整为Tensorflow数据集:
First of all I adapted the arrays to a Tensorflow dataset:
train_input_fn = tf.estimator.inputs.numpy_input_fn(
x={"x": X},
y=Y,
num_epochs=None,
shuffle=True)
然后,我尝试拟合
一个SVM模型:
I then tried to fit
an SVM model:
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)
但这只会返回错误:
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: ''
我在做什么错了?
推荐答案
这是一个不引发错误的SVM使用示例:
Here's an SVM usage example which does not throw an error:
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估计器的示例需要字符串ID .您可能可以用 infer_real_valued_columns_from_input
代替,但是您需要将其传递给字典,以便它为列选择正确的名称.在这种情况下,从概念上讲,只需要自己构造功能列就更简单了.
Examples passed to the SVM Estimator need string IDs. You can probably substitute back infer_real_valued_columns_from_input
, but you would need to pass it a dictionary so it picks up the right name for the column. In this case it's conceptually simpler to just construct the feature column yourself.
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