Question

我目前有两个numpy数组：

X - （157,128） - 157套128个功能
Y - （157） - 功能集的分类

这是我为编写这些功能的线性分类模型而编写的代码。

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 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: ''

我做错了什么？

Answer 1

这是一个不会引发错误的SVM用法示例：

Imports Microsoft.Office.Interop.Word

传递给SVM Estimator need string IDs的示例。您可以替换回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)，但是您需要将其传递给字典，以便为该列选择正确的名称。在这种情况下，它在概念上更简单，只需自己构建功能列。

Answer 2

self.name字典中的密钥column_to_tensors不存在错误所说的内容，self.name的值为空字符串
我认为你在向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并且不做任何幻想

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)

===============修订==============

我的更新回答将特定于您的错误
由于错误显示self.name是一个空字符串，并且您的字典中没有空字符串，而您传递给创建infer_real_valued_columns_from_input对象的_RealValuedColumn
所以我通过调试错误发现的是，你传递的tf.contrib.learn.infer_real_valued_columns_from_input(X) X必须是一个字典，以便self.name _RealValuedColumn对象的关键字初始化你传递的字典

所以这就是我做的事情

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，这会删除上述错误，但会出现新错误TypeError: Input 'input' of 'SdcaFprint' Op has type int64 that does not match expected type of string.
希望你删除你的投票

使用Tensorflow构建SVM

2 个答案: