Nimitz14 - 1 year ago 873
Python Question

# Tensorflow: Using tf.slice to split the input

I'm trying to split my input layer into different sized parts. I'm trying to use tf.slice to do that but it's not working.

Some sample code:

``````import tensorflow as tf
import numpy as np

ph = tf.placeholder(shape=[None,3], dtype=tf.int32)

x = tf.slice(ph, [0, 0], [3, 2])

input_ = np.array([[1,2,3],
[3,4,5],
[5,6,7]])

with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
print sess.run(x, feed_dict={ph: input_})
``````

Output:

``````[[1 2]
[3 4]
[5 6]]
``````

This works and is roughly what I want to happen, but I have to specify the first dimension (
`3`
in this case). I can't know though how many vectors I'll be inputting, that's why I'm using a
`placeholder`
with
`None`
in the first place!

Is it possible to use
`slice`
in such a way that it will work when a dimension is unknown until runtime?

I've tried using a
`placeholder`
that takes its value from
`ph.get_shape()[0]`
like so:
`x = tf.slice(ph, [0, 0], [num_input, 2])`
. but that didn't work either.

You can specify one negative dimension in the `size` parameter of `tf.slice`. The negative dimension tells Tensorflow to dynamically determine the right value basing its decision on the other dimensions.

``````import tensorflow as tf
import numpy as np

ph = tf.placeholder(shape=[None,3], dtype=tf.int32)

# look the -1 in the first position
x = tf.slice(ph, [0, 0], [-1, 2])

input_ = np.array([[1,2,3],
[3,4,5],
[5,6,7]])

with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
print(sess.run(x, feed_dict={ph: input_})
``````
Recommended from our users: Dynamic Network Monitoring from WhatsUp Gold from IPSwitch. Free Download