# Cannot infer input type for reshape array

**URL:** <https://community.konduit.ai/t/cannot-infer-input-type-for-reshape-array/1358>\
**Category:** DL4J\
**Created:** [April 30, 2021, 3:24pm UTC](https://community.konduit.ai/t/cannot-infer-input-type-for-reshape-array/1358 "2021-04-30T15:24:12Z")\
**Posts on this page:** 18\
**Page:** 1

<div class="post-metadata">

**Author:** ![Acander](https://yyz1.discourse-cdn.com/flex035/user_avatar/community.konduit.ai/acander/32/520_2.png) [@Acander](https://community.konduit.ai/u/Acander)\
**Post date:** [April 30, 2021, 3:24pm UTC](https://community.konduit.ai/t/cannot-infer-input-type-for-reshape-array/1358/1 "2021-04-30T15:24:12Z")

</div>

Hi!

I am trying to import a keras model created in Python. I am working with convolutional 3D layers which requires reshaping dense layer input into 5 dimensional shapes, however it does not seem that this is allowed by the reshape processor class?

The exception is:

Exception in thread “main” java.lang.UnsupportedOperationException: Cannot infer input type for reshape array [0, 60, 1, 3, 4]

The error is thrown in the following method in the ReshapePreprocessor class:

@Override  
public InputType getOutputType(InputType inputType) throws InvalidInputTypeException {  
long shape = getShape(this.targetShape, 0);  
InputType ret;  
switch (shape.length) {  
case 2:  
ret = InputType.feedForward(shape[1]);  
break;  
case 3:  
RNNFormat format = RNNFormat.NCW;  
if(this.format != null && this.format instanceof RNNFormat)  
format = (RNNFormat)this.format;

```
            ret = InputType.recurrent(shape[2], shape[1], format);
            break;
        case 4:
            if (inputShape.length == 1 || inputType.getType() == InputType.Type.RNN) {
                ret = InputType.convolutional(shape[1], shape[2], shape[3]);
            } else {

                CNN2DFormat cnnFormat = CNN2DFormat.NCHW;
                if (this.format != null && this.format instanceof CNN2DFormat)
                    cnnFormat = (CNN2DFormat) this.format;

                if (cnnFormat == CNN2DFormat.NCHW) {
                    ret = InputType.convolutional(shape[2], shape[3], shape[1], cnnFormat);
                } else {
                    ret = InputType.convolutional(shape[1], shape[2], shape[3], cnnFormat);
                }
            }
            break;
        default:
            throw new UnsupportedOperationException(
                    "Cannot infer input type for reshape array " + Arrays.toString(shape));
    }
    return ret;
}

```

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<div class="post-metadata">

**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:** [May 3, 2021, 7:39am UTC](https://community.konduit.ai/t/cannot-infer-input-type-for-reshape-array/1358/2 "2021-05-03T07:39:52Z")

</div>

@Acander have you tried with snapshots? I believe this has been fixed. See: [https://deeplearning4j.konduit.ai/config/config-snapshots](https://deeplearning4j.konduit.ai/config/config-snapshots)

---

<div class="post-metadata">

**Author:** ![Acander](https://yyz1.discourse-cdn.com/flex035/user_avatar/community.konduit.ai/acander/32/520_2.png) [@Acander](https://community.konduit.ai/u/Acander)\
**Post date:** [May 3, 2021, 9:25am UTC](https://community.konduit.ai/t/cannot-infer-input-type-for-reshape-array/1358/3 "2021-05-03T09:25:15Z")

</div>

Hi! I tried with snapshots, but the problem does not seem to have been fixed 😕 Please advice.

---

<div class="post-metadata">

**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:** [May 3, 2021, 9:35am UTC](https://community.konduit.ai/t/cannot-infer-input-type-for-reshape-array/1358/4 "2021-05-03T09:35:04Z")

</div>

Can you provide us with a self-contained reproduction project?

If we don’t have to spend a lot of time to reproduce the exact problem you are running into, we can spend more time on fixing the bug 🙂

---

<div class="post-metadata">

**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:** [May 3, 2021, 9:45am UTC](https://community.konduit.ai/t/cannot-infer-input-type-for-reshape-array/1358/5 "2021-05-03T09:45:44Z")

</div>

Actually…yeah we don’t support rank 5 in the execution, but we do in the import.  
[https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/preprocessors/ReshapePreprocessor.java](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/preprocessors/ReshapePreprocessor.java)

@Acander I added an issue: [Support CNN3D in ReshapePreprocessor · Issue #9288 · eclipse/deeplearning4j · GitHub](https://github.com/eclipse/deeplearning4j/issues/9288)  
I won’t be able to get to this till after golden week in japan is over. (Thursday) if you want to try to implement the necessary change yourself, most of the machinery should already be there.  
Anything more than me approving a pull request or clicking “build” with some configuration I won’t be doing till after the holiday though.

