Function Differences with tf.expand_dims

tf.expand_dims

tf.expand_dims(x, axis, name=None) -> Tensor

For more information, see tf.expand_dims.

mindspore.ops.expand_dims

mindspore.ops.expand_dims(input_x, axis) -> Tensor

For more information, see mindspore.ops.expand_dims.

Differences

TensorFlow: Add an extra dimension to the input x on the given axis.

MindSpore: MindSpore API implements the same function as TensorFlow, and only the parameter names are different.

Categories	Subcategories	TensorFlow	MindSpore	Differences
Parameters	Parameter 1	x	input_x	Same function, different parameter names
	Parameter 2	axis	axis	-
	Parameter 3	name	-	Not involved

Code Example 1

The two APIs achieve the same function and have the same usage.

# TensorFlow
import numpy as np
import tensorflow as tf

x = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]], dtype=np.float32)
axis = 1
out = tf.expand_dims (x, axis).numpy()
print(out)
# [[[ 1.  2.  3.  4.]]
#  [[ 5.  6.  7.  8.]]
#  [[ 9. 10. 11. 12.]]]

# MindSpore
import mindspore
import numpy as np
import mindspore.ops as ops
from mindspore import Tensor

input_params = Tensor(np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]), mindspore.float32)
axis = 1
output = ops.expand_dims(input_params,  axis)
print(output)
# [[[ 1.  2.  3.  4.]]
#  [[ 5.  6.  7.  8.]]
#  [[ 9. 10. 11. 12.]]]

Code Example 2

The two APIs achieve the same function and have the same usage.

# TensorFlow
import numpy as np
import tensorflow as tf

x = np.array([[1,1,1]], dtype=np.float32)
axis = 2
out = tf.expand_dims (x, axis).numpy()
print(out)
# [[[1.]
#   [1.]
#   [1.]]]


# MindSpore
import mindspore
import numpy as np
import mindspore.ops as ops
from mindspore import Tensor

input_params = Tensor(np.array([[1,1,1]]), mindspore.float32)
axis = 2
output = ops.expand_dims(input_params,  axis)
print(output)
# [[[1.]
#   [1.]
#   [1.]]]