Fix

Signed-off-by: Alex Black <[email protected]>

Author: AlexDBlack

dl4j-examples_javafx/src/main/java/org/deeplearning4j/examples/userInterface/util/UIExampleUtils.java

@@ -0,0 +1,97 @@
+/*******************************************************************************
+ * Copyright (c) 2015-2019 Skymind, Inc.
+ *
+ * This program and the accompanying materials are made available under the
+ * terms of the Apache License, Version 2.0 which is available at
+ * https://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.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ ******************************************************************************/
+
+package org.deeplearning4j.examples.userInterface.util;
+
+import org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator;
+import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
+import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
+import org.deeplearning4j.nn.conf.inputs.InputType;
+import org.deeplearning4j.nn.conf.layers.ConvolutionLayer;
+import org.deeplearning4j.nn.conf.layers.DenseLayer;
+import org.deeplearning4j.nn.conf.layers.OutputLayer;
+import org.deeplearning4j.nn.conf.layers.SubsamplingLayer;
+import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
+import org.deeplearning4j.nn.weights.WeightInit;
+import org.nd4j.linalg.activations.Activation;
+import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
+import org.nd4j.linalg.learning.config.Adam;
+import org.nd4j.linalg.lossfunctions.LossFunctions;
+
+import java.io.IOException;
+
+/**
+ * Created by Alex on 11/11/2016.
+ */
+public class UIExampleUtils {
+
+    public static MultiLayerNetwork getMnistNetwork(){
+
+        int nChannels = 1; // Number of input channels
+        int outputNum = 10; // The number of possible outcomes
+        int seed = 123; //
+
+        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
+            .seed(seed)
+            .l2(0.0005)
+            .weightInit(WeightInit.XAVIER)
+            .updater(new Adam(0.001))
+            .list()
+            .layer(new ConvolutionLayer.Builder(5, 5)
+                //nIn and nOut specify depth. nIn here is the nChannels and nOut is the number of filters to be applied
+                .nIn(nChannels)
+                .stride(1, 1)
+                .nOut(20)
+                .activation(Activation.LEAKYRELU)
+                .build())
+            .layer(new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX)
+                .kernelSize(2,2)
+                .stride(2,2)
+                .build())
+            .layer(new ConvolutionLayer.Builder(5, 5)
+                //Note that nIn need not be specified in later layers
+                .stride(1, 1)
+                .nOut(50)
+                .activation(Activation.LEAKYRELU)
+                .build())
+            .layer(new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX)
+                .kernelSize(2,2)
+                .stride(2,2)
+                .build())
+            .layer(new DenseLayer.Builder().activation(Activation.LEAKYRELU).nOut(500).build())
+            .layer(new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
+                .nOut(outputNum)
+                .activation(Activation.SOFTMAX)
+                .build())
+            .setInputType(InputType.convolutionalFlat(28,28,1))
+            .build();
+
+
+        MultiLayerNetwork net = new MultiLayerNetwork(conf);
+        net.init();
+
+        return net;
+    }
+
+    public static DataSetIterator getMnistData(){
+        try{
+            return new MnistDataSetIterator(64,true,12345);
+        }catch (IOException e){
+            throw new RuntimeException(e);
+        }
+    }
+
+}