---

<div class="post-metadata">

**Author:** ![Acander](https://yyz1.discourse-cdn.com/flex035/user_avatar/community.konduit.ai/acander/32/520_2.png) [@Acander](https://community.konduit.ai/u/Acander)\
**Post date:** [May 3, 2021, 10:04am UTC](https://community.konduit.ai/t/cannot-infer-input-type-for-reshape-array/1358/6 "2021-05-03T10:04:14Z")

</div>

Okay, I understand. Thank you for your answer! I don’t dare implementing the change myself, but I think I can wait until you get around to it.

Again, thanks for everything!

---

<div class="post-metadata">

**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:** [May 6, 2021, 4:05am UTC](https://community.konduit.ai/t/cannot-infer-input-type-for-reshape-array/1358/7 "2021-05-06T04:05:09Z")

</div>

@Acander[Adds cnn 3d to input reshape preprocessor by agibsonccc · Pull Request #9291 · eclipse/deeplearning4j · GitHub](https://github.com/eclipse/deeplearning4j/pull/9291)

---

<div class="post-metadata">

**Author:** ![Acander](https://yyz1.discourse-cdn.com/flex035/user_avatar/community.konduit.ai/acander/32/520_2.png) [@Acander](https://community.konduit.ai/u/Acander)\
**Post date:** [May 8, 2021, 12:40pm UTC](https://community.konduit.ai/t/cannot-infer-input-type-for-reshape-array/1358/8 "2021-05-08T12:40:42Z")

</div>

Hi @agibsonccc! Thanks for updating the code. I do however get another error now (only including part of the error stack involving d3j classes):

Exception in thread “main” java.lang.IllegalArgumentException: Illegal format found null  
at org.deeplearning4j.nn.modelimport.keras.preprocessors.ReshapePreprocessor.getOutputType(ReshapePreprocessor.java:179)  
at org.deeplearning4j.nn.modelimport.keras.layers.core.KerasReshape.getOutputType(KerasReshape.java:183)  
at org.deeplearning4j.nn.modelimport.keras.KerasModel.inferOutputTypes(KerasModel.java:473)  
at org.deeplearning4j.nn.modelimport.keras.KerasSequentialModel.(KerasSequentialModel.java:148)  
at org.deeplearning4j.nn.modelimport.keras.KerasSequentialModel.(KerasSequentialModel.java:57)  
at org.deeplearning4j.nn.modelimport.keras.utils.KerasModelBuilder.buildSequential(KerasModelBuilder.java:326)  
at org.deeplearning4j.nn.modelimport.keras.KerasModelImport.importKerasSequentialModelAndWeights(KerasModelImport.java:296)

It seems to do with the reshape processor class, specifically the Dataformat format variable that is set to seems to be set to null via the constructor in the KerasReshape class (line 182). Have you not accommodated for the convolution3D dataformat in the kerasReshape class? Or have I done something wrong?

---

<div class="post-metadata">

**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:** [May 9, 2021, 8:47am UTC](https://community.konduit.ai/t/cannot-infer-input-type-for-reshape-array/1358/9 "2021-05-09T08:47:15Z")

</div>

@Acander actually we do here: [deeplearning4j/KerasReshape.java at c715aea405980eb043420af86c671032bbb78ec6 · eclipse/deeplearning4j · GitHub](https://github.com/eclipse/deeplearning4j/blob/c715aea405980eb043420af86c671032bbb78ec6/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/core/KerasReshape.java#L124)

Could you DM me your model just to be sure?

---

<div class="post-metadata">

**Author:** ![Acander](https://yyz1.discourse-cdn.com/flex035/user_avatar/community.konduit.ai/acander/32/520_2.png) [@Acander](https://community.konduit.ai/u/Acander)\
**Post date:** [May 9, 2021, 3:24pm UTC](https://community.konduit.ai/t/cannot-infer-input-type-for-reshape-array/1358/10 "2021-05-09T15:24:55Z")

