Function Differences with tf.keras.initializers.Constant

tf.keras.initializers.Constant

tf.keras.initializers.Constant(value=0)

For more information, see tf.keras.initializers.Constant.

mindspore.common.initializer.Constant

mindspore.common.initializer.Constant(value)

For more information, see mindspore.common.initializer.Constant.

Usage

TensorFlow: The function input parameter value supports scalar, list, tuple, and array types. Suppose you need to create a tensor of the specified shape, and the input parameter value of this interface is a list or an array, the number of elements contained in value must be less than or equal to the number of elements with the specified shape. If the number of elements contained in value must be less than the number of elements with the specified shape, the last element of value is used to fill the remaining positions.

MindSpore: The function input parameter value supports scalar and array types. When value is an array, only a tensor with the same shape as value can be generated.

Code Example

As an example, if the input is an array, the code sample is as follows:

TensorFlow:

import numpy as np
import tensorflow as tf

value = np.array([0, 1, 2, 3, 4, 5, 6, 7])
value = value.reshape([2, 4])

init = tf.keras.initializers.Constant(value)

x = init(shape=(2, 4))
y = init(shape=(3, 4))

with tf.Session() as sess:
    print(x.eval(), "\n")
    print(y.eval())

# out:
# [[0. 1. 2. 3.]
#  [4. 5. 6. 7.]]

# [[0. 1. 2. 3.]
#  [4. 5. 6. 7.]
#  [7. 7. 7. 7.]]

MindSpore:

import numpy as np
import mindspore as ms
from mindspore.common.initializer import initializer, Constant

value = np.array([0, 1, 2, 3, 4, 5, 6, 7])
value = value.reshape([2, 4])

x = initializer(Constant(value), shape=[2, 4], dtype=ms.float32)

print(x)

# out:
# [[0. 1. 2. 3.]
#  [4. 5. 6. 7.]]