如何在tensorflow中恢复会话？[副本]

Question

我想使用我的神经网络，而不需要再次训练网络。我读到关于

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

现在我在文件夹中有3个文件：检查点、model.ckpt和model.ckpt.meta

我想在python的另一个类中恢复数据，得到我的神经网络的权重，并进行一次预测。

我该怎么做呢？

Answer 1

要保存模型，您可以这样做：

model_checkpoint = 'model.chkpt'

# Create the model
...
...

with tf.Session() as sess:
    sess.run(tf.initialize_all_variables())

    # Create a saver so we can save and load the model as we train it
    tf_saver = tf.train.Saver(tf.all_variables())

    # (Optionally) do some training of the model
    ...
    ...

    tf_saver.save(sess, model_checkpoint)

我想你已经这样做了，因为你已经得到了三份文件。当您想在另一个类中加载模型时，可以这样做：

# The same file as we saved earlier
model_checkpoint = 'model.chkpt'

# Create the SAME model as before
...
...

with tf.Session() as sess:
    # Restore the model
    tf_saver = tf.train.Saver()
    tf_saver.restore(sess, model_checkpoint)

    # Now your model is loaded with the same values as when you saved,
    #   and you can do prediction or continue training