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        ComputationGraphConfiguration.GraphBuilder graph = new NeuralNetConfiguration.Builder().seed(seed)
        		.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
                .updater(new Adam(0.0001))
                .weightInit(new NormalDistribution(0.0, 0.01))
                .biasInit(0)
                .miniBatch(true)
                .cacheMode(cacheMode)
                .trainingWorkspaceMode(workspaceMode)
                .inferenceWorkspaceMode(workspaceMode)
                .graphBuilder();

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Exception in thread "main" org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException: Requires model configuration as either JSON or YAML string.. For more information, see http://deeplearning4j.org/model-import-keras.
	at org.deeplearning4j.nn.modelimport.keras.utils.KerasModelUtils.parseModelConfig(KerasModelUtils.java:333)
	at org.deeplearning4j.nn.modelimport.keras.KerasModel.<init>(KerasModel.java:120)
	at org.deeplearning4j.nn.modelimport.keras.KerasModel.<init>(KerasModel.java:96)
	at org.deeplearning4j.nn.modelimport.keras.utils.KerasModelBuilder.buildModel(KerasModelBuilder.java:307)
	at ma.enset.brain_tumor_segmentation.SemanticSegmentationLoadKeras.run(SemanticSegmentationLoadKeras.java:55)
	at ma.enset.brain_tumor_segmentation.SemanticSegmentationLoadKeras.main(SemanticSegmentationLoadKeras.java:138)

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Exception in thread "main" org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException: Model configuration attribute missing from C:\Users\bismi\AppData\Local\Temp\DL4JKerasModelImport3875863276192533560.bin archive.. For more information, see http://deeplearning4j.org/model-import-keras.
	at org.deeplearning4j.nn.modelimport.keras.utils.KerasModelBuilder.modelHdf5Filename(KerasModelBuilder.java:230)
	at org.deeplearning4j.nn.modelimport.keras.KerasModelImport.importKerasModelAndWeights(KerasModelImport.java:171)
	at org.deeplearning4j.nn.modelimport.keras.KerasModelImport.importKerasModelAndWeights(KerasModelImport.java:73)
	at ma.enset.brain_tumor_segmentation.SemanticSegmentationLoadKeras.run(SemanticSegmentationLoadKeras.java:49)
	at ma.enset.brain_tumor_segmentation.SemanticSegmentationLoadKeras.main(SemanticSegmentationLoadKeras.java:132)

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Exception in thread "main" org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException: Model configuration attribute missing from C:\Users\bismi\AppData\Local\Temp\DL4JKerasModelImport3875863276192533560.bin archive.. For more information, see http://deeplearning4j.org/model-import-keras.
	at org.deeplearning4j.nn.modelimport.keras.utils.KerasModelBuilder.modelHdf5Filename(KerasModelBuilder.java:230)
	at org.deeplearning4j.nn.modelimport.keras.KerasModelImport.importKerasModelAndWeights(KerasModelImport.java:171)
	at org.deeplearning4j.nn.modelimport.keras.KerasModelImport.importKerasModelAndWeights(KerasModelImport.java:73)
	at ma.enset.brain_tumor_segmentation.SemanticSegmentationLoadKeras.run(SemanticSegmentationLoadKeras.java:49)
	at ma.enset.brain_tumor_segmentation.SemanticSegmentationLoadKeras.main(SemanticSegmentationLoadKeras.java:132)

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package ma.enset.brain_tumor_segmentation;

import java.io.File;
import java.io.IOException;
import java.net.URI;
import java.util.Arrays;

import org.datavec.api.io.labels.PathLabelGenerator;
import org.datavec.api.writable.NDArrayWritable;
import org.datavec.api.writable.Writable;

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/*******************************************************************************
 * Copyright (c) 2015-2018 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

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package ma.enset.brain_tumor_segmentation;

import org.deeplearning4j.nn.api.OptimizationAlgorithm;
import org.deeplearning4j.nn.conf.CacheMode;
import org.deeplearning4j.nn.conf.ComputationGraphConfiguration;
import org.deeplearning4j.nn.conf.ConvolutionMode;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.WorkspaceMode;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.Cnn3DLossLayer;

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Exception in thread "main" org.deeplearning4j.exception.DL4JInvalidInputException: Got rank 4 array as input to Convolution3DLayer (layer name = conv1-1, layer index = 1) with shape [1, 1, 256, 256]. Expected rank 5 array with shape [minibatchSize, numChannels, inputHeight, inputWidth, inputDepth]. (layer name: conv1-1, layer index: 1, layer type: Convolution3DLayer)
	at org.deeplearning4j.nn.layers.convolution.Convolution3DLayer.preOutput(Convolution3DLayer.java:189)
	at org.deeplearning4j.nn.layers.convolution.ConvolutionLayer.activate(ConvolutionLayer.java:437)
	at org.deeplearning4j.nn.graph.vertex.impl.LayerVertex.doForward(LayerVertex.java:111)
	at org.deeplearning4j.nn.graph.ComputationGraph.ffToLayerActivationsInWS(ComputationGraph.java:2116)
	at org.deeplearning4j.nn.graph.ComputationGraph.computeGradientAndScore(ComputationGraph.java:1369)
	at org.deeplearning4j.nn.graph.ComputationGraph.computeGradientAndScore(ComputationGraph.java:1338)
	at org.deeplearning4j.optimize.solvers.BaseOptimizer.gradien

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public ComputationGraphConfiguration.GraphBuilder unetBuilder() {

        ComputationGraphConfiguration.GraphBuilder graph = new NeuralNetConfiguration.Builder().seed(seed)
        		   .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
                   .updater(updater)
                   .weightInit(weightInit)
                   .l2(5e-5)
                   .miniBatch(true)
                   .cacheMode(cacheMode)
                   .trainingWorkspaceMode(workspaceMode)