python - Tensorflow仅初始化特定范围

Question

他在那里，

我有一个关于控制初始化哪个变量范围的问题，或者至少是在运行期间使用哪个变量范围的问题。

以这段简单的代码为例

import numpy as np
import tensorflow as tf

with tf.variable_scope('0') as scope:
    place_holder_batch_x = tf.Variable(np.random.rand(11,6), dtype=tf.float64)
    place_holder_batch_y = tf.Variable(np.random.rand(8,5), dtype=tf.float64)
    rnn_cell = tf.nn.rnn_cell.BasicRNNCell(3)
    z = place_holder_batch_x*2

with tf.variable_scope('1') as scope:
    place_holder_batch_x = tf.Variable(np.random.rand(10,5), dtype=tf.float64)
    place_holder_batch_y = tf.Variable(np.random.rand(9,6), dtype=tf.float64)
    rnn_cell = tf.nn.rnn_cell.BasicRNNCell(4)
    z = place_holder_batch_x*2

init = tf.global_variables_initializer()

sess = tf.Session()
sess.run(init)
print(sess.run(z).shape)

如果我按原样运行它，我将获得变量 z 的形状，如变量范围“1”中定义的那样。但是我如何指定在 session 期间使用哪个变量范围呢？我在 stackoverflow 或文档中找不到任何答案...

当然，我可以将两个 z 重命名为 z1 和 z2...但我想保持两个作用域看起来很相似并使用相同名称的情况...

Answer 1

试试这个:

import numpy as np
import tensorflow as tf


g1 = tf.Graph()
with g1.as_default() as g:
    with tf.variable_scope('0') as scope:
        place_holder_batch_x = tf.Variable(np.random.rand(11,6), dtype=tf.float64)
        place_holder_batch_y = tf.Variable(np.random.rand(8,5), dtype=tf.float64)
        rnn_cell = tf.nn.rnn_cell.BasicRNNCell(3)
        z = place_holder_batch_x*2
g2 = tf.Graph()
with g2.as_default() as g:
    with tf.variable_scope('1') as scope:
        place_holder_batch_x = tf.Variable(np.random.rand(10,5), dtype=tf.float64)
        place_holder_batch_y = tf.Variable(np.random.rand(9,6), dtype=tf.float64)
        rnn_cell = tf.nn.rnn_cell.BasicRNNCell(4)
        z = place_holder_batch_x*2

tf.reset_graph_default()

graph_to_be_used = g1

with tf.session(graph = graph_to_be_used) as sess:
    init = tf.global_variables_initializer()

    sess.run(init)
    print(sess.run(z).shape)