Question

我正在尝试使用Tensorboard来形象化我的训练程序。我的目的是，当每个时代完成时，我想使用整个验证数据集测试网络的准确性，并将此准确性结果存储到摘要文件中，以便我可以在Tensorboard中将其可视化。

我知道Tensorflow有summary_op这样做，但是在运行代码sess.run(summary_op)时它似乎只适用于一个批处理。我需要计算整个数据集的准确性。怎么样？

有没有任何例子可以做到？

Answer 1

定义接受占位符的tf.scalar_summary：

accuracy_value_ = tf.placeholder(tf.float32, shape=())
accuracy_summary = tf.scalar_summary('accuracy', accuracy_value_)

然后计算整个数据集的准确度（定义计算数据集中每个批次的准确度并提取平均值的例程）并将其保存到python变量中，我们称之为va。

获得va的值后，只需运行accuracy_summary操作，为accuracy_value_占位符提供信息：

sess.run(accuracy_summary, feed_dict={accuracy_value_: va})

Answer 2

我实现了一个天真的单层模型作为例子来对MNIST数据集进行分类并在Tensorboard中可视化验证的准确性，它对我有用。

import tensorflow as tf
from tensorflow.contrib.learn.python.learn.datasets.mnist import read_data_sets
import os

# number of epoch
num_epoch = 1000
model_dir = '/tmp/tf/onelayer_model/accu_info'
# mnist dataset location, change if you need
data_dir = '../data/mnist'

# load MNIST dataset without one hot
dataset = read_data_sets(data_dir, one_hot=False)

# Create placeholder for input images X and labels y
X = tf.placeholder(tf.float32, [None, 784])
# one_hot = False
y = tf.placeholder(tf.int32)

# One layer model graph
W = tf.Variable(tf.truncated_normal([784, 10], stddev=0.1))
b = tf.Variable(tf.constant(0.1, shape=[10]))
logits = tf.nn.relu(tf.matmul(X, W) + b)

init = tf.initialize_all_variables()

cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits, y)
# loss function
loss = tf.reduce_mean(cross_entropy)
train_op = tf.train.GradientDescentOptimizer(0.01).minimize(loss)

_, top_1_op = tf.nn.top_k(logits)
top_1 = tf.reshape(top_1_op, shape=[-1])
correct_classification = tf.cast(tf.equal(top_1, y), tf.float32)
# accuracy function
acc = tf.reduce_mean(correct_classification)

# define info that is used in SummaryWritter
acc_summary = tf.scalar_summary('valid_accuracy', acc)
valid_summary_op = tf.merge_summary([acc_summary])

with tf.Session() as sess:
    # initialize all the variable
    sess.run(init)

    print("Writing Summaries to %s" % model_dir)
    train_summary_writer = tf.train.SummaryWriter(model_dir, sess.graph)

    # load validation dataset
    valid_x = dataset.validation.images
    valid_y = dataset.validation.labels

    for epoch in xrange(num_epoch):
        batch_x, batch_y = dataset.train.next_batch(100)
        feed_dict = {X: batch_x, y: batch_y}
        _, acc_value, loss_value = sess.run(
            [train_op, acc, loss], feed_dict=feed_dict)
        vsummary = sess.run(valid_summary_op,
                            feed_dict={X: valid_x,
                                       y: valid_y})

        # Write validation accuracy summary
        train_summary_writer.add_summary(vsummary, epoch)

Answer 3

如果您使用的是使用内部计数器的tf.metrics操作，则可以使用您的验证集进行批处理。这是一个简化的例子：

model = create_model()
tf.summary.scalar('cost', model.cost_op)
acc_value_op, acc_update_op = tf.metrics.accuracy(labels,predictions)

summary_common = tf.summary.merge_all()

summary_valid = tf.summary.merge([
    tf.summary.scalar('accuracy', acc_value_op),
    # other metrics here...
])

with tf.Session() as sess:
    train_writer = tf.summary.FileWriter(logs_path + '/train',
                                         sess.graph)
    valid_writer = tf.summary.FileWriter(logs_path + '/valid')

在培训时，只能使用您的列车编写者编写常见摘要：

summary = sess.run(summary_common)
train_writer.add_summary(summary, tf.train.global_step(sess, gstep_op))
train_writer.flush()

每次验证后，使用valid-writer编写两个摘要：

gstep, summaryc, summaryv = sess.run([gstep_op, summary_common, summary_valid])
valid_writer.add_summary(summaryc, gstep)
valid_writer.add_summary(summaryv, gstep)
valid_writer.flush()

使用tf.metrics时，不要忘记在每个验证步骤之前重置内部计数器（局部变量）。

在Tensorflow中添加整个列车/测试数据集的准确性摘要

3 个答案: