Tensorflow中的Retrain模型

Question

我有一個使用Tensorflow的簡單神經網絡。 這裏是會議：

with tensorFlow.Session() as sess: 
    sess.run(tensorFlow.global_variables_initializer()) 
    for epoch in range(epochs): 
    i = 0 
    epochLoss = 0 
    for _ in range(int(len(data)/batchSize)): 
     ex, ey = nextBatch(i) 
     i += 1 
     feedDict = {x :ex, y:ey } 
     _, cos = sess.run([optimizer,cost], feed_dict= feedDict) 
     epochLoss += cos/(int(len(data))/batchSize) 
    print("Epoch", epoch + 1, "completed out of", epochs, "loss:", "{:.9f}".format(epochLoss)) 

    save_path = saver.save(sess, "model.ckpt") 
    print("Model saved in file: %s" % save_path) 

在最後2行我保存的模型和還原在另一大類圖：

with new_graph.as_default(): 
    with tf.Session(graph=new_graph) as sess: 
     sess.run(tf.global_variables_initializer()) 
     new_saver = tf.train.import_meta_graph('model.ckpt.meta') 
     new_saver.restore(sess, tf.train.latest_checkpoint('./')) 

我想重新訓練模型，這意味着未初始化權重，只是爲了從停止的最後一點更新它們。

我該怎麼做？

Answer 1

從https://www.tensorflow.org/api_docs/python/state_ops/saving_and_restoring_variables

tf.train.Saver.restore（SESS，的save_path）

恢復總計先前保存的變量。

此方法運行構造函數添加的ops以恢復 變量。它需要一個圖表啓動的會話。 要恢復的變量不必被初始化，因爲 恢復本身就是一種初始化變量的方法。

下面的例子是從https://www.tensorflow.org/how_tos/variables/

# Create some variables. 
v1 = tf.Variable(..., name="v1") 
v2 = tf.Variable(..., name="v2") 
... 
# Add ops to save and restore all the variables. 
saver = tf.train.Saver() 

# Later, launch the model, use the saver to restore variables from disk, and 
# do some work with the model. 
with tf.Session() as sess: 
    # Restore variables from disk. 
    saver.restore(sess, "/tmp/model.ckpt") 
    print("Model restored.") 
    # Do some work with the model 
    ...