</div>

```json
{"class_name": "Sequential", "config": {"name": "sequential_1", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": [null, 32], "dtype": "float32", "sparse": false, "ragged": false, "name": "input_2"}}, {"class_name": "Dense", "config": {"name": "dense_4", "trainable": true, "dtype": "float32", "units": 720, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "LeakyReLU", "config": {"name": "leaky_re_lu", "trainable": true, "dtype": "float32", "alpha": 0.10000000149011612}}, {"class_name": "BatchNormalization", "config": {"name": "batch_normalization_3", "trainable": true, "dtype": "float32", "axis": [1], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}}, "gamma_initializer": {"class_name": "Ones", "config": {}}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}}, "moving_variance_initializer": {"class_name": "Ones", "config": {}}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}}, {"class_name": "Dropout", "config": {"name": "dropout_1", "trainable": true, "dtype": "float32", "rate": 0.2, "noise_shape": null, "seed": null}}, {"class_name": "Reshape", "config": {"name": "reshape", "trainable": true, "dtype": "float32", "target_shape": [60, 1, 3, 4]}}, {"class_name": "Conv3D", "config": {"name": "conv3d_4", "trainable": true, "dtype": "float32", "filters": 256, "kernel_size": [3, 3, 3], "strides": [1, 1, 1], "padding": "same", "data_format": "channels_last", "dilation_rate": [1, 1, 1], "groups": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "LeakyReLU", "config": {"name": "leaky_re_lu_1", "trainable": true, "dtype": "float32", "alpha": 0.10000000149011612}}, {"class_name": "UpSampling3D", "config": {"name": "up_sampling3d", "trainable": true, "dtype": "float32", "size": [2, 2, 2], "data_format": "channels_last"}}, {"class_name": "Conv3D", "config": {"name": "conv3d_5", "trainable": true, "dtype": "float32", "filters": 128, "kernel_size": [3, 3, 3], "strides": [1, 1, 1], "padding": "same", "data_format": "channels_last", "dilation_rate": [1, 1, 1], "groups": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "LeakyReLU", "config": {"name": "leaky_re_lu_2", "trainable": true, "dtype": "float32", "alpha": 0.10000000149011612}}, {"class_name": "UpSampling3D", "config": {"name": "up_sampling3d_1", "trainable": true, "dtype": "float32", "size": [2, 2, 2], "data_format": "channels_last"}}, {"class_name": "Conv3D", "config": {"name": "conv3d_6", "trainable": true, "dtype": "float32", "filters": 16, "kernel_size": [3, 3, 3], "strides": [1, 1, 1], "padding": "same", "data_format": "channels_last", "dilation_rate": [1, 1, 1], "groups": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "LeakyReLU", "config": {"name": "leaky_re_lu_3", "trainable": true, "dtype": "float32", "alpha": 0.10000000149011612}}, {"class_name": "UpSampling3D", "config": {"name": "up_sampling3d_2", "trainable": true, "dtype": "float32", "size": [2, 2, 2], "data_format": "channels_last"}}, {"class_name": "Conv3D", "config": {"name": "conv3d_7", "trainable": true, "dtype": "float32", "filters": 8, "kernel_size": [3, 3, 3], "strides": [1, 1, 1], "padding": "same", "data_format": "channels_last", "dilation_rate": [1, 1, 1], "groups": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "LeakyReLU", "config": {"name": "leaky_re_lu_4", "trainable": true, "dtype": "float32", "alpha": 0.10000000149011612}}, {"class_name": "Conv3D", "config": {"name": "conv3d_8", "trainable": true, "dtype": "float32", "filters": 1, "kernel_size": [3, 3, 3], "strides": [1, 1, 1], "padding": "same", "data_format": "channels_last", "dilation_rate": [1, 1, 1], "groups": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}]}, "keras_version": "2.4.0", "backend": "tensorflow"}

```

Could you put the content above in a JSON file and open it in a web browser? I think that representation is the clearest. I cannot send you the original JSON file since the DM does not allow it. I trying to implement the model described in this paper: [https://arxiv.org/pdf/1903.04144.pdf](https://arxiv.org/pdf/1903.04144.pdf)

---

<div class="post-metadata">

**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:** [May 10, 2021, 12:33am UTC](https://community.konduit.ai/t/cannot-infer-input-type-for-reshape-array/1358/11 "2021-05-10T00:33:00Z")

</div>

> [@Acander](#):
>
> rt.keras.preprocessors.ReshapePreprocessor.getOutputType(ReshapePreprocessor.java:179)  
> at

@Acander fixed here: [Add default value for cnn3d keras import channel layout, fix mkldnn class missing interfaces by agibsonccc · Pull Request #9305 · deeplearning4j/deeplearning4j · GitHub](https://github.com/eclipse/deeplearning4j/pull/9305)  
Do you mind if I add the model to our test cases?

---

<div class="post-metadata">

**Author:** ![Acander](https://yyz1.discourse-cdn.com/flex035/user_avatar/community.konduit.ai/acander/32/520_2.png) [@Acander](https://community.konduit.ai/u/Acander)\
**Post date:** [May 10, 2021, 6:38am UTC](https://community.konduit.ai/t/cannot-infer-input-type-for-reshape-array/1358/12 "2021-05-10T06:38:53Z")

</div>

No, I don’t mind. You can do that!

