如何用tensorflow计算AUC？

我已经使用 Tensorflow 构建了一个二元分类器，现在我想使用 AUC 和准确性来评估分类器。

就准确性而言，我可以轻松地这样做：

X = tf.placeholder('float', [None, n_input])
y = tf.placeholder('float', [None, n_classes])
pred = mlp(X, weights, biases, dropout_keep_prob)
correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

在计算 AUC 时，我使用以下公式：

print(tf.argmax(pred, 1).dtype.name)
print(tf.argmax(pred, 1).dtype.name)

a = tf.cast(tf.argmax(pred, 1),tf.float32)
b = tf.cast(tf.argmax(y,1),tf.float32)

auc = tf.contrib.metrics.streaming_auc(a, b)

在训练循环中：

train_acc = sess.run(accuracy, feed_dict={X: batch_xs, y: batch_ys, dropout_keep_prob:1.})
train_auc = sess.run(auc, feed_dict={X: batch_xs, y: batch_ys, dropout_keep_prob:1.})

这给了我以下输出（和错误）错误：

int64
int64
/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/array_ops.py:1197: VisibleDeprecationWarning: converting an array with ndim > 0 to an index will result in an error in the future
  result_shape.insert(dim, 1)
Net built successfully...

Starting training...

Epoch: 000/300 cost: 0.618990561
Traceback (most recent call last):
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 715, in _do_call
    return fn(*args)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 697, in _run_fn
    status, run_metadata)
  File "/usr/lib/python3.5/contextlib.py", line 66, in __exit__
    next(self.gen)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/errors.py", line 450, in raise_exception_on_not_ok_status
    pywrap_tensorflow.TF_GetCode(status))
tensorflow.python.framework.errors.FailedPreconditionError: Attempting to use uninitialized value auc/false_positives
     [[Node: auc/false_positives/read = Identity[T=DT_FLOAT, _class=["loc:@auc/false_positives"], _device="/job:localhost/replica:0/task:0/cpu:0"](auc/false_positives)]]

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "./mlp_.py", line 152, in <module>
    train_auc = sess.run(auc, feed_dict={X: batch_xs, y: batch_ys, dropout_keep_prob:1.})
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 372, in run
    run_metadata_ptr)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 636, in _run
    feed_dict_string, options, run_metadata)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 708, in _do_run
    target_list, options, run_metadata)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 728, in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors.FailedPreconditionError: Attempting to use uninitialized value auc/false_positives
     [[Node: auc/false_positives/read = Identity[T=DT_FLOAT, _class=["loc:@auc/false_positives"], _device="/job:localhost/replica:0/task:0/cpu:0"](auc/false_positives)]]
Caused by op 'auc/false_positives/read', defined at:
  File "./mlp_.py", line 121, in <module>
    auc = tf.contrib.metrics.streaming_auc(a, b)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/contrib/metrics/python/ops/metric_ops.py", line 718, in streaming_auc
    predictions, labels, thresholds, ignore_mask)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/contrib/metrics/python/ops/metric_ops.py", line 603, in _tp_fn_tn_fp
    false_positives = _create_local('false_positives', shape=[num_thresholds])
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/contrib/metrics/python/ops/metric_ops.py", line 75, in _create_local
    collections=collections)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/variables.py", line 211, in __init__
    dtype=dtype)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/variables.py", line 319, in _init_from_args
    self._snapshot = array_ops.identity(self._variable, name="read")
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/gen_array_ops.py", line 831, in identity
    result = _op_def_lib.apply_op("Identity", input=input, name=name)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/op_def_library.py", line 704, in apply_op
    op_def=op_def)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 2260, in create_op
    original_op=self._default_original_op, op_def=op_def)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 1230, in __init__
    self._traceback = _extract_stack()

我不明白我做错了什么，也不明白为什么当使用准确性时，只有代码运行良好，但当使用 AUC 时，它会抛出此错误。 您能否提示我正确的方向以了解如何解决此问题？

我的目标是计算 AUC 和 ROC，以更好地评估二元分类器的性能。

我在上发现了同样的问题github https://github.com/tensorflow/tensorflow/issues/3971。目前看来你还需要运行sess.run(tf.initialize_local_variables())为了要做tf.contrib.metrics.streaming_auc()工作。他们正在努力。

这里有一个示例演示如何解决此问题：

import tensorflow as tf

a = tf.Variable([0.1, 0.5])
b = tf.Variable([0.2, 0.6])

auc = tf.contrib.metrics.streaming_auc(a, b)

sess = tf.Session()
sess.run(tf.initialize_all_variables())
sess.run(tf.initialize_local_variables()) # try commenting this line and you'll get the error
train_auc = sess.run(auc)

print(train_auc)