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# Glorot

public class Glorot

The Glorot initializer, also called Xavier initializer.

Draws samples from a random distribution.

If the distribution is TRUNCATED_NORMAL, then the distribution is centered on 0 with ``` stddev = Math.sqrt(2. / (fanIn + fanOut)) ``` where ``` fanIn ``` is the number of input units in the weight tensor and ``` fanOut ``` is the number of output units in the weight tensor.

If the distribution is UNIFORM, then samples are drawn from a uniform distribution within ``` [-limit, limit] ``` , where ``` limit = sqrt(6 / (fanIn + fanOut)) ``` ( ``` fanIn ``` is the number of input units in the weight tensor and ``` fanOut ``` is the number of output units).

Examples:

Glorot Normal:

```     long seed = 1001l;
Glorot<TFloat32, TFloat32> initializer =
new org.tensorflow.framework.initializers.Glorot<>(tf,
Distribution.TRUNCATED_NORMAL, seed);
Operand<TFloat32> values =
initializer.call(tf.constant(Shape.of(2,2)), TFloat32.class);
```

Glorot Uniform:

```    long seed = 1001l;
Glorot<TFloat32, TFloat32> initializer =
new org.tensorflow.framework.initializers.Glorot<>(tf,
Distribution.UNIFORM, seed);
Operand<TFloat32> values =
initializer.call(tf.constant(Shape.of(2,2)), TFloat32.class);
```

NOTE:

For a GlorotNormal equivalent initializer, use ``` TRUNCATED_NORMAL ``` for the distribution parameter.

For a GlorotUniform equivalent initializer, use ``` UNIFORM ``` for the distribution parameter.

### Constants

 double SCALE

### Public Constructors

 (Ops tf, VarianceScaling.Distribution distribution, long seed) Creates a Glorot initializer

## Constants

#### public static final double SCALE

Constant Value: 1.0

## Public Constructors

#### public Glorot (Ops tf, VarianceScaling.Distribution distribution, long seed)

Creates a Glorot initializer

##### Parameters
 tf the TensorFlow Ops The distribution type for the Glorot initializer. the seed for random number generation. An initializer created with a given seed will always produce the same random tensor for a given shape and dtype.
• ``` VarianceScaling.Distribution ```