---

<div class="post-metadata">

**Author:** ![Acander](https://yyz1.discourse-cdn.com/flex035/user_avatar/community.konduit.ai/acander/32/520_2.png) [@Acander](https://community.konduit.ai/u/Acander)\
**Post date:** [May 11, 2021, 8:28am UTC](https://community.konduit.ai/t/cannot-infer-input-type-for-reshape-array/1358/13 "2021-05-11T08:28:18Z")

</div>

I might be going of topic now, but I am currently trying to import the Keras model with KerasModelImport.importKerasSequentialModelAndWeights(model\_config, model\_weights), but I get the below error:

java.lang.NoSuchMethodException: org.deeplearning4j.nn.layers.mkldnn.MKLDNNBatchNormHelper.(java.lang.Class, org.nd4j.linalg.api.buffer.DataType)  
at java.base/java.lang.Class.getConstructor0(Class.java:3349)  
at java.base/java.lang.Class.getDeclaredConstructor(Class.java:2553)  
at org.deeplearning4j.common.config.DL4JClassLoading.createNewInstance(DL4JClassLoading.java:103)  
at org.deeplearning4j.common.config.DL4JClassLoading.createNewInstance(DL4JClassLoading.java:89)  
at org.deeplearning4j.common.config.DL4JClassLoading.createNewInstance(DL4JClassLoading.java:74)  
at org.deeplearning4j.nn.layers.HelperUtils.createHelper(HelperUtils.java:93)  
at org.deeplearning4j.nn.layers.normalization.BatchNormalization.initializeHelper(BatchNormalization.java:74)  
at org.deeplearning4j.nn.layers.normalization.BatchNormalization.(BatchNormalization.java:70)  
at org.deeplearning4j.nn.conf.layers.BatchNormalization.instantiate(BatchNormalization.java:94)  
at org.deeplearning4j.nn.multilayer.MultiLayerNetwork.init(MultiLayerNetwork.java:714)  
at org.deeplearning4j.nn.multilayer.MultiLayerNetwork.init(MultiLayerNetwork.java:604)  
at org.deeplearning4j.nn.modelimport.keras.KerasSequentialModel.getMultiLayerNetwork(KerasSequentialModel.java:260)  
at org.deeplearning4j.nn.modelimport.keras.KerasSequentialModel.getMultiLayerNetwork(KerasSequentialModel.java:249)  
at org.deeplearning4j.nn.modelimport.keras.KerasModelImport.importKerasSequentialModelAndWeights(KerasModelImport.java:297)  
at MLSCM.d4jMavenTest.SCMGenerator.(SCMGenerator.java:55)  
at MLSCM.d4jMavenTest.SCMGenerator.main(SCMGenerator.java:89)  
Exception in thread “main” java.lang.RuntimeException: java.lang.NoSuchMethodException: org.deeplearning4j.nn.layers.mkldnn.MKLDNNBatchNormHelper.(java.lang.Class, org.nd4j.linalg.api.buffer.DataType)  
at org.deeplearning4j.common.config.DL4JClassLoading.createNewInstance(DL4JClassLoading.java:108)  
at org.deeplearning4j.common.config.DL4JClassLoading.createNewInstance(DL4JClassLoading.java:89)  
at org.deeplearning4j.common.config.DL4JClassLoading.createNewInstance(DL4JClassLoading.java:74)  
at org.deeplearning4j.nn.layers.HelperUtils.createHelper(HelperUtils.java:93)  
at org.deeplearning4j.nn.layers.normalization.BatchNormalization.initializeHelper(BatchNormalization.java:74)  
at org.deeplearning4j.nn.layers.normalization.BatchNormalization.(BatchNormalization.java:70)  
at org.deeplearning4j.nn.conf.layers.BatchNormalization.instantiate(BatchNormalization.java:94)  
at org.deeplearning4j.nn.multilayer.MultiLayerNetwork.init(MultiLayerNetwork.java:714)  
at org.deeplearning4j.nn.multilayer.MultiLayerNetwork.init(MultiLayerNetwork.java:604)  
at org.deeplearning4j.nn.modelimport.keras.KerasSequentialModel.getMultiLayerNetwork(KerasSequentialModel.java:260)  
at org.deeplearning4j.nn.modelimport.keras.KerasSequentialModel.getMultiLayerNetwork(KerasSequentialModel.java:249)  
at org.deeplearning4j.nn.modelimport.keras.KerasModelImport.importKerasSequentialModelAndWeights(KerasModelImport.java:297)  
at MLSCM.d4jMavenTest.SCMGenerator.(SCMGenerator.java:55)  
at MLSCM.d4jMavenTest.SCMGenerator.main(SCMGenerator.java:89)  
Caused by: java.lang.NoSuchMethodException: org.deeplearning4j.nn.layers.mkldnn.MKLDNNBatchNormHelper.(java.lang.Class, org.nd4j.linalg.api.buffer.DataType)  
at java.base/java.lang.Class.getConstructor0(Class.java:3349)  
at java.base/java.lang.Class.getDeclaredConstructor(Class.java:2553)  
at org.deeplearning4j.common.config.DL4JClassLoading.createNewInstance(DL4JClassLoading.java:103)  
… 13 more

