Reshaping a Tensor in TensorFlow with scalar tensors

Question

I have a tensor, X of shape (T, n, k) .

If I knew the shape beforehand, it is easy to reshape, tf.reshape(X, (T * n, k)) where T, n, k are ints, not tensors. But is there a way to do this if I don't know the shapes. It seems that getting the shapes like shape = tf.shape(X) and reshaping doesn't work. That is,

tf.reshape(X, (tf.shape[0] * tf.shape[1], tf.shape[2]))

Any ideas? In my application, T and k are known before runtime but n is only known at runtime.

Answer 1

Take a look at this:

import tensorflow as tf

a, b, c = 2, 3, 4
x = tf.Variable(tf.random_normal([a, b, c], mean=0.0, stddev=1.0, dtype=tf.float32))
s = tf.shape(x)

init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
v1, v2, v3 = sess.run(s)
y = tf.reshape(x, [v1 * v2, v3])
shape = tf.shape(y)

print sess.run(y)
print sess.run(shape)

I am getting the shape of the variable after it's initialization and then use it later. Also take a look at this answer , as it deals with a similar thing.

Answer 2

Now that you told that you use placeholders to populate data, it started to make sense. Here is an example of how can you reshape your data in this case:

import tensorflow as tf
import numpy as np
data = np.random.rand(2, 3, 4)

x = tf.placeholder("float", None)
s = tf.shape(x)

sess = tf.Session()
shape_original = sess.run(s, feed_dict={x: data})

x_ = tf.reshape(x, [shape_original[0] * shape_original[1], shape_original[2]])
s_ = tf.shape(x_)

shape_now = sess.run(s_, feed_dict={x: data})
print 'Original\t', shape_original
print 'Now\t\t\t', shape_now

sess.close()