如何在TensorFlow中初始化tf.metrics成员？

  • Z时代
  • 2024-01-10
  • 分类：问答

以下是我的项目代码的一部分。如何在TensorFlow中初始化tf.metrics成员？

with tf.name_scope("test_accuracy"): 
    test_mean_abs_err, test_mean_abs_err_op = tf.metrics.mean_absolute_error(labels=label_pl, predictions=test_eval_predict) 
    test_accuracy, test_accuracy_op   = tf.metrics.accuracy(labels=label_pl, predictions=test_eval_predict) 
    test_precision, test_precision_op  = tf.metrics.precision(labels=label_pl, predictions=test_eval_predict) 
    test_recall, test_recall_op    = tf.metrics.recall(labels=label_pl, predictions=test_eval_predict) 
    test_f1_measure = 2 * test_precision * test_recall/(test_precision + test_recall) 
tf.summary.scalar('test_mean_abs_err', test_mean_abs_err) 
tf.summary.scalar('test_accuracy', test_accuracy) 
tf.summary.scalar('test_precision', test_precision) 
tf.summary.scalar('test_recall', test_recall) 
tf.summary.scalar('test_f1_measure', test_f1_measure) 
# validation metric init op 
validation_metrics_init_op = tf.variables_initializer(\ 
     var_list=[test_mean_abs_err_op, test_accuracy_op, test_precision_op, test_recall_op], \ 
     name='validation_metrics_init')

然而，当我运行它，会出现这样的错误：

Traceback (most recent call last): 
    File "./run_dnn.py", line 285, in <module> 
    train(wnd_conf) 
    File "./run_dnn.py", line 89, in train 
    name='validation_metrics_init') 
    File "/export/local/anaconda2/lib/python2.7/site- 
packages/tensorflow/python/ops/variables.py", line 1176, in 
variables_initializer 
return control_flow_ops.group(*[v.initializer for v in var_list], name=name) 
AttributeError: 'Tensor' object has no attribute 'initializer'

我意识到，我不能创建一个验证初始化这样。当我保存新的检查点模型并应用新一轮验证时，我想重新计算相应的指标。所以，我必须重新初始化指标为零。

但是如何将所有这些指标重置为零？非常感谢您的帮助！

回答：

我参考了博客（Avoiding headaches with tf.metrics）后，以下面的方式解决了这个问题。

# validation metrics 
validation_metrics_var_scope = "validation_metrics" 
test_mean_abs_err, test_mean_abs_err_op = tf.metrics.mean_absolute_error(labels=label_pl, predictions=test_eval_predict, name=validation_metrics_var_scope) 
test_accuracy, test_accuracy_op   = tf.metrics.accuracy(labels=label_pl, predictions=test_eval_predict, name=validation_metrics_var_scope) 
test_precision, test_precision_op  = tf.metrics.precision(labels=label_pl, predictions=test_eval_predict, name=validation_metrics_var_scope) 
test_recall, test_recall_op    = tf.metrics.recall(labels=label_pl, predictions=test_eval_predict, name=validation_metrics_var_scope) 
test_f1_measure = 2 * test_precision * test_recall/(test_precision + test_recall) 
tf.summary.scalar('test_mean_abs_err', test_mean_abs_err) 
tf.summary.scalar('test_accuracy', test_accuracy) 
tf.summary.scalar('test_precision', test_precision) 
tf.summary.scalar('test_recall', test_recall) 
tf.summary.scalar('test_f1_measure', test_f1_measure) 
# validation metric init op 
validation_metrics_vars = tf.get_collection(tf.GraphKeys.LOCAL_VARIABLES, scope=validation_metrics_var_scope) 
validation_metrics_init_op = tf.variables_initializer(var_list=validation_metrics_vars, name='validation_metrics_init')

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