Has it to do with with the following?

10:09:51.157 [main] WARN org.deeplearning4j.nn.modelimport.keras.layers.normalization.KerasBatchNormalization - Warning: batch normalization axis 1  
DL4J currently picks batch norm dimensions for you, according to industrystandard conventions. If your results do not match, please file an issue.

Could you import the model?

---

<div class="post-metadata">

**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:** [May 11, 2021, 1:28pm UTC](https://community.konduit.ai/t/cannot-infer-input-type-for-reshape-array/1358/14 "2021-05-11T13:28:35Z")

</div>

@Acander model imported fine. It’s something related to using onednn, I’ll have to investigate. Should be an easy fix though.  
It’s related to this:

> <https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/normalization/BatchNormalization.java#L74>

If you want as a workaround, go ahead and just mvn clean install -DskipTests on deeplearning4j-nn and comment that out.  
Just make sure the helper stays null. It just attempts to use onednn/mkldnn. What it should do is a fall back if it fails to load. That specific error you’re seeing is a reflection error though. I’ll audit all the helpers to make sure they have the normal methods setup.

I’ll also add a JVM argument that allows all helpers to be disabled via a system property.

I’ll implement a real fix tomorrow.

---

<div class="post-metadata">

**Author:** ![Acander](https://yyz1.discourse-cdn.com/flex035/user_avatar/community.konduit.ai/acander/32/520_2.png) [@Acander](https://community.konduit.ai/u/Acander)\
**Post date:** [May 16, 2021, 7:45pm UTC](https://community.konduit.ai/t/cannot-infer-input-type-for-reshape-array/1358/15 "2021-05-16T19:45:55Z")

</div>

Hi, again!  
I see the code imported through maven is slightly different from what you displayed above. Have you implemented your fix? I am unable to make any changes to the code and I still get the same error. Am I importing the model incorrectly? Could you show me your code for that?

```
    if ("CUDA".equalsIgnoreCase(backend)) {
        helper = DL4JClassLoading.createNewInstance(
                "org.deeplearning4j.cuda.normalization.CudnnBatchNormalizationHelper",
                BatchNormalizationHelper.class,
                dataType);
        log.debug("CudnnBatchNormalizationHelper successfully initialized");
    } else if ("CPU".equalsIgnoreCase(backend)){
        helper = new MKLDNNBatchNormHelper(dataType);
        log.trace("Created MKLDNNBatchNormHelper, layer {}", layerConf().getLayerName());
    }

    if (helper != null && !helper.checkSupported(layerConf().getEps(), layerConf().isLockGammaBeta())) {
        log.debug("Removed helper {} as not supported with epsilon {}, lockGammaBeta={}", helper.getClass(), layerConf().getEps(), layerConf().isLockGammaBeta());
        helper = null;
    }

```

The full output stack (I don’t think I added it above):

