model null?

model.java

model = {MultiLayerNetwork@6104} 
 defaultConfiguration = {NeuralNetConfiguration@6111} "NeuralNetConfiguration(layer=DenseLayer(super=FeedForwardLayer(super=Layer(layerName=layer0, activationFn=tanh, weightInit=XAVIER, biasInit=0.0, dist=null, learningRate=0.1, biasLearningRate=0.1, learningRateSchedule=null, momentum=0.9, momentumSchedule={}, l1=0.0, l2=0.0, l1Bias=0.0, l2Bias=0.0, dropOut=0.0, updater=NESTEROVS, rho=NaN, epsilon=NaN, rmsDecay=NaN, adamMeanDecay=NaN, adamVarDecay=NaN, gradientNormalization=None, gradientNormalizationThreshold=1.0), nIn=4, nOut=3)), leakyreluAlpha=0.0, miniBatch=true, numIterations=1, maxNumLineSearchIterations=5, seed=1489796271540, optimizationAlgo=STOCHASTIC_GRADIENT_DESCENT, variables=[0_W, 0_b, 1_W, 1_b], stepFunction=null, useRegularization=false, useDropConnect=false, minimize=true, learningRateByParam={}, l1ByParam={}, l2ByParam={}, learningRatePolicy=None, lrPolicyDecayRate=NaN, lrPolicySteps=NaN, lrPolicyPower=NaN, pretrain=false, iterationCount=0)"
 epsilon = null
 flattenedGradients = null
 flattenedParams = {NDArray@6112} "[0.65, -0.64, 0.11, 0.58, -0.09, 0.89, 0.17, 0.69, 0.30, 0.09, 0.15, 0.27, 0.00, 0.00, 0.00, -0.48, -1.51, 0.01, 0.82, -0.60, -0.35, 0.53, -0.32, -0.18, 0.00, 0.00, 0.00]"
 gradient = null
 initCalled = true
 initDone = false
 input = null
 labels = null
 layerIndex = 0
 layerMap = {LinkedHashMap@6113}  size = 2
 layerWiseConfigurations = {MultiLayerConfiguration@6114} "{\n  "backprop" : true,\n  "backpropType" : "Standard",\n  "confs" : [ {\n    "iterationCount" : 0,\n    "l1ByParam" : {\n      "b" : 0.0,\n      "W" : 0.0\n    },\n    "l2ByParam" : {\n      "b" : 0.0,\n      "W" : 0.0\n    },\n    "layer" : {\n      "dense" : {\n        "activationFn" : {\n          "TanH" : { }\n        },\n        "adamMeanDecay" : "NaN",\n        "adamVarDecay" : "NaN",\n        "biasInit" : 0.0,\n        "biasLearningRate" : 0.1,\n        "dist" : null,\n        "dropOut" : 0.0,\n        "epsilon" : "NaN",\n        "gradientNormalization" : "None",\n        "gradientNormalizationThreshold" : 1.0,\n        "l1" : 0.0,\n        "l1Bias" : 0.0,\n        "l2" : 0.0,\n        "l2Bias" : 0.0,\n        "layerName" : "layer0",\n        "learningRate" : 0.1,\n        "learningRateSchedule" : null,\n        "momentum" : 0.9,\n        "momentumSchedule" : { },\n        "nin" : 4,\n        "nout" : 3,\n        "rho" : "NaN",\n        "rmsDecay" : "NaN",\n        "updater" : "NESTEROVS",\n        "weightInit" : "XAV"
 layers = {Layer[2]@6115} 
 listeners = {ArrayList@6117}  size = 0
 mask = null
 score = 0.0
 solver = null
 trainingListeners = {ArrayList@6118}  size = 0
 shadow$_klass_ = {Class@4623} "class org.deeplearning4j.nn.multilayer.MultiLayerNetwork"
 shadow$_monitor_ = -2073807350