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
