# Simple sequential model example **URL:** \ **Category:** Uncategorized\ **Created:** [April 7, 2023, 10:32am UTC](https://community.konduit.ai/t/simple-sequential-model-example/2453 "2023-04-07T10:32:24Z")\ **Posts on this page:** 7\ **Page:** 1
**Author:** ![hodophile](https://yyz1.discourse-cdn.com/flex035/user_avatar/community.konduit.ai/hodophile/32/1071_2.png) [@hodophile](https://community.konduit.ai/u/hodophile)\ **Post date:** [April 7, 2023, 10:32am UTC](https://community.konduit.ai/t/simple-sequential-model-example/2453/1 "2023-04-07T10:32:24Z")
public class SimpleSequentialMlpImport { ``` public static String dataLocalPath; public static void main(String[] args) throws Exception { String SIMPLE_MLP = new ClassPathResource("simple_mlp.h5").getFile().getPath(); System.out.println(SIMPLE_MLP); MultiLayerNetwork model = KerasModelImport.importKerasSequentialModelAndWeights(SIMPLE_MLP, false); INDArray input = Nd4j.create(DataType.FLOAT, 256, 100); INDArray output = model.output(input); model.fit(input, output); } ``` } Trying to load a tf.keras sequential model using dl4j throws the error below.?! Exception in thread “main” org.deeplearning4j.exception.DL4JException: Cannot calculate gradient and score with respect to labels: final layer is not an IOutputLayer. Final layer class: class org.deeplearning4j.nn.layers.feedforward.dense.DenseLayer. To calculate gradients and fit a network using backpropagation, the final layer must be an output layer ---
**Author:** ![agibsonccc](https://yyz1.discourse-cdn.com/flex035/user_avatar/community.konduit.ai/agibsonccc/32/697_2.png) [@agibsonccc](https://community.konduit.ai/u/agibsonccc)\ **Post date:** [April 7, 2023, 10:45am UTC](https://community.konduit.ai/t/simple-sequential-model-example/2453/2 "2023-04-07T10:45:54Z")
@hodophile you seem to have an invalid keras model. Could you post your keras code? I almost guarantee you forgot to specify a loss function or something. You are ending on a dense layer. ---
**Author:** ![hodophile](https://yyz1.discourse-cdn.com/flex035/user_avatar/community.konduit.ai/hodophile/32/1071_2.png) [@hodophile](https://community.konduit.ai/u/hodophile)\ **Post date:** [April 7, 2023, 10:47am UTC](https://community.konduit.ai/t/simple-sequential-model-example/2453/3 "2023-04-07T10:47:41Z")
from keras.models import Sequential from keras.layers import Dense import tensorflow as tf model = Sequential() model.add(Dense(units=64, activation=‘relu’, input\_dim=100)) model.add(Dense(units=10, activation=‘softmax’)) model.compile(loss=‘categorical\_crossentropy’,optimizer=tf.keras.optimizers.experimental.SGD(), metrics=tf.keras.metrics.Accuracy()) model.save(‘simple\_mlp.h5’) ---
**Author:** ![agibsonccc](https://yyz1.discourse-cdn.com/flex035/user_avatar/community.konduit.ai/agibsonccc/32/697_2.png) [@agibsonccc](https://community.konduit.ai/u/agibsonccc)\ **Post date:** [April 7, 2023, 10:56am UTC](https://community.konduit.ai/t/simple-sequential-model-example/2453/4 "2023-04-07T10:56:58Z")
@hodophile what version of dl4j and keras are you using? Just to double check. ---
**Author:** ![hodophile](https://yyz1.discourse-cdn.com/flex035/user_avatar/community.konduit.ai/hodophile/32/1071_2.png) [@hodophile](https://community.konduit.ai/u/hodophile)\ **Post date:** [April 7, 2023, 11:03am UTC](https://community.konduit.ai/t/simple-sequential-model-example/2453/5 "2023-04-07T11:03:54Z")
keras : 2.12.0 dl4j : 1.0.0-M2.1 ---
**Author:** ![agibsonccc](https://yyz1.discourse-cdn.com/flex035/user_avatar/community.konduit.ai/agibsonccc/32/697_2.png) [@agibsonccc](https://community.konduit.ai/u/agibsonccc)\ **Post date:** [April 7, 2023, 11:04am UTC](https://community.konduit.ai/t/simple-sequential-model-example/2453/6 "2023-04-07T11:04:25Z")
> [@hodophile](#): > > ```auto > String SIMPLE_MLP = new ClassPathResource("simple_mlp.h5").getFile().getPath(); > > System.out.println(SIMPLE_MLP); > MultiLayerNetwork model = KerasModelImport.importKerasSequentialModelAndWeights(SIMPLE_MLP, false); > > INDArray input = Nd4j.create(DataType.FLOAT, 256, 100); > INDArray output = model.output(input); > > model.fit(input, output); > > ``` @hodophile do this: ```auto package org.example; import org.deeplearning4j.nn.conf.layers.OutputLayer; import org.deeplearning4j.nn.modelimport.keras.KerasModelImport; import org.deeplearning4j.nn.multilayer.MultiLayerNetwork; import org.deeplearning4j.nn.transferlearning.FineTuneConfiguration; import org.deeplearning4j.nn.transferlearning.TransferLearning; import org.nd4j.linalg.activations.impl.ActivationSoftmax; import org.nd4j.linalg.api.buffer.DataType; import org.nd4j.linalg.api.ndarray.INDArray; import org.nd4j.linalg.factory.Nd4j; import org.nd4j.linalg.learning.config.Adam; public class TestForumMlp { public static void main(String...args) throws Exception { String SIMPLE_MLP = "simple_mlp.h5"; System.out.println(SIMPLE_MLP); MultiLayerNetwork model = KerasModelImport.importKerasSequentialModelAndWeights(SIMPLE_MLP, false); MultiLayerNetwork transferLearning = new TransferLearning.Builder(model) .fineTuneConfiguration(new FineTuneConfiguration.Builder() .updater(new Adam()).build()) .addLayer(new OutputLayer.Builder() .nIn(10) .nOut(10) .activation(new ActivationSoftmax()) .build()) .build(); INDArray input = Nd4j.create(DataType.FLOAT, 256, 100); INDArray output = model.output(input); transferLearning.fit(input, output); } } ``` Remove the extra output layer and you should be good to go. 99% of the time model import is used for inference then you add your updaters and other training information on top of that. ---
**Author:** ![agibsonccc](https://yyz1.discourse-cdn.com/flex035/user_avatar/community.konduit.ai/agibsonccc/32/697_2.png) [@agibsonccc](https://community.konduit.ai/u/agibsonccc)\ **Post date:** [April 7, 2023, 11:15am UTC](https://community.konduit.ai/t/simple-sequential-model-example/2453/7 "2023-04-07T11:15:32Z")
@hodophile sorry this is not very intuitive I’ll work on fixing that. Thanks for the test case.