region_proposal.py 文件源码

def create_train_summaries(learning_rate, clas_loss, reg_loss, rpn_loss, clas_accuracy, clas_positive_percentage, clas_positive_accuracy, VGG16D_activations, clas_activations):
    with tf.name_scope('train'):
        learning_rate_summary = tf.scalar_summary('learning_rate', learning_rate)

        loss_clas_summary = tf.scalar_summary('loss/clas', clas_loss)
        loss_reg_summary = tf.scalar_summary('loss/reg', reg_loss)
        loss_rpn_summary = tf.scalar_summary('loss/rpn', rpn_loss)

        stat_accuracy_summary = tf.scalar_summary('stat/accuracy', clas_accuracy)
        stat_positive_percentage_summary = tf.scalar_summary('stat/positive_percentage', clas_positive_percentage)
        stat_positive_accuracy_summary = tf.scalar_summary('stat/positive_accuracy', clas_positive_accuracy)

        VGG16D_histogram = tf.histogram_summary('activations/VGG16D', VGG16D_activations)
        clas_histogram = tf.histogram_summary('activations/clas', clas_activations)

        return tf.merge_summary([learning_rate_summary, loss_clas_summary, loss_reg_summary, loss_rpn_summary, stat_accuracy_summary, stat_positive_percentage_summary, stat_positive_accuracy_summary, VGG16D_histogram, clas_histogram])