# Function Differences with tf.keras.layers.BatchNormalization
[](https://gitee.com/mindspore/docs/blob/r2.1/docs/mindspore/source_en/note/api_mapping/tensorflow_diff/BatchNorm2d.md)
## tf.keras.layers.BatchNormalization
```text
tf.keras.layers.BatchNormalization(
axis=-1,
momentum=0.99,
epsilon=0.001,
center=True,
scale=True,
beta_initializer='zeros',
gamma_initializer='ones',
moving_mean_initializer='zeros',
moving_variance_initializer='ones',
beta_regularizer=None,
gamma_regularizer=None,
beta_constraint=None,
gamma_constraint=None,
**kwargs
)(inputs, training) -> Tensor
```
For more information, see [tf.keras.layers.BatchNormalization](https://tensorflow.google.cn/versions/r2.6/api_docs/python/tf/keras/layers/BatchNormalization).
## mindspore.nn.BatchNorm2d
```text
class mindspore.nn.BatchNorm2d(
num_features,
eps=1e-5,
momentum=0.9,
affine=True,
gamma_init='ones',
beta_init='zeros',
moving_mean_init='zeros',
moving_var_init='ones',
use_batch_statistics=None,
data_format='NCHW'
)(x) -> Tensor
```
For more information, see [mindspore.nn.BatchNorm2d](https://www.mindspore.cn/docs/en/r2.1/api_python/nn/mindspore.nn.BatchNorm2d.html).
## Differences
TensorFlow: Perform batch normalization on the input data.
MindSpore: Batch Normalization Layer (BNL) is applied to the input four-dimensional data. Batch normalization is applied on four-dimensional inputs (small batches of two-dimensional inputs with additional channel dimensionality) to avoid internal covariate shifts.
| Categories | Subcategories |TensorFlow | MindSpore | Differences |
| --- | --- | --- | --- |---|
|Parameters | Parameter 1 | axis | - | The axis that should be normalized (usually the feature axis). MindSpore does not have this parameter |
| | Parameter 2 | momentum | momentum | - |
| | Parameter 3 | epsilon | eps | Same function, different parameter names |
| | Parameter 4 | center | - | If True, the offset beta is added to the normalization tensor. If False, beta is ignored. MindSpore does not have this parameter |
| | Parameter 5 | scale | - | If True, multiply by gamma. if False, do not use gamma. MindSpore does not have this parameter |
| | Parameter 6 | beta_initializer | beta_init | Same function, different parameter names |
| | Parameter 7 | gamma_initializer | gamma_init | Same function, different parameter names |
| | Parameter 8 | moving_mean_initializer | moving_mean_init | Same function, different parameter names |
| | Parameter 9 | moving_variance_initializer | moving_var_init | Same function, different parameter names |
| | Parameter 10 | beta_regularizer | - | Optional regularizer for beta weights. MindSpore does not have this parameter |
| | Parameter 11 | gamma_regularizer | - | Optional regularizer for gamma weights. MindSpore does not have this parameter |
| | Parameter 12 | beta_constraint | - | Optional constraint on the beta weight. MindSpore does not have this parameter |
| | Parameter 13 | gamma_constraint | - | Optional constraint on gamma weights. MindSpore does not have this parameter |
| | Parameter 14 | **kwargs | - | Not involved |
| | Parameter 15 | - | num_features | Number of channels, input C in Tensor shape (N,C,H,W)(N,C,H,W). TensorFlow does not have this parameter |
| | Parameter 16 | - | affine | bool type. When set to True, the γ and β values can be learned. Default value: True. TensorFlow does not have this parameter |
| | Parameter 17 | - | use_batch_statistics | If True, the mean and variance values of the current batch data are used, and the running mean and running variance are tracked.
If False, the mean and variance values of the specified values are used and no statistical values are tracked.
If None, use_batch_statistics is automatically set to True or False depending on the training and validation modes. use_batch_statistics is set to True for training. use_batch_statistics is automatically set to False for validation. Default value: None. TensorFlow does not have this parameter |
| | Parameter 18 | - | data_format | MindSpore can specify the input data format as "NHWC" or "NCHW". Default value: "NCHW". TensorFlow does not have this parameter |
| Inputs |Input 1 | inputs | x | Same function, different parameter names |
| | Input 2 | training | - | Not involved |
### Code Example
> Both APIs have the same function and are used in the same way.
```python
# TensorFlow
import tensorflow as tf
inputx = [[[[1, 2],
[2, 1]],
[[3, 4],
[4, 3]]],
[[[5, 6],
[6, 5]],
[[7, 8],
[8, 7]]]]
input_tf = tf.constant(inputx, dtype=tf.float32)
output_tf = tf.keras.layers.BatchNormalization(axis=3, momentum=0.1, epsilon=1e-5)
output = output_tf(input_tf, training=False)
print(output.numpy())
# [[[[0.999995 1.99999 ]
# [1.99999 0.999995 ]]
#
# [[2.999985 3.99998 ]
# [3.99998 2.999985 ]]]
#
#
# [[[4.999975 5.99997 ]
# [5.99997 4.999975 ]]
#
# [[6.9999647 7.99996 ]
# [7.99996 6.9999647]]]]
# MindSpore
from mindspore import Tensor, nn
import numpy as np
m = nn.BatchNorm2d(num_features=2, momentum=0.9)
input_ms = Tensor(np.array([[[[1, 2], [2, 1]],
[[3, 4], [4, 3]]],
[[[5, 6], [6, 5]],
[[7, 8], [8, 7]]]]).astype(np.float32))
output = m(input_ms)
print(output)
# [[[[0.99999493 1.9999899 ]
# [1.9999899 0.99999493]]
#
# [[2.9999847 3.9999797 ]
# [3.9999797 2.9999847 ]]]
#
#
# [[[4.9999747 5.9999695 ]
# [5.9999695 4.9999747 ]]
#
# [[6.9999647 7.9999595 ]
# [7.9999595 6.9999647 ]]]]
```