"ValueError: Unknown layer: Functional"

Question

I have trained my model on Google Teachable Machine and have downloaded the trained model and I am using that model to classify the images. I am experiencing this error and don't know what is it trying to say or how to solve it. My environment package details are: Tensorflow: 2.1.0 Keras: 2.3.1 Pillow: 7.0.0 h5py: 2.10.0 Below is the code I am trying to run.

import tensorflow.keras
from PIL import Image, ImageOps
import numpy as np

# Disable scientific notation for clarity
np.set_printoptions(suppress=True)

# Load the model
model = tensorflow.keras.models.load_model('keras_model.h5')

# Create the array of the right shape to feed into the keras model
# The 'length' or number of images you can put into the array is
# determined by the first position in the shape tuple, in this case 1.
data = np.ndarray(shape=(1, 32, 32, 3), dtype=np.float32)

# Replace this with the path to your image
image = Image.open(r'C:\Users\DELL\Desktop\Dataset\TEST\2_Final\Alaa\image_14022021_065404.jpg')

#resize the image to a 224x224 with the same strategy as in TM2:
#resizing the image to be at least 224x224 and then cropping from the center
size = (32, 32)
image = ImageOps.fit(image, size, Image.ANTIALIAS)

#turn the image into a numpy array
image_array = np.asarray(image)

# display the resized image
image.show()

# Normalize the image
normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1

# Load the image into the array
data[0] = normalized_image_array

# run the inference
prediction = model.predict(data)
print(prediction)

This is the full traceback

ValueError                                Traceback (most recent call last)
<ipython-input-3-ac2b19895981> in <module>
      9 
     10 # Load the model
---> 11 model = tensorflow.keras.models.load_model('keras_model.h5')
     12 
     13 # Create the array of the right shape to feed into the keras model

~\Anaconda3\envs\teach11\lib\site-packages\tensorflow_core\python\keras\saving\save.py in load_model(filepath, custom_objects, compile)
    144   if (h5py is not None and (
    145       isinstance(filepath, h5py.File) or h5py.is_hdf5(filepath))):
--> 146     return hdf5_format.load_model_from_hdf5(filepath, custom_objects, compile)
    147 
    148   if isinstance(filepath, six.string_types):

~\Anaconda3\envs\teach11\lib\site-packages\tensorflow_core\python\keras\saving\hdf5_format.py in load_model_from_hdf5(filepath, custom_objects, compile)
    166     model_config = json.loads(model_config.decode('utf-8'))
    167     model = model_config_lib.model_from_config(model_config,
--> 168                                                custom_objects=custom_objects)
    169 
    170     # set weights

~\Anaconda3\envs\teach11\lib\site-packages\tensorflow_core\python\keras\saving\model_config.py in model_from_config(config, custom_objects)
     53                     '`Sequential.from_config(config)`?')
     54   from tensorflow.python.keras.layers import deserialize  # pylint: disable=g-import-not-at-top
---> 55   return deserialize(config, custom_objects=custom_objects)
     56 
     57 

~\Anaconda3\envs\teach11\lib\site-packages\tensorflow_core\python\keras\layers\serialization.py in deserialize(config, custom_objects)
    104       module_objects=globs,
    105       custom_objects=custom_objects,
--> 106       printable_module_name='layer')

~\Anaconda3\envs\teach11\lib\site-packages\tensorflow_core\python\keras\utils\generic_utils.py in deserialize_keras_object(identifier, module_objects, custom_objects, printable_module_name)
    301             custom_objects=dict(
    302                 list(_GLOBAL_CUSTOM_OBJECTS.items()) +
--> 303                 list(custom_objects.items())))
    304       with CustomObjectScope(custom_objects):
    305         return cls.from_config(cls_config)

~\Anaconda3\envs\teach11\lib\site-packages\tensorflow_core\python\keras\engine\sequential.py in from_config(cls, config, custom_objects)
    375     for layer_config in layer_configs:
    376       layer = layer_module.deserialize(layer_config,
--> 377                                        custom_objects=custom_objects)
    378       model.add(layer)
    379     if not model.inputs and build_input_shape:

~\Anaconda3\envs\teach11\lib\site-packages\tensorflow_core\python\keras\layers\serialization.py in deserialize(config, custom_objects)
    104       module_objects=globs,
    105       custom_objects=custom_objects,
--> 106       printable_module_name='layer')

~\Anaconda3\envs\teach11\lib\site-packages\tensorflow_core\python\keras\utils\generic_utils.py in deserialize_keras_object(identifier, module_objects, custom_objects, printable_module_name)
    301             custom_objects=dict(
    302                 list(_GLOBAL_CUSTOM_OBJECTS.items()) +
--> 303                 list(custom_objects.items())))
    304       with CustomObjectScope(custom_objects):
    305         return cls.from_config(cls_config)

~\Anaconda3\envs\teach11\lib\site-packages\tensorflow_core\python\keras\engine\sequential.py in from_config(cls, config, custom_objects)
    375     for layer_config in layer_configs:
    376       layer = layer_module.deserialize(layer_config,
--> 377                                        custom_objects=custom_objects)
    378       model.add(layer)
    379     if not model.inputs and build_input_shape:

~\Anaconda3\envs\teach11\lib\site-packages\tensorflow_core\python\keras\layers\serialization.py in deserialize(config, custom_objects)
    104       module_objects=globs,
    105       custom_objects=custom_objects,
--> 106       printable_module_name='layer')

~\Anaconda3\envs\teach11\lib\site-packages\tensorflow_core\python\keras\utils\generic_utils.py in deserialize_keras_object(identifier, module_objects, custom_objects, printable_module_name)
    290     config = identifier
    291     (cls, cls_config) = class_and_config_for_serialized_keras_object(
--> 292         config, module_objects, custom_objects, printable_module_name)
    293 
    294     if hasattr(cls, 'from_config'):

~\Anaconda3\envs\teach11\lib\site-packages\tensorflow_core\python\keras\utils\generic_utils.py in class_and_config_for_serialized_keras_object(config, module_objects, custom_objects, printable_module_name)
    248     cls = module_objects.get(class_name)
    249     if cls is None:
--> 250       raise ValueError('Unknown ' + printable_module_name + ': ' + class_name)
    251 
    252   cls_config = config['config']

ValueError: Unknown layer: Functional

Answer 1

The model file you are loading seems not to contain the model, but only the weights. Can you make sure the h5 file also contains the full model definition?