如何在tensorflow中恢复会话？

Question

我想使用我的神经网络而不再训练网络.我读到了

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

现在我把文件夹中的3个文件:checkpoint,model.ckpt,和model.ckpt.meta

我希望,在python的另一个类中恢复数据,获得我的神经网络的权重并进行单一预测.

我怎样才能做到这一点？

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)

我假设您已经完成了此操作，因为您已经获得了三个文件。当你想在另一个类中加载模型时，你可以这样做：

# 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