Question

我想在另一个培训操作中进行如下的小型培训操作：

def get_alphas(weights, filters):
    alphas = tf.Variable(...)
    # Define some loss and training_op here
    with tf.Session() as sess:
        for some_epochs:
            sess.run(training_op)
        return tf.convert_to_tensor(sess.run(alphas))

def get_updated_weights(default_weights):
    weights = tf.Variable(default_weights)
    # Some operation on weights to get filters

    # Now, the following will produce errors since weights is not initialized
    alphas = get_alphas(weights, filters)

    # Other option is to initialize it here as follows
    with tf.Session() as sess:
        sess.run(tf.variables_initializer([weights]))
        calculated_filters = sess.run(filters)
        alphas = get_alphas(default_weights, calculated_filters)

    return Some operation on alphas and filters

所以，我想要做的是创建一个名为weights的变量。 alphas和filters在weights上动态依赖（通过一些培训）。现在，当weights经过培训时，filters会在weights上通过某些操作创建时发生变化，但alphas也需要更改，只有另一个才能找到培训操作。

如果从上面不清楚意图，我将提供确切的功能。

Answer 1

您描述的技巧不起作用，因为tf.Session.close会释放所有相关资源，例如变量，队列和读者。因此get_alphas的结果将不是有效的张量。

最佳做法是定义多个损失和培训操作（影响图表的不同部分），并在需要时在单个会话中运行。

alphas = tf.Variable(...)
# Define some loss and training_op here

def get_alphas(sess, weights, filters):
  for some_epochs:
    sess.run(training_op)

# The rest of the training...

在另一个培训操作中运行培训操作

1 个答案: