# Mismatched Shapes **URL:** \ **Category:** DL4J\ **Created:** [June 10, 2020, 9:24am UTC](https://community.konduit.ai/t/mismatched-shapes/597 "2020-06-10T09:24:02Z")\ **Posts on this page:** 3\ **Page:** 1
**Author:** ![maarten.schadd](https://avatars.discourse-cdn.com/v4/letter/m/aca169/32.png) [@maarten.schadd](https://community.konduit.ai/u/maarten.schadd)\ **Post date:** [June 10, 2020, 9:24am UTC](https://community.konduit.ai/t/mismatched-shapes/597/1 "2020-06-10T09:24:02Z")
Hello there. I am trying to create an autoencoder for a csv file with 27 float columns. I made my own datasetiterator, based on the AnomalyDataSet example project. The resulting output is a nice Dataset with 27 floats as columns, and a mask with all 1s. ```auto ===========INPUT=================== [[832.2833, 907.5333, 75.2635, ... 67.4916, 32.9680, 32.9513], [29.0333, 346.5667, 317.5359, ... 37.4298, 52.6219, 52.6225], [599.8500, 643.9667, 44.1232, ... 57.8035, 49.0082, 48.9850], ..., [1370.0500, 504.4167, 574.3795, ... 37.5940, 28.8473, 28.8397], [1293.5834, 295.0833, 441.5164, ... 39.3701, 17.5364, 17.5363], [1327.3000, 544.4000, 657.1083, ... 38.1679, 20.4905, 20.4902]] =================OUTPUT================== [[832.2833, 907.5333, 75.2635, ... 67.4916, 32.9680, 32.9513], [29.0333, 346.5667, 317.5359, ... 37.4298, 52.6219, 52.6225], [599.8500, 643.9667, 44.1232, ... 57.8035, 49.0082, 48.9850], ..., [1370.0500, 504.4167, 574.3795, ... 37.5940, 28.8473, 28.8397], [1293.5834, 295.0833, 441.5164, ... 39.3701, 17.5364, 17.5363], [1327.3000, 544.4000, 657.1083, ... 38.1679, 20.4905, 20.4902]] ===========INPUT MASK=================== [[1.0000, 1.0000, 1.0000, ... 1.0000, 1.0000, 1.0000], [1.0000, 1.0000, 1.0000, ... 1.0000, 1.0000, 1.0000], [1.0000, 1.0000, 1.0000, ... 1.0000, 1.0000, 1.0000], ..., [1.0000, 1.0000, 1.0000, ... 1.0000, 1.0000, 1.0000], [1.0000, 1.0000, 1.0000, ... 1.0000, 1.0000, 1.0000], [1.0000, 1.0000, 1.0000, ... 1.0000, 1.0000, 1.0000]] ===========OUTPUT MASK=================== [[1.0000, 1.0000, 1.0000, ... 1.0000, 1.0000, 1.0000], [1.0000, 1.0000, 1.0000, ... 1.0000, 1.0000, 1.0000], [1.0000, 1.0000, 1.0000, ... 1.0000, 1.0000, 1.0000], ..., [1.0000, 1.0000, 1.0000, ... 1.0000, 1.0000, 1.0000], [1.0000, 1.0000, 1.0000, ... 1.0000, 1.0000, 1.0000], [1.0000, 1.0000, 1.0000, ... 1.0000, 1.0000, 1.0000]] ``` The output is the same as the input, because of the autoencoder idea. Now I have made this network configuration: ```auto MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder() .seed(12345) .weightInit(WeightInit.XAVIER) .updater(new AdaGrad(0.05)) .activation(Activation.RELU) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .l2(0.0001) .list() .layer(0, new DenseLayer.Builder().nIn(27).nOut(50) .build()) .layer(1, new DenseLayer.Builder().nIn(50).nOut(10) .build()) .layer(2, new DenseLayer.Builder().nIn(10).nOut(50) .build()) .layer(3, new OutputLayer.Builder().nIn(50).nOut(27) .lossFunction(LossFunctions.LossFunction.MSE) .build()) .build(); MultiLayerNetwork net = new MultiLayerNetwork(conf); net.init(); ``` I am not