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.]]]