無法在tensorboard中使用summary.merge進行單獨的培訓和評估摘要

Question

我正在嘗試使用張量板來觀察卷積神經網絡的學習。我對tf.summary.merge_all函數做的很好，可以創建一個合併的摘要。但是，我想跟蹤培訓和測試數據的準確性和損失。這篇文章很有用：Logging training and validation loss in tensorboard。爲了使事情更容易處理，我想將我的摘要合併成兩個合併的摘要，一個用於訓練和一個用於驗證（最終我會添加更多的東西，比如圖像權重等）。我試圖遵循來自張力板tf.summary.merge的描述。我無法做到這一點，我無法找到任何有用的例子來幫助我理解我的錯在哪裏。

with tf.name_scope('accuracy'): 
    correct_prediction = tf.equal(tf.argmax(y_logits, 1), tf.argmax(y, 1)) 
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float')) 
    tf.summary.scalar('accuracy', accuracy) 
    tf.summary.scalar('train_accuracy', accuracy) 

with tf.name_scope('Cost'): 
    cross_entropy = tf.reduce_mean(
     tf.nn.softmax_cross_entropy_with_logits(logits=y_logits, labels=y)) 
    opt = tf.train.AdamOptimizer() 
    optimizer = opt.minimize(cross_entropy) 
    grads = opt.compute_gradients(cross_entropy, [b_fc_loc2]) 
    tf.summary.scalar('cost', cross_entropy) 
    tf.summary.scalar('train_cost', cross_entropy) 


with tf.Session() as sess: 
    writer = tf.summary.FileWriter('./logs/mnistlogs/1f', sess.graph) 
    sess.run(tf.global_variables_initializer()) 
    merged = tf.summary.merge([cost, accuracy]) 

這將導致以下錯誤：

InvalidArgumentError (see above for traceback): Could not parse one of the summary inputs [[Node: Merge/MergeSummary = MergeSummary[N=2, _device="/job:localhost/replica:0/task:0/cpu:0"](Merge/MergeSummary/inputs_0, Merge/MergeSummary/inputs_1)]]

我想知道爲什麼會這樣是不行的，我怎麼能找到解決的辦法，任何工作實施讚賞。

Answer 1

我想通了。在合併之前，我需要提供摘要名稱。下面的代碼解決了這個問題：

with tf.name_scope('Cost'): 
cross_entropy = tf.reduce_mean(
     tf.nn.softmax_cross_entropy_with_logits(logits=y_logits, labels=y)) 
opt = tf.train.AdamOptimizer(learning_rate=0.000003) 
optimizer = opt.minimize(cross_entropy) 
grads = opt.compute_gradients(cross_entropy, [b_fc_loc2]) 
cost_sum = tf.summary.scalar('val_cost', cross_entropy) 
training_cost_sum = tf.summary.scalar('train_cost', cross_entropy) 


with tf.name_scope('accuracy'): 
correct_prediction = tf.equal(tf.argmax(y_logits, 1), tf.argmax(y, 1)) 
accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float')) 
train_accuracy = accuracy 
accuracy_sum = tf.summary.scalar('val_accuracy', accuracy) 
training_accuracy_sum = tf.summary.scalar('train_accuracy', accuracy) 


with tf.Session() as sess: 
writer = tf.summary.FileWriter('./logs/{}/{}'.format(session_name, run_num), sess.graph) 
sess.run(tf.global_variables_initializer()) 
train_merged = tf.summary.merge([training_accuracy_sum, training_cost_sum])