tf.raw_ops.FusedBatchNormV2

Batch normalization.

View aliases

Compat aliases for migration

tf.compat.v1.raw_ops.FusedBatchNormV2

tf.raw_ops.FusedBatchNormV2(
    x,
    scale,
    offset,
    mean,
    variance,
    epsilon=0.0001,
    exponential_avg_factor=1,
    data_format='NHWC',
    is_training=True,
    name=None
)

Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW". The size of 1D Tensors matches the dimension C of the 4D Tensors.

Args
`x`	A `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`. A 4D Tensor for input data.
`scale`	A `Tensor`. Must be one of the following types: `float32`. A 1D Tensor for scaling factor, to scale the normalized x.
`offset`	A `Tensor`. Must have the same type as `scale`. A 1D Tensor for offset, to shift to the normalized x.
`mean`	A `Tensor`. Must have the same type as `scale`. A 1D Tensor for population mean. Used for inference only; must be empty for training.
`variance`	A `Tensor`. Must have the same type as `scale`. A 1D Tensor for population variance. Used for inference only; must be empty for training.
`epsilon`	An optional `float`. Defaults to `0.0001`. A small float number added to the variance of x.
`exponential_avg_factor`	An optional `float`. Defaults to `1`.
`data_format`	An optional `string` from: `"NHWC", "NCHW"`. Defaults to `"NHWC"`. The data format for x and y. Either "NHWC" (default) or "NCHW".
`is_training`	An optional `bool`. Defaults to `True`. A bool value to indicate the operation is for training (default) or inference.
`name`	A name for the operation (optional).

Returns
A tuple of `Tensor` objects (y, batch_mean, batch_variance, reserve_space_1, reserve_space_2).
`y`	A `Tensor`. Has the same type as `x`.
`batch_mean`	A `Tensor`. Has the same type as `scale`.
`batch_variance`	A `Tensor`. Has the same type as `scale`.
`reserve_space_1`	A `Tensor`. Has the same type as `scale`.
`reserve_space_2`	A `Tensor`. Has the same type as `scale`.

tf.raw_ops.FusedBatchNormV2

View aliases

Args

Returns