Function Differences with tf.fill
tf.fill
tf.fill(dims, value, name=None) -> Tensor
For more information, see tf.fill.
mindspore.ops.fill
mindspore.ops.fill(type, shape, value) -> Tensor
For more information, see mindspore.ops.fill.
Differences
TensorFlow: is used to generate a tensor with scalar values.
MindSpore: MindSpore API implements the same function as TensorFlow, and only the parameter names are different.
Categories |
Subcategories |
TensorFlow |
MindSpore |
Differences |
---|---|---|---|---|
Parameters |
Parameter 1 |
dims |
shape |
Same function, different parameter names |
Parameter 2 |
value |
value |
- |
|
Parameter 3 |
name |
- |
Not involved |
|
Parameter 4 |
- |
type |
Specify the data type of the output Tensor |
Code Example 1
Both APIs implement the same function. MindSpore only has one more parameter specifying the type of output, and the rest of the parameters are used in the same way.
# TensorFlow
import tensorflow as tf
import numpy as np
dims = np.array([2,3])
value = 9
output = tf.fill(dims, value)
output_m = output.numpy()
print(output_m)
#[[9 9 9]
# [9 9 9]]
# MindSpore
import mindspore
import mindspore.ops as ops
type = mindspore.int32
shape = tuple((2,3))
value = 9
output = ops.fill(type, shape, value)
print(output)
#[[9 9 9]
# [9 9 9]]