在Tensorflow中训练一个模型后:

如何拯救训练过的模型? 您以后如何恢复这个保存的模型?


当前回答

最简单的方法是使用keras api,在线保存模型和一行加载模型

from keras.models import load_model

my_model.save('my_model.h5')  # creates a HDF5 file 'my_model.h5'

del my_model  # deletes the existing model


my_model = load_model('my_model.h5') # returns a compiled model identical to the previous one

其他回答

根据新的Tensorflow版本,tf.train.Checkpoint是保存和恢复模型的最佳方式:

Checkpoint.save and Checkpoint.restore write and read object-based checkpoints, in contrast to tf.train.Saver which writes and reads variable.name based checkpoints. Object-based checkpointing saves a graph of dependencies between Python objects (Layers, Optimizers, Variables, etc.) with named edges, and this graph is used to match variables when restoring a checkpoint. It can be more robust to changes in the Python program, and helps to support restore-on-create for variables when executing eagerly. Prefer tf.train.Checkpoint over tf.train.Saver for new code.

这里有一个例子:

import tensorflow as tf
import os

tf.enable_eager_execution()

checkpoint_directory = "/tmp/training_checkpoints"
checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")

checkpoint = tf.train.Checkpoint(optimizer=optimizer, model=model)
status = checkpoint.restore(tf.train.latest_checkpoint(checkpoint_directory))
for _ in range(num_training_steps):
  optimizer.minimize( ... )  # Variables will be restored on creation.
status.assert_consumed()  # Optional sanity checks.
checkpoint.save(file_prefix=checkpoint_prefix)

这里有更多信息和示例。

对于TensorFlow版本< 0.11.0RC1:

保存的检查点包含模型中的变量值,而不是模型/图本身,这意味着当您恢复检查点时,图应该是相同的。

这里有一个线性回归的例子,其中有一个训练循环,保存变量检查点,还有一个评估部分,将恢复之前运行中保存的变量并计算预测。当然,如果你愿意,你也可以恢复变量并继续训练。

x = tf.placeholder(tf.float32)
y = tf.placeholder(tf.float32)

w = tf.Variable(tf.zeros([1, 1], dtype=tf.float32))
b = tf.Variable(tf.ones([1, 1], dtype=tf.float32))
y_hat = tf.add(b, tf.matmul(x, w))

...more setup for optimization and what not...

saver = tf.train.Saver()  # defaults to saving all variables - in this case w and b

with tf.Session() as sess:
    sess.run(tf.initialize_all_variables())
    if FLAGS.train:
        for i in xrange(FLAGS.training_steps):
            ...training loop...
            if (i + 1) % FLAGS.checkpoint_steps == 0:
                saver.save(sess, FLAGS.checkpoint_dir + 'model.ckpt',
                           global_step=i+1)
    else:
        # Here's where you're restoring the variables w and b.
        # Note that the graph is exactly as it was when the variables were
        # saved in a prior training run.
        ckpt = tf.train.get_checkpoint_state(FLAGS.checkpoint_dir)
        if ckpt and ckpt.model_checkpoint_path:
            saver.restore(sess, ckpt.model_checkpoint_path)
        else:
            ...no checkpoint found...

        # Now you can run the model to get predictions
        batch_x = ...load some data...
        predictions = sess.run(y_hat, feed_dict={x: batch_x})

下面是变量文档,涵盖了保存和恢复。这是保存程序的文档。

在新版本的tensorflow 2.0中,保存/加载模型的过程要容易得多。因为Keras API的实现,一个TensorFlow的高级API。

保存一个模型: 请查阅相关文档以作参考: https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/models/save_model

tf.keras.models.save_model(model_name, filepath, save_format)

加载一个模型:

https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/models/load_model

model = tf.keras.models.load_model(filepath)

使用tf.train.Saver保存模型。记住,如果想要减小模型大小,就需要指定var_list。val_list可以是:

特遣部队。trainable_variables或 tf.global_variables。

模型有两个部分,模型定义,由Supervisor保存为图。模型目录中的PBTXT和张量的数值,保存到检查点文件,如model.ckpt-1003418。

可以使用tf恢复模型定义。import_graph_def,并且使用Saver恢复权重。

然而,Saver使用特殊的集合保存附加到模型Graph的变量列表,并且这个集合没有使用import_graph_def初始化,所以您目前不能同时使用这两者(这在我们的路线图中进行修复)。现在,您必须使用Ryan Sepassi的方法——手动构造具有相同节点名称的图,并使用Saver将权重加载到其中。

(或者,您可以通过使用import_graph_def,手动创建变量和使用tf.add_to_collection(tf.GraphKeys. collection)来破解它。变量,变量)为每个变量,然后使用Saver)