If the shape of the tensor to initialize is two-dimensional, it is initialized with an orthogonal matrix obtained from the QR decomposition of a matrix of random numbers drawn from a normal distribution. If the matrix has fewer rows than columns then the output will have orthogonal rows. Otherwise, the output will have orthogonal columns.

If the shape of the tensor to initialize is more than two-dimensional,
a matrix of shape `(shape[1] * ... * shape[n - 1], shape[n])`

is initialized, where `n`

is the length of the shape vector.
The matrix is subsequently reshaped to give a tensor of the desired shape.

## Arguments

- gain
Multiplicative factor to apply to the orthogonal matrix.

- seed
An integer. Used to make the behavior of the initializer deterministic.

## Value

An `Initializer`

instance that can be passed to layer or variable
constructors, or called directly with a `shape`

to return a Tensor.

## Examples

```
# Standalone usage:
initializer <- initializer_orthogonal()
values <- initializer(shape = c(2, 2))
```

```
# Usage in a Keras layer:
initializer <- initializer_orthogonal()
layer <- layer_dense(units = 3, kernel_initializer = initializer)
```

## See also

Other random initializers: `initializer_glorot_normal()`

`initializer_glorot_uniform()`

`initializer_he_normal()`

`initializer_he_uniform()`

`initializer_lecun_normal()`

`initializer_lecun_uniform()`

`initializer_random_normal()`

`initializer_random_uniform()`

`initializer_truncated_normal()`

`initializer_variance_scaling()`

Other initializers: `initializer_constant()`

`initializer_glorot_normal()`

`initializer_glorot_uniform()`

`initializer_he_normal()`

`initializer_he_uniform()`

`initializer_identity()`

`initializer_lecun_normal()`

`initializer_lecun_uniform()`

`initializer_ones()`

`initializer_random_normal()`

`initializer_random_uniform()`

`initializer_truncated_normal()`

`initializer_variance_scaling()`

`initializer_zeros()`