com.simiacryptus.mindseye.layers.java.ReshapeLayer.java Source code

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Here is the source code for com.simiacryptus.mindseye.layers.java.ReshapeLayer.java

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/*
 * Copyright (c) 2018 by Andrew Charneski.
 *
 * The author licenses this file to you under the
 * Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance
 * with the License.  You may obtain a copy
 * of the License at
 *
 *   http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing,
 * software distributed under the License 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 com.simiacryptus.mindseye.layers.java;

import com.google.gson.JsonObject;
import com.simiacryptus.mindseye.lang.*;
import com.simiacryptus.util.JsonUtil;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import javax.annotation.Nonnull;
import javax.annotation.Nullable;
import java.util.Arrays;
import java.util.List;
import java.util.Map;
import java.util.UUID;

/**
 * A dense matrix operator using vector-matrix multiplication. Represents a fully connected key of synapses, where all
 * inputs are connected to all outputs via seperate coefficients.
 */
@SuppressWarnings("serial")
public class ReshapeLayer extends LayerBase {
    private static final Logger log = LoggerFactory.getLogger(ReshapeLayer.class);
    /**
     * The Output dims.
     */
    @Nullable
    public final int[] outputDims;

    /**
     * Instantiates a new Img eval key.
     */
    private ReshapeLayer() {
        outputDims = null;
    }

    /**
     * Instantiates a new Fully connected key.
     *
     * @param outputDims the output dims
     */
    public ReshapeLayer(@Nonnull final int... outputDims) {
        this.outputDims = Arrays.copyOf(outputDims, outputDims.length);
    }

    /**
     * Instantiates a new Img eval key.
     *
     * @param json the json
     * @param rs   the rs
     */
    protected ReshapeLayer(@Nonnull final JsonObject json, Map<CharSequence, byte[]> rs) {
        super(json);
        outputDims = JsonUtil.getIntArray(json.getAsJsonArray("outputDims"));
    }

    /**
     * From json img eval key.
     *
     * @param json the json
     * @param rs   the rs
     * @return the img eval key
     */
    public static ReshapeLayer fromJson(@Nonnull final JsonObject json, Map<CharSequence, byte[]> rs) {
        return new ReshapeLayer(json, rs);
    }

    @Nullable
    @Override
    public Result evalAndFree(@Nonnull final Result... inObj) {
        assert 1 == inObj.length;
        TensorList data = inObj[0].getData();
        @Nonnull
        int[] inputDims = data.getDimensions();
        ReshapedTensorList reshapedTensorList = new ReshapedTensorList(data, outputDims);
        data.freeRef();
        return new Result(reshapedTensorList, (DeltaSet<UUID> buffer, TensorList delta) -> {
            @Nonnull
            ReshapedTensorList tensorList = new ReshapedTensorList(delta, inputDims);
            inObj[0].accumulate(buffer, tensorList);
        }) {

            @Override
            protected void _free() {
                for (@Nonnull
                Result result : inObj) {
                    result.freeRef();
                }
            }

            @Override
            public boolean isAlive() {
                return inObj[0].isAlive();
            }
        };

    }

    @Nonnull
    @Override
    public JsonObject getJson(Map<CharSequence, byte[]> resources, DataSerializer dataSerializer) {
        @Nonnull
        final JsonObject json = super.getJsonStub();
        json.add("outputDims", JsonUtil.getJson(outputDims));
        return json;
    }

    @Nonnull
    @Override
    public List<double[]> state() {
        return Arrays.asList();
    }

}