What is the alternative of numpy.newaxis in tensorflow?

The corresponding command is tf.newaxis (or None, as in numpy). It does not have an entry on its own in tensorflow's documentation, but is briefly mentioned on the doc page of tf.stride_slice.

x = tf.ones((10,10,10))
y = x[:, tf.newaxis] # or y = x [:, None]
print(y.shape)
# prints (10, 1, 10, 10)

Using tf.expand_dims is fine too but, as stated in the link above,

Those interfaces are much more friendly, and highly recommended.

a = a[..., tf.newaxis].astype("float32")

This Works as well

I think that would be tf.expand_dims -

tf.expand_dims(a, 1) # Or tf.expand_dims(a, -1)

Basically, we list the axis ID where this new axis is to be inserted and the trailing axes/dims are pushed-back.

From the linked docs, here's few examples of expanding dimensions -

# 't' is a tensor of shape [2]
shape(expand_dims(t, 0)) ==> [1, 2]
shape(expand_dims(t, 1)) ==> [2, 1]
shape(expand_dims(t, -1)) ==> [2, 1]

# 't2' is a tensor of shape [2, 3, 5]
shape(expand_dims(t2, 0)) ==> [1, 2, 3, 5]
shape(expand_dims(t2, 2)) ==> [2, 3, 1, 5]
shape(expand_dims(t2, 3)) ==> [2, 3, 5, 1]