21:24:06.237 [main] WARN org.deeplearning4j.nn.modelimport.keras.layers.normalization.KerasBatchNormalization - Warning: batch normalization axis 1  
DL4J currently picks batch norm dimensions for you, according to industrystandard conventions. If your results do not match, please file an issue.  
21:24:06.502 [main] INFO org.nd4j.linalg.factory.Nd4jBackend - Loaded [CpuBackend] backend  
21:24:06.511 [main] ERROR org.nd4j.common.config.ND4JClassLoading - Cannot find class [org.nd4j.linalg.jblas.JblasBackend] of provided class-loader.  
21:24:06.512 [main] ERROR org.nd4j.common.config.ND4JClassLoading - Cannot find class [org.canova.api.io.data.DoubleWritable] of provided class-loader.  
21:24:06.514 [main] ERROR org.nd4j.common.config.ND4JClassLoading - Cannot find class [org.nd4j.linalg.jblas.JblasBackend] of provided class-loader.  
21:24:06.515 [main] ERROR org.nd4j.common.config.ND4JClassLoading - Cannot find class [org.canova.api.io.data.DoubleWritable] of provided class-loader.  
21:24:08.092 [main] INFO org.nd4j.nativeblas.NativeOpsHolder - Number of threads used for linear algebra: 4  
21:24:08.095 [main] INFO org.nd4j.linalg.cpu.nativecpu.CpuNDArrayFactory - Binary level Generic x86 optimization level AVX/AVX2  
21:24:08.131 [main] INFO org.nd4j.nativeblas.Nd4jBlas - Number of threads used for OpenMP BLAS: 4  
21:24:08.155 [main] INFO org.nd4j.linalg.api.ops.executioner.DefaultOpExecutioner - Backend used: [CPU]; OS: [Windows 10]  
21:24:08.155 [main] INFO org.nd4j.linalg.api.ops.executioner.DefaultOpExecutioner - Cores: [8]; Memory: [4,0GB];  
21:24:08.155 [main] INFO org.nd4j.linalg.api.ops.executioner.DefaultOpExecutioner - Blas vendor: [OPENBLAS]  
21:24:08.162 [main] INFO org.nd4j.linalg.cpu.nativecpu.CpuBackend - Backend build information:  
GCC: “10.2.0”  
STD version: 201103L  
DEFAULT\_ENGINE: samediff::ENGINE\_CPU  
HAVE\_FLATBUFFERS  
HAVE\_OPENBLAS  
21:24:08.417 [main] INFO org.deeplearning4j.nn.multilayer.MultiLayerNetwork - Starting MultiLayerNetwork with WorkspaceModes set to [training: ENABLED; inference: ENABLED], cacheMode set to [NONE]  
21:24:08.461 [main] ERROR org.deeplearning4j.common.config.DL4JClassLoading - Cannot create instance of class ‘org.deeplearning4j.nn.layers.mkldnn.MKLDNNBatchNormHelper’.  
java.lang.NoSuchMethodException: org.deeplearning4j.nn.layers.mkldnn.MKLDNNBatchNormHelper.(java.lang.Class, org.nd4j.linalg.api.buffer.DataType)  
at java.base/java.lang.Class.getConstructor0(Class.java:3349)  
at java.base/java.lang.Class.getDeclaredConstructor(Class.java:2553)  
at org.deeplearning4j.common.config.DL4JClassLoading.createNewInstance(DL4JClassLoading.java:103)  
at org.deeplearning4j.common.config.DL4JClassLoading.createNewInstance(DL4JClassLoading.java:89)  
at org.deeplearning4j.common.config.DL4JClassLoading.createNewInstance(DL4JClassLoading.java:74)  
at org.deeplearning4j.nn.layers.HelperUtils.createHelper(HelperUtils.java:93)  
at org.deeplearning4j.nn.layers.normalization.BatchNormalization.initializeHelper(BatchNormalization.java:74)  
at org.deeplearning4j.nn.layers.normalization.BatchNormalization.(BatchNormalization.java:70)  
at org.deeplearning4j.nn.conf.layers.BatchNormalization.instantiate(BatchNormalization.java:94)  
at org.deeplearning4j.nn.multilayer.MultiLayerNetwork.init(MultiLayerNetwork.java:714)  
at org.deeplearning4j.nn.multilayer.MultiLayerNetwork.init(MultiLayerNetwork.java:604)  
at org.deeplearning4j.nn.modelimport.keras.KerasSequentialModel.getMultiLayerNetwork(KerasSequentialModel.java:260)  
at org.deeplearning4j.nn.modelimport.keras.KerasSequentialModel.getMultiLayerNetwork(KerasSequentialModel.java:249)  
at org.deeplearning4j.nn.modelimport.keras.KerasModelImport.importKerasSequentialModelAndWeights(KerasModelImport.java:297)  
at MLSCM.d4jTesting.SCMGenerator.(SCMGenerator.java:55)  
at MLSCM.d4jTesting.SCMGenerator.main(SCMGenerator.java:89)  
Exception in thread “main” java.lang.RuntimeException: java.lang.NoSuchMethodException: org.deeplearning4j.nn.layers.mkldnn.MKLDNNBatchNormHelper.(java.lang.Class, org.nd4j.linalg.api.buffer.DataType)  
at org.deeplearning4j.common.config.DL4JClassLoading.createNewInstance(DL4JClassLoading.java:108)  
at org.deeplearning4j.common.config.DL4JClassLoading.createNewInstance(DL4JClassLoading.java:89)  
at org.deeplearning4j.common.config.DL4JClassLoading.createNewInstance(DL4JClassLoading.java:74)  
at org.deeplearning4j.nn.layers.HelperUtils.createHelper(HelperUtils.java:93)  
at org.deeplearning4j.nn.layers.normalization.BatchNormalization.initializeHelper(BatchNormalization.java:74)  
at org.deeplearning4j.nn.layers.normalization.BatchNormalization.(BatchNormalization.java:70)  
at org.deeplearning4j.nn.conf.layers.BatchNormalization.instantiate(BatchNormalization.java:94)  
at org.deeplearning4j.nn.multilayer.MultiLayerNetwork.init(MultiLayerNetwork.java:714)  
at org.deeplearning4j.nn.multilayer.MultiLayerNetwork.init(MultiLayerNetwork.java:604)  
at org.deeplearning4j.nn.modelimport.keras.KerasSequentialModel.getMultiLayerNetwork(KerasSequentialModel.java:260)  
at org.deeplearning4j.nn.modelimport.keras.KerasSequentialModel.getMultiLayerNetwork(KerasSequentialModel.java:249)  
at org.deeplearning4j.nn.modelimport.keras.KerasModelImport.importKerasSequentialModelAndWeights(KerasModelImport.java:297)  
at MLSCM.d4jTesting.SCMGenerator.(SCMGenerator.java:55)  
at MLSCM.d4jTesting.SCMGenerator.main(SCMGenerator.java:89)  
Caused by: java.lang.NoSuchMethodException: org.deeplearning4j.nn.layers.mkldnn.MKLDNNBatchNormHelper.(java.lang.Class, org.nd4j.linalg.api.buffer.DataType)  
at java.base/java.lang.Class.getConstructor0(Class.java:3349)  
at java.base/java.lang.Class.getDeclaredConstructor(Class.java:2553)  
at org.deeplearning4j.common.config.DL4JClassLoading.createNewInstance(DL4JClassLoading.java:103)  
… 13 more

