[FIXED] Add None dimension in tensorflow 2.0

Issue

I have a tensor xx with shape:

>>> xx.shape
TensorShape([32, 32, 256])

How can I add a leading None dimension to get:

>>> xx.shape
TensorShape([None, 32, 32, 256])

I have seen many answers here but all are related to TF 1.x

What is the straight forward way for TF 2.0?

Solution

You can either use "None" or numpy's "newaxis" to create the new dimension.

General Tip: You can also use None in place of np.newaxis; These are in fact the same objects.

Below is the code that explains both the options.

try:
  %tensorflow_version 2.x
except Exception:
  pass
import tensorflow as tf

print(tf.__version__)

# TensorFlow and tf.keras
from tensorflow import keras

# Helper libraries
import numpy as np

#### Import the Fashion MNIST dataset
fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()

#Original Dimension
print(train_images.shape)

train_images1 = train_images[None,:,:,:]
#Add Dimension using None
print(train_images1.shape)

train_images2 = train_images[np.newaxis is None,:,:,:]
#Add dimension using np.newaxis
print(train_images2.shape)

#np.newaxis and none are same
np.newaxis is None

The Output of the above code is

2.1.0
(60000, 28, 28)
(1, 60000, 28, 28)
(1, 60000, 28, 28)
True

Answered By - user8879803