# How to reshape and padding zeros in tf.Tensor

## Issue

The following code is trying to convert the tensor into (x,y) dimension arrays in Tensorflow.

The "a" can be convert to "b" by using this code, but the "c" can’t.

Here is the test code:

``````def reshape_array(old_array, x, y):
new_array = tf.reshape(old_array, [-1])

current_size = tf.size(new_array)
reshape_size = tf.math.multiply(x, y)

diff = tf.math.subtract(reshape_size, current_size)
if tf.greater_equal(diff, tf.constant([0])):
new_array = tf.pad(new_array, [[0,0],[0, diff]], mode='CONSTANT', constant_values=0)
new_array = tf.reshape(new_array, (x, y))
else:
new_array = tf.slice(new_array, begin=[0], size=[reshape_size])
new_array = tf.reshape(new_array, (x, y))

return tf.cast(new_array, old_array.dtype)

a = tf.zeros(256*192*1)
print("a.shape: {}".format(a.shape))
b = reshape_array(a, 28, 28)
print("b.shape: {}".format(b.shape))

c = tf.constant([1, 2, 3, 4, 5, 6])
print("c.shape: {}".format(c.shape))
d = reshape_array(c, 28, 28)
print("d.shape: {}".format(d.shape))
``````

Here is the output:

``````a.shape: (49152,)
b.shape: (28, 28)
c.shape: (6,)

---------------------------------------------------------------------------
InvalidArgumentError                      Traceback (most recent call last)
/tmp/ipykernel_7071/4036910860.py in <cell line: 26>()
24 c = tf.constant([1, 2, 3, 4, 5, 6])
25 print("c.shape: {}".format(c.shape))
---> 26 d = reshape_array(c, 28, 28)
27 print("d.shape: {}".format(d.shape))

/tmp/ipykernel_7071/4036910860.py in reshape_array(old_array, x, y)
9     diff = tf.math.subtract(reshape_size, current_size)
10     if tf.greater_equal(diff, tf.constant([0])):
---> 11         new_array = tf.pad(new_array, [[0,0],[0, diff]], mode='CONSTANT', constant_values=0)
12         new_array = tf.reshape(new_array, (x, y))
13     else:

/usr/local/lib/python3.8/site-packages/tensorflow/python/util/traceback_utils.py in error_handler(*args, **kwargs)
151     except Exception as e:
152       filtered_tb = _process_traceback_frames(e.__traceback__)
--> 153       raise e.with_traceback(filtered_tb) from None
154     finally:
155       del filtered_tb

/usr/local/lib/python3.8/site-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
52   try:
53     ctx.ensure_initialized()
---> 54     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
55                                         inputs, attrs, num_outputs)
56   except core._NotOkStatusException as e:

InvalidArgumentError: The first dimension of paddings must be the rank of inputs[2,2] [6] [Op:Pad]
``````

What’s wrong in my code and how to fix?

## Solution

You a working with a 1D tensor in your second example, so try:

``````import tensorflow as tf

def reshape_array(old_array, x, y):
new_array = tf.reshape(old_array, [-1])

current_size = tf.size(new_array)
reshape_size = tf.math.multiply(x, y)

diff = tf.math.subtract(reshape_size, current_size)
if tf.greater_equal(diff, tf.constant([0])):
print(diff)
new_array = tf.pad(new_array, [[0, diff]], mode='CONSTANT', constant_values=0)
new_array = tf.reshape(new_array, (x, y))
else:
new_array = tf.slice(new_array, begin=[0], size=[reshape_size])
new_array = tf.reshape(new_array, (x, y))

return tf.cast(new_array, old_array.dtype)

a = tf.zeros(256*192*1)
print("a.shape: {}".format(a.shape))
b = reshape_array(a, 28, 28)
print("b.shape: {}".format(b.shape))

c = tf.constant([1, 2, 3, 4, 5, 6])
print("c.shape: {}".format(c.shape))
d = reshape_array(c, 28, 28)
print("d.shape: {}".format(d.shape))
``````

In your case, I would generally prefer using `tf.concat` for padding:

``````def reshape_array(old_array, x, y):
new_array = tf.reshape(old_array, [-1])

current_size = tf.size(new_array)
reshape_size = tf.math.multiply(x, y)

diff = tf.math.subtract(reshape_size, current_size)
if tf.greater_equal(diff, tf.constant([0])):
new_array = tf.concat([new_array, tf.repeat([0], repeats=diff)], axis=0)
new_array = tf.reshape(new_array, (x, y))
else:
new_array = tf.slice(new_array, begin=[0], size=[reshape_size])
new_array = tf.reshape(new_array, (x, y))

return tf.cast(new_array, old_array.dtype)
``````