TFLite 2.16.1 conversions fail with "AttributeError: 'Sequential' object has no attribute '_get_save_spec'" about tensorflow HOT 9 OPEN

@pkgoogle I downgraded to 2.15 and it worked, thanks.

from tensorflow.

    model = Sequential([
        data_augmentation,
        # layers.Rescaling(1./255, input_shape=(img_height, img_width, 3)),
        # layers.Conv2D(16, 3, padding='same', activation=activation1),
        # layers.MaxPooling2D(),
        layers.Conv2D(32, 3, padding='same', activation=activation1),
        layers.MaxPooling2D(),
        layers.Conv2D(48, 3, padding='same', activation=activation2),
        layers.MaxPooling2D(),
        layers.Conv2D(64, 3, padding='same', activation=activation1),
        layers.MaxPooling2D(),
        layers.Dropout(0.15),
        layers.Flatten(),
        layers.Dense(128, activation=activation2),
        layers.Dense(num_classes)
    ])

Sequential here might be imported from something other than tf.keras. Please take a look at #39001

from tensorflow.

Hi @schwefeljm,

I am trying to reproduce the code meanwhile I encountered some other error. Is it `args = argparser(). Please provide complete reproducible code/link to debug the issue.

Thank You

from tensorflow.

Hi @schwefeljm,

I am trying to reproduce the code meanwhile I encountered some other error. Is it `args = argparser(). Please provide complete reproducible code/link to debug the issue.

Thank You
Hi @LakshmiKalaKadali ,

I updated to code to remove the depency on 'argsparser()'

The dataset I used is from: https://www.kaggle.com/datasets/gpiosenka/100-bird-species Though, I expect it work on any dataset.

Jason

from tensorflow.

    model = Sequential([
        data_augmentation,
        # layers.Rescaling(1./255, input_shape=(img_height, img_width, 3)),
        # layers.Conv2D(16, 3, padding='same', activation=activation1),
        # layers.MaxPooling2D(),
        layers.Conv2D(32, 3, padding='same', activation=activation1),
        layers.MaxPooling2D(),
        layers.Conv2D(48, 3, padding='same', activation=activation2),
        layers.MaxPooling2D(),
        layers.Conv2D(64, 3, padding='same', activation=activation1),
        layers.MaxPooling2D(),
        layers.Dropout(0.15),
        layers.Flatten(),
        layers.Dense(128, activation=activation2),
        layers.Dense(num_classes)
    ])

Sequential here might be imported from something other than tf.keras. Please take a look at #39001
@Aloqeely

I went through and forced 'tf.keras.models.Sequential' and it had no effect. Thank you for the suggestions, though.

Jason

from tensorflow.

I was able to replicate on tf-nightly as well as 2.16.1. gist, I reduced the reproducible sample to what mattered i.e. the training process is actually irrelevant. This appears to happen when using Keras with the TFLite converter in 2.16.1 onward.

Hi @haozha111, can you please take a look?

from tensorflow.

I'm experiencing the same error here

converter = tf.lite.TFLiteConverter.from_keras_model(model)
tfmodel = converter.convert()
open ("mobilenetv2_finetuned.tflite" , "wb") .write(tfmodel)

Is it gonna help if I downgrade to an older version of Tensorflow?

from tensorflow.

I'm experiencing the same error here

converter = tf.lite.TFLiteConverter.from_keras_model(model)
tfmodel = converter.convert()
open ("mobilenetv2_finetuned.tflite" , "wb") .write(tfmodel)

Is it gonna help if I downgrade to an older version of Tensorflow?

@Takudzwamz

For me it did.

from tensorflow.

Hi @Takudzwamz, it seems like 2.15 does not exhibit this behavior currently.

from tensorflow.