Tensorflow培訓和驗證輸入隊列分隔

我試圖使用TensorFlow複製完全卷積網絡結果。我用Marvin Teichmann's implementation from github。我只需要寫培訓包裝。我創建了兩個共享變量和兩個輸入隊列的圖形，一個用於訓練，一個用於驗證。爲了測試我的培訓包裝，我使用了兩個簡短的培訓和驗證文件列表，並且在每個培訓時期後立即進行驗證。我還從輸入隊列中打印出每個圖像的形狀，以檢查是否得到正確的輸入。但是，在我開始訓練後，似乎只有訓練隊列中的圖像正在排隊。因此，訓練和驗證圖都從訓練隊列中獲取輸入，並且驗證隊列從不被訪問。任何人都可以幫助解釋和解決這個問題？Tensorflow培訓和驗證輸入隊列分隔

下面是相關的代碼的一部分：

def get_data(image_name_list, num_epochs, scope_name, num_class = NUM_CLASS): 
    with tf.variable_scope(scope_name) as scope: 
     images_path = [os.path.join(DATASET_DIR, i+'.jpg') for i in image_name_list] 
     gts_path = [os.path.join(GT_DIR, i+'.png') for i in image_name_list] 
     seed = random.randint(0, 2147483647) 
     image_name_queue = tf.train.string_input_producer(images_path, num_epochs=num_epochs, shuffle=False, seed = seed) 
     gt_name_queue = tf.train.string_input_producer(gts_path, num_epochs=num_epochs, shuffle=False, seed = seed) 
     reader = tf.WholeFileReader() 
     image_key, image_value = reader.read(image_name_queue) 
     my_image = tf.image.decode_jpeg(image_value) 
     my_image = tf.cast(my_image, tf.float32) 
     my_image = tf.expand_dims(my_image, 0) 
     gt_key, gt_value = reader.read(gt_name_queue) 
     # gt stands for ground truth 
     my_gt = tf.cast(tf.image.decode_png(gt_value, channels = 1), tf.float32) 
     my_gt = tf.one_hot(tf.cast(my_gt, tf.int32), NUM_CLASS) 
     return my_image, my_gt 

train_image, train_gt = get_data(train_files, NUM_EPOCH, 'training') 
val_image, val_gt = get_data(val_files, NUM_EPOCH, 'validation') 
with tf.variable_scope('FCN16') as scope: 
     train_vgg16_fcn = fcn16_vgg.FCN16VGG() 
     train_vgg16_fcn.build(train_image, train=True, num_classes=NUM_CLASS, keep_prob = KEEP_PROB) 
     scope.reuse_variables() 
     val_vgg16_fcn = fcn16_vgg.FCN16VGG() 
     val_vgg16_fcn.build(val_image, train=False, num_classes=NUM_CLASS, keep_prob = 1) 
""" 
Define the loss, evaluation metric, summary, saver in the computation graph. Initialize variables and start a session. 
""" 
for epoch in range(starting_epoch, NUM_EPOCH): 
    for i in range(train_num): 
     _, loss_value, shape = sess.run([train_op, train_entropy_loss, tf.shape(train_image)]) 
     print shape 
    for i in range(val_num): 
     loss_value, shape = sess.run([val_entropy_loss, tf.shape(val_image)]) 
     print shape

來源

2016-08-24 Ruizhi Deng

您是否找到答案？ – thigi

我沒有一個好的答案，但建議在單獨的過程中運行評估。它更容易和更清潔。如果您不想這樣做，您可以創建兩個不同的圖表和會話，並將您的驗證輸入隊列與此關聯起來。 –

要確保你正在閱讀不同的圖像，你可以運行：

[train_image_np, val_image_np] = sess.run([train_image, val_image])

要重複使用的變量，這是更好，更安全：

with tf.variable_scope('FCN16') as scope: 
    train_vgg16_fcn = fcn16_vgg.FCN16VGG() 
    train_vgg16_fcn.build(train_image, train=True, num_classes=NUM_CLASS, keep_prob = KEEP_PROB) 
with tf.variable_scope(scope, reuse=True): 
    val_vgg16_fcn = fcn16_vgg.FCN16VGG() 
    val_vgg16_fcn.build(val_image, train=False, num_classes=NUM_CLASS, keep_prob = 1)

來源

2017-05-20 06:07:19

Tensorflow培訓和驗證輸入隊列分隔

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