| |
| |
| ``mx.symbol.InstanceNorm`` |
| ==================================================== |
| |
| Description |
| ---------------------- |
| |
| Applies instance normalization to the n-dimensional input array. |
| |
| This operator takes an n-dimensional input array where (n>2) and normalizes |
| the input using the following formula: |
| |
| .. math:: |
| |
| out = \frac{x - mean[data]}{ \sqrt{Var[data]} + \epsilon} * gamma + beta |
| |
| This layer is similar to batch normalization layer (`BatchNorm`) |
| with two differences: first, the normalization is |
| carried out per example (instance), not over a batch. Second, the |
| same normalization is applied both at test and train time. This |
| operation is also known as `contrast normalization`. |
| |
| If the input data is of shape [batch, channel, spacial_dim1, spacial_dim2, ...], |
| `gamma` and `beta` parameters must be vectors of shape [channel]. |
| |
| This implementation is based on this paper [1]_ |
| |
| .. [1] Instance Normalization: The Missing Ingredient for Fast Stylization, |
| D. Ulyanov, A. Vedaldi, V. Lempitsky, 2016 (arXiv:1607.08022v2). |
| |
| |
| **Example**:: |
| |
| |
| // Input of shape (2,1,2) |
| x = [[[ 1.1, 2.2]], |
| [[ 3.3, 4.4]]] |
| |
| // gamma parameter of length 1 |
| gamma = [1.5] |
| |
| // beta parameter of length 1 |
| beta = [0.5] |
| |
| // Instance normalization is calculated with the above formula |
| InstanceNorm(x,gamma,beta) = [[[-0.997527 , 1.99752665]], |
| [[-0.99752653, 1.99752724]]] |
| |
| |
| |
| |
| Usage |
| ---------- |
| |
| .. code:: r |
| |
| mx.symbol.InstanceNorm(...) |
| |
| Arguments |
| ------------------ |
| |
| +----------------------------------------+------------------------------------------------------------+ |
| | Argument | Description | |
| +========================================+============================================================+ |
| | ``data`` | NDArray-or-Symbol. | |
| | | | |
| | | An n-dimensional input array (n > 2) of the form [batch, | |
| | | channel, spatial_dim1, spatial_dim2, | |
| | | ...]. | |
| +----------------------------------------+------------------------------------------------------------+ |
| | ``gamma`` | NDArray-or-Symbol. | |
| | | | |
| | | A vector of length 'channel', which multiplies the | |
| | | normalized | |
| | | input. | |
| +----------------------------------------+------------------------------------------------------------+ |
| | ``beta`` | NDArray-or-Symbol. | |
| | | | |
| | | A vector of length 'channel', which is added to the | |
| | | product of the normalized input and the | |
| | | weight. | |
| +----------------------------------------+------------------------------------------------------------+ |
| | ``eps`` | float, optional, default=0.00100000005. | |
| | | | |
| | | An `epsilon` parameter to prevent division by 0. | |
| +----------------------------------------+------------------------------------------------------------+ |
| | ``name`` | string, optional. | |
| | | | |
| | | Name of the resulting symbol. | |
| +----------------------------------------+------------------------------------------------------------+ |
| |
| Value |
| ---------- |
| |
| ``out`` The result mx.symbol |
| |
| |
| Link to Source Code: http://github.com/apache/incubator-mxnet/blob/1.6.0/src/operator/instance_norm.cc#L95 |
| |
