將經過訓練的模型加載回張量流

我已經在Tensorflow中構建了一個模型，我已經訓練了它。現在我想處理輸出，所以我想將檢查點，Meta和所有其他文件加載到tensorlow中。將經過訓練的模型加載回張量流

我用下面的代碼來訓練模型：

# Logging 
merged = tf.summary.merge_all() 
train_writer = tf.summary.FileWriter(FLAGS.summary_dir + '/train') 
test_writer = tf.summary.FileWriter(FLAGS.summary_dir + '/test') 
validate_writer = tf.summary.FileWriter(FLAGS.summary_dir + '/validate') 
writer = tf.summary.FileWriter(FLAGS.summary_dir, sess.graph) 
saver = tf.train.Saver() # for storing the best network 

# Initialize variables 
init = tf.global_variables_initializer() 
sess.run(init) 

# Best validation accuracy seen so far 
bestValidation = -0.1 

# Training loop 
coord = tf.train.Coordinator() # coordinator for threads 
threads = tf.train.start_queue_runners(coord = coord, sess=sess) # start queue thread 

# Training loop 
for i in range(FLAGS.maxIter): 
    xTrain, yTrain = sess.run(data_batch) 
    sess.run(train_step, feed_dict={x_data: xTrain, y_target: np.transpose([yTrain])}) 
    summary = sess.run(merged, feed_dict={x_data: xTrain, y_target: np.transpose([yTrain])}) 
    train_writer.add_summary(summary, i) 
    if ((i + 1) % 10 == 0): 
     print("Iteration:", i + 1, "/", FLAGS.maxIter) 
     summary = sess.run(merged, feed_dict={x_data: dataTest.data, y_target: np.transpose([dataTest.target])}) 
     test_writer.add_summary(summary, i) 
     currentValidation, summary = sess.run([accuracy, merged], feed_dict={x_data: dataTest.data, 
                      y_target: np.transpose(
                       [dataTest.target])}) 
    validate_writer.add_summary(summary, i) 
    if (currentValidation > bestValidation and currentValidation <= 0.9): 
     bestValidation = currentValidation 
     saver.save(sess=sess, save_path=FLAGS.summary_dir + '/bestNetwork') 
     print("\tbetter network stored,", currentValidation, ">", bestValidation) 

coord.request_stop() # ask threads to stop 
coord.join(threads) # wait for threads to stop

現在我想加載模型回Tensorflow。我希望能夠做一些事情：

使用我已經爲訓練和測試數據集創建的輸出。
將新數據加載到模型中，然後可以使用相同的權重生成新的輸出。

我使用下面的代碼回加載模型到tensorflow試過，但它不工作：

with tf.Session() as sess: 
    saver = tf.train.import_meta_graph(FLAGS.summary_dir + '/bestNetwork.meta') 
    saver.restore(sess,tf.train.latest_checkpoint(FLAGS.summary_dir + '/checkpoint'))

運行的代碼時，我收到以下錯誤：

TypeError：期望的字節，找不到的類型

正如我已經說明的那樣，我使用tf.train.import_meta_graph（）函數加載了上一節中的元圖，然後使用檢查點部分加載了權重。那麼，爲什麼這不起作用？

來源

2017-08-07 Mike Johansson

您將模型保存爲bestNetwork。試試這個：

saver.restore(sess,tf.train.latest_checkpoint(FLAGS.summary_dir + '/**bestNetwork**'))

來源

2017-12-14 02:09:45

將經過訓練的模型加載回張量流

回答

相關問題