Reshape tensor using placeholder value

Question

I want to reshape a tensor using the [int, -1] notation (to flatten an image, for example). But I don't know the first dimension ahead of time. One use case is train on a large batch, then evaluate on a smaller batch.

Why does this give the following error: got list containing Tensors of type '_Message'?

import tensorflow as tf
import numpy as np

x = tf.placeholder(tf.float32, shape=[None, 28, 28])
batch_size = tf.placeholder(tf.int32)

def reshape(_batch_size):
    return tf.reshape(x, [_batch_size, -1])

reshaped = reshape(batch_size)


with tf.Session() as sess:
    sess.run([reshaped], feed_dict={x: np.random.rand(100, 28, 28), batch_size: 100})

    # Evaluate
    sess.run([reshaped], feed_dict={x: np.random.rand(8, 28, 28), batch_size: 8})

Note: when I have the reshape outside of the function it seems to work, but I have very large models that I use multiple times, so I need to keep them in a function and pass the dim using an argument.

Answer 1

To make this work, replace the function:

def reshape(_batch_size):
    return tf.reshape(x, [_batch_size, -1])

…with the function:

def reshape(_batch_size):
    return tf.reshape(x, tf.pack([_batch_size, -1]))

The reason for the error is that tf.reshape() expects a value that is convertible to a tf.Tensor as its second argument. TensorFlow will automatically convert a list of Python numbers to a tf.Tensor but will not automatically convert a mixed list of numbers and tensors (such as a tf.placeholder())—instead raising the somewhat unintuitive error message you saw.

The tf.pack() op takes a list of objects convertible to a tensor, and converts each element individually, so it can handle the combination of a placeholder and an integer.