RuntimeError: Model-building function did not return a valid Keras Model instance

Hi,

I followed exactly the tutorial and I got this error …

Input :

import keras
from keras import layers
from kerastuner.tuners import RandomSearch

def build_model(hp):
    model = keras.Sequential()
    model.add(layers.Dense(units=hp.Int('units',
                                        min_value=32,
                                        max_value=512,
                                        step=32),
                           activation='relu'))
    model.add(layers.Dense(10, activation='softmax'))
    model.compile(
        optimizer=keras.optimizers.Adam(
            hp.Choice('learning_rate',
                      values=[1e-2, 1e-3, 1e-4])),
        loss='sparse_categorical_crossentropy',
        metrics=['accuracy'])
    return model

tuner = RandomSearch(
    build_model,
    objective='val_accuracy',
    max_trials=5,
    executions_per_trial=3,
    directory='my_dir',
    project_name='helloworld')

Output :

RuntimeError: Model-building function did not return a valid Keras Model instance, found <keras.engine.sequential.Sequential object at 0x7f7209a72950>

Help please ?

[Vaunorage]

OKay sorry I got it on my own…

Just needed to change the import from

import keras to from tensorflow import keras

Now it works !

[rao208]

Oh! It was such a silly mistake on my end. It is working now. Thank you! @ben-arnao

can you please share it? i am getting the same error and I don’t understand where am I going wrong.

My layers and optimizers were imported from tensorflow.keras like this

from tensorflow.keras import layers
from tensorflow.keras.optimizers import Adam, Adadelta

whereas Sequential and layers like Conv2D, MaxPooling etc were imported from keras like this:

from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Dense, Lambda, Flatten, Dropout, BatchNormalization

Also, doing this from tensorflow import keras doesn’t help, because I had both, tensorflow 2.0 and keras installed.

So, all I had to do was change all from keras. import ..... to from tensorflow.keras. import .... to get rid of the error.