テンソル流でAUCを計算するには？

Question

私はTensorflowを使ってバイナリクラシファイアを構築しましたが、今はAUCと精度を使ってクラシファイアを評価したいと思います。

限り精度に関しては、私は簡単に次のように行うことができます。

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")) 

をAUCを計算するとき、私は次のようにします。

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) 

とトレーニングループ中：

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.}) 

私には次の出力（およびエラー）エラーが表示されます：

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() 

私は何が間違っているのか分かりませんし、精度を使用するとコードは正常に動作しますが、AUCを使用するとこのエラーが発生します。 これを修正する方法を理解するには、正しい方向で私にヒントを教えてください。

私の目的は、バイナリ分類器のパフォーマンスをよりよく評価するためにAUCとROCを計算することです。

Answer 1

私はgithubで同じ問題を発見しました。現時点では、tf.contrib.metrics.streaming_auc()を動作させるためには、sess.run(tf.initialize_local_variables())も実行する必要があるようです。彼らはそれに取り組んでいます。

ここでは、あなたがこの問題を解決することができる方法を示す例があります。

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)