Provided weight data has no target variable: batch_normalization

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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>)

[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.

[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.