tensorflow:保存和恢复会话

我试图从答案中实现一个建议: Tensorflow:如何保存/恢复模型？

我有一个对象,它tensorflow以一种sklearn风格包装模型.

import tensorflow as tf
class tflasso():
    saver = tf.train.Saver()
    def __init__(self,
                 learning_rate = 2e-2,
                 training_epochs = 5000,
                    display_step = 50,
                    BATCH_SIZE = 100,
                    ALPHA = 1e-5,
                    checkpoint_dir = "./",
             ):
        ...

    def _create_network(self):
       ...


    def _load_(self, sess, checkpoint_dir = None):
        if checkpoint_dir:
            self.checkpoint_dir = checkpoint_dir

        print("loading a session")
        ckpt = tf.train.get_checkpoint_state(self.checkpoint_dir)
        if ckpt and ckpt.model_checkpoint_path:
            self.saver.restore(sess, ckpt.model_checkpoint_path)
        else:
            raise Exception("no checkpoint found")
        return

    def fit(self, train_X, train_Y , load = True):
        self.X = train_X
        self.xlen = train_X.shape[1]
        # n_samples = y.shape[0]

        self._create_network()
        tot_loss = self._create_loss()
        optimizer = tf.train.AdagradOptimizer( self.learning_rate).minimize(tot_loss)

        # Initializing the variables
        init = tf.initialize_all_variables()
        " training per se"
        getb = batchgen( self.BATCH_SIZE)

        yvar = train_Y.var()
        print(yvar)
        # Launch the graph
        NUM_CORES = 3  # Choose how many cores to use.
        sess_config = tf.ConfigProto(inter_op_parallelism_threads=NUM_CORES,
                                                           intra_op_parallelism_threads=NUM_CORES)
        with tf.Session(config= sess_config) as sess:
            sess.run(init)
            if load:
                self._load_(sess)
            # Fit all training data
            for epoch in range( self.training_epochs):
                for (_x_, _y_) in getb(train_X, train_Y):
                    _y_ = np.reshape(_y_, [-1, 1])
                    sess.run(optimizer, feed_dict={ self.vars.xx: _x_, self.vars.yy: _y_})
                # Display logs per epoch step
                if (1+epoch) % self.display_step == 0:
                    cost = sess.run(tot_loss,
                            feed_dict={ self.vars.xx: train_X,
                                    self.vars.yy: np.reshape(train_Y, [-1, 1])})
                    rsq =  1 - cost / yvar
                    logstr = "Epoch: {:4d}\tcost = {:.4f}\tR^2 = {:.4f}".format((epoch+1), cost, rsq)
                    print(logstr )
                    self.saver.save(sess, self.checkpoint_dir + 'model.ckpt',
                       global_step= 1+ epoch)

            print("Optimization Finished!")
        return self

当我跑:

tfl = tflasso()
tfl.fit( train_X, train_Y , load = False)

我得到输出:

Epoch:   50 cost = 38.4705  R^2 = -1.2036
    b1: 0.118122
Epoch:  100 cost = 26.4506  R^2 = -0.5151
    b1: 0.133597
Epoch:  150 cost = 22.4330  R^2 = -0.2850
    b1: 0.142261
Epoch:  200 cost = 20.0361  R^2 = -0.1477
    b1: 0.147998

但是,当我尝试恢复参数时(即使没有杀死对象): tfl.fit( train_X, train_Y , load = True)

我得到了奇怪的结果.首先,加载的值与保存的值不对应.

loading a session
loaded b1: 0.1          <------- Loaded another value than saved
Epoch:   50 cost = 30.8483  R^2 = -0.7670
    b1: 0.137484  

加载的正确方法是什么,可能首先检查保存的变量？

TL; DR:你应该尝试重做这个类,这样就self.create_network()可以(i)只调用一次,然后(ii)调用tf.train.Saver()它.

这里有两个微妙的问题,这是由于代码结构和tf.train.Saver构造函数的默认行为.当您构造一个没有参数的保护程序时(如在您的代码中),它会收集程序中的当前变量集,并将操作添加到图中以保存和恢复它们.在你的代码中,当你调用时tflasso(),它将构造一个saver,并且不会有变量(因为create_network()尚未被调用).因此,检查点应为空.

第二个问题是 - 默认情况下 - 保存的检查点的格式是从name变量属性到其当前值的映射.如果您创建两个具有相同名称的变量,它们将由TensorFlow自动"不加":

v = tf.Variable(..., name="weights")
assert v.name == "weights"
w = tf.Variable(..., name="weights")
assert v.name == "weights_1"  # The "_1" is added by TensorFlow.

这样做的结果是,当您self.create_network()在第二次调用时调用时tfl.fit(),变量将与存储在检查点中的名称具有不同的名称 - 或者如果在网络之后构建了保护程序,则可能是这样.(您可以通过将名称Variable字典传递给saver构造函数来避免此行为,但这通常很尴尬.)

有两个主要的解决方法:

在每次调用中tflasso.fit(),重新创建整个模型,通过定义一个新的tf.Graph,然后在该图中构建网络并创建一个tf.train.Saver.

推荐创建网络,然后tf.train.Saver在tflasso构造函数中创建,并在每次调用时重用此图tflasso.fit().请注意,您可能需要做更多的工作,以重组的事情(尤其是我不知道你做什么self.X和self.xlen),但它应该是可能的,实现这个占位符和喂养.