Provided weight data has no target variable: batch_normalization

[rajeev-samalkha]

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TensorFlow.js version

0.13

Browser version

Chrome Version 69.0.3497.100

Describe the problem or feature request

I converted a Keras model to tfjs using python utility with no errors. But when I try to load the model in tfjs, I get the following error:

tfjs@0.13.0:2 Uncaught (in promise) Error: Provided weight data has no target variable: batch_normalization_1_2/gamma
    at new t (tfjs@0.13.0:2)
    at loadWeightsFromNamedTensorMap (tfjs@0.13.0:2)
    at t.loadWeights (tfjs@0.13.0:2)
    at tfjs@0.13.0:2
    at tfjs@0.13.0:2
    at Object.next (tfjs@0.13.0:2)
    at i (tfjs@0.13.0:2)

Code to reproduce the bug / link to feature request

Running it on local machine.
model = await tf.loadModel(<path_to_model.json>)

[bileschi]

Hi
Can you please share your (original) model and the commands used to convert & load?
Thanks

[davidsoergel]

If that weight is an extra one that is lying around for some reason but is not actually needed, you can call tf.loadModel(..., strict=false) to disable the error.

Of course, if the weight is needed, doing this would leave you with a broken model. In that case, as @bileschi said, we'd need to see the original Keras model to determine whether there is a conversion bug.

[bileschi]

@rajeev-samalkha is this issue resolved? If so feel free to close. Thankyou.

[rajeev-samalkha]

Folks, sorry for delayed response.

When I take out Batch Norm layer then it seems to work fine (I had to retrain the model). Is there any difference between Batch norm in tf.keras and tfjs. I have used tensorflowjs.converter utility. The model in itself is quite simple with few Con2D layers interspersed by Max Pool/Dropout.

Regards
Rajeev

[bileschi]

TFJS BatchNorm maintains 4 up to weights:
gamma, beta, movingMean, and movingVariance which matches those in keras-team/keras

https://github.com/keras-team/keras/blob/master/keras/layers/normalization.py#L93

I wonder if tf.keras is saving additional tensors, possibly for optimization or related to the momentum for training?

Looking at the tensorflow/keras implementation, I see that it is somewhat more complex: including a 'fused' batch-norm implementation that reaches into c.
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/layers/normalization.py

Can you list out the weights in your model from the python code?

[rajeev-samalkha]

Do you need weights for all the layers or just BatchNorm one.

[bileschi]

Just the names and sizes of the batch_norm related weights should be fine. No need to provide the actual values.

…

On Wed, Oct 10, 2018 at 10:51 AM rajeev-samalkha ***@***.***> wrote: Do you need weights for all the layers or just BatchNorm one. — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub <#755 (comment)>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AAhZTn53dfvl1D88ywKN59zRuoDWERpgks5ujglrgaJpZM4XFl_7> .

-- Stan Bileschi Ph.D. | SWE | bileschi@google.com | 617-230-8081

[Raza25]

I get the same error while loading a keras converted model on browser.
following error pops up

As for the model weights:

Note: Some of my converted model tend to work fine on browser having embeddings in it, but sometimes this error shows up. @bileschi @davidsoergel kindly post a proper fix for this issue.

[ashsharma28]

Same error here, Someone please help.

tfjs@0.13.0:2 Uncaught (in promise) Error: Provided weight data has no target variable: conv2d/kernel
at new t (tfjs@0.13.0:2)
at loadWeightsFromNamedTensorMap (tfjs@0.13.0:2)
at t.loadWeights (tfjs@0.13.0:2)
at tfjs@0.13.0:2
at tfjs@0.13.0:2
at Object.next (tfjs@0.13.0:2)
at i (tfjs@0.13.0:2)

[rajeev-tbrew]

I hit the same error without batch norm. Appreciate your help.

[rajeev-tbrew]

Folks

I think I found why we are getting this error. The error can happen for any layer. Steps to reproduce the error:

1. Load the model in tensorFlow using tf.keras.
2. Load the same model again (basically load the model more than once).
3. Use tfjs.converters to convert keras model and you get this error.

It seems every layer name changes in model.json file (it will be different than model.summary name). For example, one of the layer in my model was 'conv2d_6' but it got named as 'conv2d_6_2' when I loaded the model twice. But it seems actual weights (assuming in shard file) still expect 'conv2d_6 in my case.

So till we get a fix, pls make sure you load your model only once before doing tfjs conversion. Hope this helps.

[hardikmodi1]

I have tried that too by loading the model exactly once but still, the same error prevails.

[stephenrt42]

Not sure if this helps, but when I converted my h5 model using the python code
`import tensorflowjs as tfjs
from keras.models import load_model

modelk = load_model('./input/model.h5')
tfjs.converters.save_keras_model(modelk, './output/')`

I would receive the following error:

errors.ts:48 Uncaught (in promise) Error: Provided weight data has no target variable: dense_1_7/kernel at new t (errors.ts:48) at loadWeightsFromNamedTensorMap (container.ts:190) at t.loadWeights (container.ts:759) at models.ts:285 at index.ts:79 at Object.next (index.ts:79) at i (index.ts:79)

But if I convert the h5 model using the tensorflowjs_converter command line tool my tfjs json model file will load without any problems.