# Tensorflow Tensor reshape and pad with zeros

## Question:

Is there a way to reshape a tensor and pad any overflow with zeros? I know ndarray.reshape does this, but as I understand it, converting a Tensor to an ndarray would require flip-flopping between the GPU and CPU.

Tensorflow’s reshape() documentation says the TensorShapes need to have the same number of elements, so perhaps the best way would be a pad() and then reshape()?

I’m trying to achieve:

a = tf.Tensor([[1,2],[3,4]])
tf.reshape(a, [2,3])
a => [[1, 2, 3],
[4, 0 ,0]]

As far as I know, there’s no built-in operator that does this (tf.reshape() will give you an error if the shapes do not match). However, you can achieve the same result with a few different operators:

a = tf.constant([[1, 2], [3, 4]])

# Reshape `a` as a vector. -1 means "set this dimension automatically".
a_as_vector = tf.reshape(a, [-1])

# Create another vector containing zeroes to pad `a` to (2 * 3) elements.
zero_padding = tf.zeros([2 * 3] - tf.shape(a_as_vector), dtype=a.dtype)

# Concatenate `a_as_vector` with the padding.

# Reshape the padded vector to the desired shape.

Tensorflow now offers the pad function which performs padding on a tensor in a number of ways(like opencv2’s padding function for arrays):

example from the docs above:

# 't' is [[1, 2, 3],
#         [4, 5, 6]]
# 'paddings' is [[1, 1], [2, 2]]
# rank of 't' is 2.
[0, 0, 1, 2, 3, 0, 0],
[0, 0, 4, 5, 6, 0, 0],
[0, 0, 0, 0, 0, 0, 0]]