Tensorflow：為什么必須在聲明變量后聲明`saver = tf.train.Saver（）`？

Question

重要說明：我只在筆記本環境中運行此部分，圖形定義。 我還沒有參加過實際的會議。

運行此代碼時：

with graph.as_default(): #took out " , tf.device('/cpu:0')"

  saver = tf.train.Saver()
  valid_examples = np.array(random.sample(range(1, valid_window), valid_size)) #put inside graph to get new words each time

  train_dataset = tf.placeholder(tf.int32, shape=[batch_size, cbow_window*2 ])
  train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])
  valid_dataset = tf.constant(valid_examples, dtype=tf.int32)
  valid_datasetSM = tf.constant(valid_examples, dtype=tf.int32)

  embeddings = tf.get_variable( 'embeddings', 
    initializer= tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))

  softmax_weights = tf.get_variable( 'softmax_weights',
    initializer= tf.truncated_normal([vocabulary_size, embedding_size],
                         stddev=1.0 / math.sqrt(embedding_size)))

  softmax_biases = tf.get_variable('softmax_biases', 
    initializer= tf.zeros([vocabulary_size]),  trainable=False )

  embed = tf.nn.embedding_lookup(embeddings, train_dataset) #train data set is

  embed_reshaped = tf.reshape( embed, [batch_size*cbow_window*2, embedding_size] )


  segments= np.arange(batch_size).repeat(cbow_window*2)

  averaged_embeds = tf.segment_mean(embed_reshaped, segments, name=None)

    #return tf.reduce_mean( tf.nn.sampled_softmax_loss(weights=softmax_weights, biases=softmax_biases, inputs=averaged_embeds,
                               #labels=train_labels, num_sampled=num_sampled, num_classes=vocabulary_size))

  loss = tf.reduce_mean(
    tf.nn.sampled_softmax_loss(weights=softmax_weights, biases=softmax_biases, inputs=averaged_embeds,
                               labels=train_labels, num_sampled=num_sampled, num_classes=vocabulary_size))

  norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keepdims=True))
  normSM = tf.sqrt(tf.reduce_sum(tf.square(softmax_weights), 1, keepdims=True))

  normalized_embeddings = embeddings / norm
  normalized_embeddingsSM = softmax_weights / normSM

  valid_embeddings = tf.nn.embedding_lookup(
    normalized_embeddings, valid_dataset)
  valid_embeddingsSM = tf.nn.embedding_lookup(
    normalized_embeddingsSM, valid_datasetSM)

  similarity = tf.matmul(valid_embeddings, tf.transpose(normalized_embeddings))
  similaritySM = tf.matmul(valid_embeddingsSM, tf.transpose(normalized_embeddingsSM))

我收到了這個錯誤

ValueError：沒有要保存的變量

同時指着這條線

saver = tf.train.Saver()

我搜索了堆棧溢出並找到了這個答案

Tensorflow ValueError：無需保存的變量

所以我只是簡單地將該行放在圖形定義的底部

with graph.as_default(): #took out " , tf.device('/cpu:0')"

  valid_examples = np.array(random.sample(range(1, valid_window), valid_size)) #put inside graph to get new words each time

  train_dataset = tf.placeholder(tf.int32, shape=[batch_size, cbow_window*2 ])
  train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])
  valid_dataset = tf.constant(valid_examples, dtype=tf.int32)
  valid_datasetSM = tf.constant(valid_examples, dtype=tf.int32)

  embeddings = tf.get_variable( 'embeddings', 
    initializer= tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))
  softmax_weights = tf.get_variable( 'softmax_weights',
    initializer= tf.truncated_normal([vocabulary_size, embedding_size],
                         stddev=1.0 / math.sqrt(embedding_size)))

  softmax_biases = tf.get_variable('softmax_biases', 
    initializer= tf.zeros([vocabulary_size]),  trainable=False )

  embed = tf.nn.embedding_lookup(embeddings, train_dataset) #train data set is
  embed_reshaped = tf.reshape( embed, [batch_size*cbow_window*2, embedding_size] )

  segments= np.arange(batch_size).repeat(cbow_window*2)

  averaged_embeds = tf.segment_mean(embed_reshaped, segments, name=None)

  loss = tf.reduce_mean(
    tf.nn.sampled_softmax_loss(weights=softmax_weights, biases=softmax_biases, inputs=averaged_embeds,
                               labels=train_labels, num_sampled=num_sampled, num_classes=vocabulary_size))

  norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keepdims=True))
  normSM = tf.sqrt(tf.reduce_sum(tf.square(softmax_weights), 1, keepdims=True))

  normalized_embeddings = embeddings / norm
  normalized_embeddingsSM = softmax_weights / normSM

  valid_embeddings = tf.nn.embedding_lookup(
    normalized_embeddings, valid_dataset)
  valid_embeddingsSM = tf.nn.embedding_lookup(
    normalized_embeddingsSM, valid_datasetSM)

  similarity = tf.matmul(valid_embeddings, tf.transpose(normalized_embeddings))
  similaritySM = tf.matmul(valid_embeddingsSM, tf.transpose(normalized_embeddingsSM))

  saver = tf.train.Saver()

然后沒有錯誤！

為什么會這樣？ 圖形定義僅定義圖形，而不是運行任何圖形。 也許這是一個防止錯誤的措施？

Answer 1

它不必。 tf.train.Saver有一個defer_build參數，如果設置為True ，則允許您在構造變量后定義它們。 然后，您需要顯式調用build 。

saver = tf.train.Saver(defer_build=True)
# construct your graph, create variables...
...
saver.build()
graph.finalize()
# go on with training

Answer 2

從tf.train.Saver上的文檔中， __init__方法有一個參數var_list其中包含以下描述：

var_list: A list of Variable/SaveableObject, or a dictionary mapping names 
to SaveableObjects. If None, defaults to the list of all saveable objects.

這表明保護程序會在首次創建時創建要保存的變量列表，默認情況下包含它可以找到的所有變量。 如果沒有變量，則錯誤是有意義的，因為沒有要保存的變量。

隨機例子：

import tensorflow as tf
saver = tf.train.Saver()

以上引發了錯誤，下面也是如此

import tensorflow as tf
x = tf.placeholder(dtype=tf.float32)
saver = tf.train.Saver()

但最后一個例子運行，

import tensorflow as tf
x = tf.Variable(0.0)
saver = tf.train.Saver()