我正在尝试在嵌套的while_loop()
中更新一个二维张量。然而,当将变量传递给第二个循环时,我无法使用tf.assign()
进行更新,因为它会抛出以下错误:
ValueError: Sliced assignment is only supported for variables
如果我在while循环外创建变量并仅在第一个循环中使用它,那么它的运行很好。
我如何在第二个while循环中修改我2D tf变量?
(我使用Python 2.7和TensorFlow 1.2)
我的代码:
import tensorflow as tf
import numpy as np
tf.reset_default_graph()
BATCH_SIZE = 10
LENGTH_MAX_OUTPUT = 31
it_batch_nr = tf.constant(0)
it_row_nr = tf.Variable(0, dtype=tf.int32)
it_col_nr = tf.constant(0)
cost = tf.constant(0)
it_batch_end = lambda it_batch_nr, cost: tf.less(it_batch_nr, BATCH_SIZE)
it_row_end = lambda it_row_nr, cost_matrix: tf.less(it_row_nr, LENGTH_MAX_OUTPUT+1)
def iterate_batch(it_batch_nr, cost):
cost_matrix = tf.Variable(np.ones((LENGTH_MAX_OUTPUT+1, LENGTH_MAX_OUTPUT+1)), dtype=tf.float32)
it_rows, cost_matrix = tf.while_loop(it_row_end, iterate_row, [it_row_nr, cost_matrix])
cost = cost_matrix[0,0] # IS 1.0, SHOULD BE 100.0
return tf.add(it_batch_nr,1), cost
def iterate_row(it_row_nr, cost_matrix):
# THIS THROWS AN ERROR:
cost_matrix[0,0].assign(100.0)
return tf.add(it_row_nr,1), cost_matrix
it_batch = tf.while_loop(it_batch_end, iterate_batch, [it_batch_nr, cost])
sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())
out = sess.run(it_batch)
print(out)