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/*
* Copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file except in compliance
* with the License. A copy of the License is located at
*
* http://aws.amazon.com/apache2.0/
*
* or in the "license" file accompanying this file. This file is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES
* OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions
* and limitations under the License.
*/
package ai.djl.training.initializer;
import ai.djl.ndarray.NDArray;
import ai.djl.ndarray.NDManager;
import ai.djl.ndarray.types.DataType;
import ai.djl.ndarray.types.Shape;
/**
* {@code UniformInitializer} initializes weights with random values uniformly sampled from a given
* range.
*/
public class UniformInitializer implements Initializer {
private float scale;
/** Creates an instance of {@code UniformInitializer} with a default {@code scale} of 0.07. */
public UniformInitializer() {
this.scale = 0.07f;
}
/**
* Initializes a uniform initializer.
*
* @param scale the bound on the range of the generated random values. Values are generated from
* the range [-`scale`, `scale`]. Default scale is 0.07.
*/
public UniformInitializer(float scale) {
this.scale = scale;
}
/** {@inheritDoc} */
@Override
public NDArray initialize(NDManager manager, Shape shape, DataType dataType) {
return manager.randomUniform(-scale, scale, shape, dataType, manager.getDevice());
}
}