---

<div class="post-metadata">

**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:** [May 16, 2021, 10:06pm UTC](https://community.konduit.ai/t/cannot-infer-input-type-for-reshape-array/1358/16 "2021-05-16T22:06:10Z")

</div>

@Acander make sure to update your dependencies running mvn -U $YOUR\_COMMAND.  
We already merged the fix for this:

> <https://github.com/eclipse/deeplearning4j/pull/9308>
>
> \## What changes were proposed in this pull request?
> Fixes up issue found here:
> …
> https://community.konduit.ai/t/cannot-infer-input-type-for-reshape-array/1358/13?u=agibsonccc
> Also adds tests for both cpu and gpu.
> (Please fill in changes proposed in this fix)
> 
> \## How was this patch tested?
> 
> (Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
> 
> \## Quick checklist
> 
> The following checklist helps ensure your PR is complete:
> 
> \- \[X\] Eclipse Contributor Agreement signed, and signed commits - see \[IP Requirements\](https://deeplearning4j.org/eclipse-contributors) page for details
> \- \[X\] Reviewed the \[Contributing Guidelines\](https://github.com/eclipse/deeplearning4j/blob/master/CONTRIBUTING.md) and followed the steps within.
> \- \[X\] Created tests for any significant new code additions.
> \- \[X \] Relevant tests for your changes are passing.

You can see the tests working as follows:

> <https://github.com/eclipse/deeplearning4j/pull/9308/files#diff-6550d44a45c0dc45824fc6a01bb57dbc14b0aecb072fed43b15ac72b45d48b89R77>
>
> \## What changes were proposed in this pull request?
> Fixes up issue found here:
> …
> https://community.konduit.ai/t/cannot-infer-input-type-for-reshape-array/1358/13?u=agibsonccc
> Also adds tests for both cpu and gpu.
> (Please fill in changes proposed in this fix)
> 
> \## How was this patch tested?
> 
> (Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
> 
> \## Quick checklist
> 
> The following checklist helps ensure your PR is complete:
> 
> \- \[X\] Eclipse Contributor Agreement signed, and signed commits - see \[IP Requirements\](https://deeplearning4j.org/eclipse-contributors) page for details
> \- \[X\] Reviewed the \[Contributing Guidelines\](https://github.com/eclipse/deeplearning4j/blob/master/CONTRIBUTING.md) and followed the steps within.
> \- \[X\] Created tests for any significant new code additions.
> \- \[X \] Relevant tests for your changes are passing.