sure all the hyperparameters, but that is a worry for when the code starts running properly. Now when the fitting starts, I get a very deep exception: ```auto Exception in thread "main" java.lang.IllegalStateException: Mismatched shapes (shape = [64, 50], column vector shape =[64, 27]) at org.nd4j.linalg.api.ndarray.BaseNDArray.doColumnWise(BaseNDArray.java:2368) at org.nd4j.linalg.api.ndarray.BaseNDArray.muliColumnVector(BaseNDArray.java:2788) at org.deeplearning4j.nn.layers.AbstractLayer.applyMask(AbstractLayer.java:252) at org.deeplearning4j.nn.layers.BaseLayer.preOutputWithPreNorm(BaseLayer.java:331) at org.deeplearning4j.nn.layers.BaseLayer.preOutput(BaseLayer.java:291) at org.deeplearning4j.nn.layers.BaseLayer.activate(BaseLayer.java:339) at org.deeplearning4j.nn.layers.AbstractLayer.activate(AbstractLayer.java:258) at org.deeplearning4j.nn.multilayer.MultiLayerNetwork.ffToLayerActivationsInWs(MultiLayerNetwork.java:1134) at org.deeplearning4j.nn.multilayer.MultiLayerNetwork.computeGradientAndScore(MultiLayerNetwork.java:2746) at org.deeplearning4j.nn.multilayer.MultiLayerNetwork.computeGradientAndScore(MultiLayerNetwork.java:2704) at org.deeplearning4j.optimize.solvers.BaseOptimizer.gradientAndScore(BaseOptimizer.java:170) at org.deeplearning4j.optimize.solvers.StochasticGradientDescent.optimize(StochasticGradientDescent.java:63) at org.deeplearning4j.optimize.Solver.optimize(Solver.java:52) at org.deeplearning4j.nn.multilayer.MultiLayerNetwork.fitHelper(MultiLayerNetwork.java:1715) at org.deeplearning4j.nn.multilayer.MultiLayerNetwork.fit(MultiLayerNetwork.java:1636) at org.deeplearning4j.nn.multilayer.MultiLayerNetwork.fit(MultiLayerNetwork.java:1623) at CAMSTAMUnsupervised.main(CAMSTAMUnsupervised.java:91) ``` The 27 are my columns, so that makes sense. The 50 correspond to the the first layer of the network. Specifically this line of code: .layer(0, new DenseLayer.Builder().nIn(inputNum).nOut(50). I am not sure what the 64 is, maybe a batch size. So my question is what I have misconfigured that this exception is thrown? I have tried simply to make the first layer have 27 nodes, but that generates the message: Mismatched shapes (shape = [64, 27], column vector shape =[64, 27]), which is even more confusing. This is probably a beginner question, so thanks for your help. Maarten ---
**Author:** ![treo](https://yyz1.discourse-cdn.com/flex035/user_avatar/community.konduit.ai/treo/32/47_2.png) [@treo](https://community.konduit.ai/u/treo)\ **Post date:** [June 10, 2020, 9:33am UTC](https://community.konduit.ai/t/mismatched-shapes/597/2 "2020-06-10T09:33:19Z")
Mask is applied per example, not per input. As you aren’t actually masking anything out, you might want to not set any masks at all. ---
**Author:** ![maarten.schadd](https://avatars.discourse-cdn.com/v4/letter/m/aca169/32.png) [@maarten.schadd](https://community.konduit.ai/u/maarten.schadd)\ **Post date:** [June 10, 2020, 9:50am UTC](https://community.konduit.ai/t/mismatched-shapes/597/3 "2020-06-10T09:50:49Z")
Removing the masks indeed fixed the problem. Thanks!