使用Tensorflow构建SVM
我目前有两个numpy数组:
X
-(157,128)-157组128个功能Y
-(157)-功能集的分类
这是我编写的试图建立这些功能的线性分类模型的代码。
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
建立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: ''
我究竟做错了什么?
-
这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估计器的示例需要字符串ID。您可能可以用back代替
infer_real_valued_columns_from_input
,但是您需要给它传递一个字典,以便它为列选择正确的名称。在这种情况下,从概念上讲,仅自己构造功能列就更简单。