问题:

I need to create a matrix in TensorFlow to store some values. The trick is the matrix has to support dynamic shape.

I am trying to do the same I would do in numpy:

myVar = tf.Variable(tf.zeros((x,y), validate_shape=False)

where x=(?) and y=2. But this does not work because zeros does not support 'partially known TensorShape', so, How should I do this in TensorFlow?

回答1:

1) You could use tf.fill(dims, value=0.0) which works with dynamic shapes.

2) You could use a placeholder for the variable dimension, like e.g.:

m = tf.placeholder(tf.int32, shape=[])
x = tf.zeros(shape=[m])

with tf.Session() as sess:
    print(sess.run(x, feed_dict={m: 5}))

回答2:

If you know the shape out of the session, this could help.

import tensorflow as tf
import numpy as np

v = tf.Variable([], validate_shape=False)

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    print(sess.run(v, feed_dict={v: np.zeros((3,4))}))
    print(sess.run(v, feed_dict={v: np.zeros((2,2))}))