The layers now look like this:

> <https://github.com/eclipse/deeplearning4j/pull/9308/files#diff-11a7f72b1d59f37f80c25168c3897bb3af6f49ac5f17ff58952f4825de95ceceR110>
>
> \## What changes were proposed in this pull request?
> Fixes up issue found here:
> …
> https://community.konduit.ai/t/cannot-infer-input-type-for-reshape-array/1358/13?u=agibsonccc
> Also adds tests for both cpu and gpu.
> (Please fill in changes proposed in this fix)
> 
> \## How was this patch tested?
> 
> (Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
> 
> \## Quick checklist
> 
> The following checklist helps ensure your PR is complete:
> 
> \- \[X\] Eclipse Contributor Agreement signed, and signed commits - see \[IP Requirements\](https://deeplearning4j.org/eclipse-contributors) page for details
> \- \[X\] Reviewed the \[Contributing Guidelines\](https://github.com/eclipse/deeplearning4j/blob/master/CONTRIBUTING.md) and followed the steps within.
> \- \[X\] Created tests for any significant new code additions.
> \- \[X \] Relevant tests for your changes are passing.

In my comment above, I’m not sure what was confused but let me clear up a few things:

1. I told you to use a workaround by commenting out the creation of the helper. It’s optional and just meant to be an accelerator for people to use. I tried to describe the nature of the error:  
it’s a reflection error in java. The constructor wasn’t present. We added tests for that so it shouldn’t be an issue now. The workaround I gave you just commented out creating that so you could get passed the invalid constructor.

2. Again, this has nothing to do with model import. Try to understand what the actual nature of the error is. It has nothing to do with model import and I’m sorry if I wasn’t clear about that. Model import just parses a keras file then tries to instantiate the creation of the equivalent configuration in dl4j. The part that fails is not the model import part here, it’s a completely unrelated issue as described above.

Hopefully that helps explain the fix as well as the nature of the issue. Let me know if you have any other issues with the updates or otherwise.

---

<div class="post-metadata">

**Author:** ![Acander](https://yyz1.discourse-cdn.com/flex035/user_avatar/community.konduit.ai/acander/32/520_2.png) [@Acander](https://community.konduit.ai/u/Acander)\
**Post date:** [May 17, 2021, 1:58pm UTC](https://community.konduit.ai/t/cannot-infer-input-type-for-reshape-array/1358/17 "2021-05-17T13:58:16Z")

</div>

Hi @agibsonccc! I have updated maven many times now, but I still get the same error. The merged fix you have mentioned seems to have with the the MKLDNNLSTMHelper.class, but from reading the error above the problem seems to have with the MKLDNNBatchNormHelper. I am sorry if I seem rude! It is not my meaning. It could of course be some kind of import error.

---

<div class="post-metadata">

**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:** [May 17, 2021, 11:27pm UTC](https://community.konduit.ai/t/cannot-infer-input-type-for-reshape-array/1358/18 "2021-05-17T23:27:20Z")

</div>

@Acander if this happens again, do you mind installing from master? For this specific issue, just run:

```bash
git clone https://github.com/eclipse/deeplearning4j
cd deeplearning4j/deeplearning4j-nn && mvn clean install -DskipTests

```

Snapshots may not have updated. I believe it’s fixed now, but do that just in case.  
In order to check this for yourself, go here:  
[https://oss.sonatype.org/content/repositories/snapshots/org/deeplearning4j/deeplearning4j-nn/1.0.0-SNAPSHOT/](https://oss.sonatype.org/content/repositories/snapshots/org/deeplearning4j/deeplearning4j-nn/1.0.0-SNAPSHOT/)

This is for the specific module where your problem is occurring. This is where you can browse snapshots for any artifact by groupId/artifactId/version.

Generally scroll down to the bottom to see when the last snapshots have been updated.  
Cross check this with when the pull request was merged:

> <https://github.com/eclipse/deeplearning4j/pull/9308>
>
> \## What changes were proposed in this pull request?
> Fixes up issue found here:
> …
> https://community.konduit.ai/t/cannot-infer-input-type-for-reshape-array/1358/13?u=agibsonccc
> Also adds tests for both cpu and gpu.
> (Please fill in changes proposed in this fix)
> 
> \## How was this patch tested?
> 
> (Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
> 
> \## Quick checklist
> 
> The following checklist helps ensure your PR is complete:
> 
> \- \[X\] Eclipse Contributor Agreement signed, and signed commits - see \[IP Requirements\](https://deeplearning4j.org/eclipse-contributors) page for details
> \- \[X\] Reviewed the \[Contributing Guidelines\](https://github.com/eclipse/deeplearning4j/blob/master/CONTRIBUTING.md) and followed the steps within.
> \- \[X\] Created tests for any significant new code additions.
> \- \[X \] Relevant tests for your changes are passing.

And no you’re not being rude at all! We’re trying to get your problem solved and I’d like to help. Good luck!