How to overcome value error python model prediction?

Question

I have trained a model and now my task was to test it on unseen images from the internet. Originally the model was trained on CIFAR-10 so for the model I chose images of cat and dog taken from the internet. Once running I have encountered an error please see it below.

 import numpy as np
    import tensorflow as tf
    from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions
    from tensorflow.keras.preprocessing import image
    from keras.models import model_from_json
    from keras.optimizers import SGD
    import matplotlib.pyplot as plt
    

# In[2]:


#load model
model_architecture = 'cifar10_architecture.json'
model_weights = 'cifar10_weights.h5'
model = model_from_json(open(model_architecture).read())
model.load_weights(model_weights)


# In[3]:


#load images
img1 = image.load_img('cat.jpg', target_size=(32, 32))
img2 = image.load_img('dog.jpg', target_size=(32, 32))


# In[4]:


#plot images
f, axarr = plt.subplots(1,2)
axarr[0].imshow(img1)
axarr[1].imshow(img2)


# In[5]:


img_array = [img1, img2]
imgs = [np.transpose(img_name).astype('float32') for img_name in img_array]
imgs = np.array(imgs)/255


# In[6]:


#train
optim = SGD()
model.compile(loss = 'categorical_crossentropy', optimizer = optim, metrics = ['accuracy'])


# In[7]:


#predict
predictions = model.predict_classes(imgs)
print(predictions)

Here is an error. I fed the image and transformed them.

WARNING:tensorflow:From <ipython-input-7-5c038790856b>:2: Sequential.predict_classes (from tensorflow.python.keras.engine.sequential) is deprecated and will be removed after 2021-01-01.
Instructions for updating:
Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-7-5c038790856b> in <module>
      1 #predict
----> 2 predictions = model.predict_classes(imgs)
      3 print(predictions)

~\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\util\deprecation.py in new_func(*args, **kwargs)
    322               'in a future version' if date is None else ('after %s' % date),
    323               instructions)
--> 324       return func(*args, **kwargs)
    325     return tf_decorator.make_decorator(
    326         func, new_func, 'deprecated',

~\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\engine\sequential.py in predict_classes(self, x, batch_size, verbose)
    451         A numpy array of class predictions.
    452     """
--> 453     proba = self.predict(x, batch_size=batch_size, verbose=verbose)
    454     if proba.shape[-1] > 1:
    455       return proba.argmax(axis=-1)

~\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\engine\training.py in _method_wrapper(self, *args, **kwargs)
    128       raise ValueError('{} is not supported in multi-worker mode.'.format(
    129           method.__name__))
--> 130     return method(self, *args, **kwargs)
    131 
    132   return tf_decorator.make_decorator(

~\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\engine\training.py in predict(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)
   1597           for step in data_handler.steps():
   1598             callbacks.on_predict_batch_begin(step)
-> 1599             tmp_batch_outputs = predict_function(iterator)
   1600             if data_handler.should_sync:
   1601               context.async_wait()

~\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\eager\def_function.py in __call__(self, *args, **kwds)
    778       else:
    779         compiler = "nonXla"
--> 780         result = self._call(*args, **kwds)
    781 
    782       new_tracing_count = self._get_tracing_count()

~\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\eager\def_function.py in _call(self, *args, **kwds)
    821       # This is the first call of __call__, so we have to initialize.
    822       initializers = []
--> 823       self._initialize(args, kwds, add_initializers_to=initializers)
    824     finally:
    825       # At this point we know that the initialization is complete (or less

~\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\eager\def_function.py in _initialize(self, args, kwds, add_initializers_to)
    695     self._concrete_stateful_fn = (
    696         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access
--> 697             *args, **kwds))
    698 
    699     def invalid_creator_scope(*unused_args, **unused_kwds):

~\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\eager\function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
   2853       args, kwargs = None, None
   2854     with self._lock:
-> 2855       graph_function, _, _ = self._maybe_define_function(args, kwargs)
   2856     return graph_function
   2857 

~\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\eager\function.py in _maybe_define_function(self, args, kwargs)
   3211 
   3212       self._function_cache.missed.add(call_context_key)
-> 3213       graph_function = self._create_graph_function(args, kwargs)
   3214       self._function_cache.primary[cache_key] = graph_function
   3215       return graph_function, args, kwargs

~\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\eager\function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
   3073             arg_names=arg_names,
   3074             override_flat_arg_shapes=override_flat_arg_shapes,
-> 3075             capture_by_value=self._capture_by_value),
   3076         self._function_attributes,
   3077         function_spec=self.function_spec,

~\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\framework\func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
    984         _, original_func = tf_decorator.unwrap(python_func)
    985 
--> 986       func_outputs = python_func(*func_args, **func_kwargs)
    987 
    988       # invariant: `func_outputs` contains only Tensors, CompositeTensors,

~\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\eager\def_function.py in wrapped_fn(*args, **kwds)
    598         # __wrapped__ allows AutoGraph to swap in a converted function. We give
    599         # the function a weak reference to itself to avoid a reference cycle.
--> 600         return weak_wrapped_fn().__wrapped__(*args, **kwds)
    601     weak_wrapped_fn = weakref.ref(wrapped_fn)
    602 

~\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\framework\func_graph.py in wrapper(*args, **kwargs)
    971           except Exception as e:  # pylint:disable=broad-except
    972             if hasattr(e, "ag_error_metadata"):
--> 973               raise e.ag_error_metadata.to_exception(e)
    974             else:
    975               raise

ValueError: in user code:

    C:\Users\nikit\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\engine\training.py:1462 predict_function  *
        return step_function(self, iterator)
    C:\Users\nikit\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\engine\training.py:1452 step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    C:\Users\nikit\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\distribute\distribute_lib.py:1211 run
        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
    C:\Users\nikit\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\distribute\distribute_lib.py:2585 call_for_each_replica
        return self._call_for_each_replica(fn, args, kwargs)
    C:\Users\nikit\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\distribute\distribute_lib.py:2945 _call_for_each_replica
        return fn(*args, **kwargs)
    C:\Users\nikit\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\engine\training.py:1445 run_step  **
        outputs = model.predict_step(data)
    C:\Users\nikit\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\engine\training.py:1418 predict_step
        return self(x, training=False)
    C:\Users\nikit\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\engine\base_layer.py:976 __call__
        self.name)
    C:\Users\nikit\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\engine\input_spec.py:216 assert_input_compatibility
        ' but received input with shape ' + str(shape))

    ValueError: Input 0 of layer sequential_2 is incompatible with the layer: expected axis -1 of input shape to have value 3 but received input with shape [None, 3, 32, 32]

Answer 1

As the error states, the model expects the input with shape [None, 32, 32, 3] instead of [None, 3, 32, 32]. Adding imgs=np.moveaxis(imgs,1,-1) before compiling will solve the error.

imgs=np.moveaxis(imgs,1,-1)

#train
optim = tf.keras.optimizers.SGD()
model.compile(loss = 'categorical_crossentropy', optimizer = optim, metrics = ['accuracy'])

Let us know if the issue still persists. Thanks!