我目前正试图通过TensorFlow，tf.Summaries和tf.FileWriter来监控我的TensorBoard模型。
我成功地绘制了培训指标，以及绘制验证（和/或测试）指标。但是，我的问题是我没有成功地将两个数据集指标一起绘制在同一个图表中：因为我的验证数据集太大，我必须批量处理，我不能满足于目前适用于MNIST和其他规范的标准解决方案数据集（参见带有摘要的Mnist的例如 this Github example code，或者一些Stackeroverflow线程here，here和here）。
由于我的验证数据集是多批处理的，我被强制使用值并按照this answer或this one的例如更新操作。
这是一个与我想要做的相对应的最小工作示例：
import os
import tensorflow as tf
from tensorflow.contrib.learn.python.learn.datasets import mnist
dataset = mnist.read_data_sets("data", one_hot=True, reshape=False, validation_size=0)
X = tf.placeholder(tf.float32, name='X', shape=[None, 28, 28, 1])
Y = tf.placeholder(tf.float32, name='Y', shape=[None, 10])
# Conv layer
w1 = tf.Variable(tf.truncated_normal([5, 5, 1, 8]), name="weights_c1", trainable=True)
b1 = tf.Variable(tf.ones([8])/10, name="biases_c1", trainable=True)
conv1 = tf.nn.conv2d(X, w1, strides=[1, 1, 1, 1], padding="SAME", name="conv1")
conv_bias1 = tf.add(conv1, b1, name="convbias1")
relu1 = tf.nn.relu(conv_bias1, name="relu1")
# Max pooling layer
pool1 = tf.nn.max_pool(relu1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")
# Fully-connected layer
reshaped = tf.reshape(pool1, [-1, 14 * 14 * 8])
wfc = tf.Variable(tf.truncated_normal([14 * 14 * 8, 500]),
name="weights_fc", trainable=True)
bfc = tf.Variable(tf.ones([500])/10, name="biases_fc", trainable=True)
fc = tf.add(tf.matmul(reshaped, wfc), bfc, name="raw_fc")
relu_fc = tf.nn.relu(fc, name="relu_fc")
# Output layer
wo = tf.Variable(tf.truncated_normal([500, 10]), name="weights_output", trainable=True)
bo = tf.Variable(tf.ones([10])/10, name="biases_output", trainable=True)
logits = tf.add(tf.matmul(relu_fc, wo), bo, name="logits")
Y_raw_predict = tf.nn.softmax(logits, name="y_pred_raw")
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=Y)
# Optimization
loss = tf.reduce_mean(cross_entropy)
optimizer = tf.train.AdamOptimizer(0.01).minimize(loss)
correct_prediction = tf.equal(tf.argmax(Y_raw_predict, 1), tf.argmax(Y, 1))
# Accuracy computing (definition of a summary for training,
# and value/update ops for batched testing)
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
acc_sum = tf.summary.scalar("accuracy", accuracy)
mean_acc_value, mean_acc_update = tf.metrics.mean(accuracy, name="mean_accuracy_op")
tf.summary.scalar("mean_accuracy", mean_acc_value, collections = ["test"])
# tf.Summary and tf.FileWriter settings
train_summary = tf.summary.merge_all()
test_summary = tf.summary.merge_all("test")
graph_path = "./logs/mnist/graph/mnist1"
train_writer = tf.summary.FileWriter(os.path.join(graph_path, "training"))
test_writer = tf.summary.FileWriter(os.path.join(graph_path, "testing"))
# tf.Session opening and graph filling
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer()) # for value/update ops
for step in range(301):
xbatch, ybatch = dataset.train.next_batch(100)
sess.run(optimizer, feed_dict={X: xbatch, Y:ybatch})
# Monitor training each 10 steps
if step % 10 == 0:
s, l, acc, accsum = sess.run([train_summary, loss, accuracy, acc_sum],
feed_dict={X: xbatch, Y: ybatch})
train_writer.add_summary(s, step)
print("step: {}, loss={:5.4f}, acc={:0.3f}".format(step, l, acc))
# Monitor testing data each 100 steps
if step % 100 == 0:
# Consider 10000 testing images by batch of 100 images
for test_step in range(101):
xtest, ytest = dataset.test.next_batch(100)
sess.run([mean_acc_update], feed_dict={X: xtest, Y: ytest})
tacc, testsum = sess.run([mean_acc_value, test_summary])
test_writer.add_summary(testsum, step)
print("Validation OK: acc={:0.3f}".format(tacc))
我在TensorBoard上得到以下结果（两个不同的图形，当我想在同一个图形上有两条曲线时）： TensorBoard result （例如，预期结果为this one）
问题是：如果必须将验证数据集拆分成批次，如何在同一图表中结合培训和验证指标？
谢谢大家！
