tf.keras.ops.batch_normalization | TensorFlow v2.16.1 (original) (raw)
Normalizes x by mean and variance.
View aliases
Main aliases
tf.keras.ops.nn.batch_normalization
tf.keras.ops.batch_normalization(
x, mean, variance, axis, offset=None, scale=None, epsilon=0.001
)
This op is typically used by the batch normalization step in a neural network. It normalizes the input tensor along the given axis.
| Args | |
|---|---|
| x | Input tensor. |
| mean | A mean vector of the same length as the axis dimension of the input thensor. |
| variance | A variance vector of the same length as the axis dimension of the input tensor. |
| axis | Integer, the axis that should be normalized. |
| offset | An offset vector of the same length as the axis dimension of the input tensor. If not None, offset is added to the normalized tensor. Defaults to None. |
| scale | A scale vector of the same length as the axis dimension of the input tensor. If not None, the normalized tensor is multiplied byscale. Defaults to None. |
| epsilon | Small float added to variance to avoid dividing by zero. Defaults to 1e-3. |
| Returns |
|---|
| The normalized tensor. |
Example:
x = keras.ops.convert_to_tensor(
[[0.1, 0.2, 0.3], [0.4, 0.5, 0.6], [0.7, 0.8, 0.9]]
)
keras.ops.batch_normalization(
x,
mean=[0.4, 0.5, 0.6],
variance=[0.67, 0.67, 0.67],
axis=-1
)
array([[-3.6624e-01, -3.6624e-01, -3.6624e-01],
[-4.6445e-09, 0.0000e+00, -1.8578e-08],
[ 3.6624e-01, 3.6624e-01, 3.6624e-01]])