DeepLearning: Got error when try to import one test data into model

Question

I am new on deep learning. To practicing I trained a simple Handwriting model with tensor-flow and mnist. After loading mnist I made model and trained that:

(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
x_train = x_train.astype('float32')/255
x_test =  x_test.astype('float32')/255
model = keras.Sequential([
    keras.layers.Flatten(input_shape=(28,28)),
    keras.layers.Dense(100,activation = 'relu'),
    keras.layers.Dense(100,activation = 'sigmoid'),
    keras.layers.Dense(10,activation = 'sigmoid'),
])

As you can see, I flatted my first layer into 784 px One-dimensional array.

I reshape my test data too:

x_test_flattened = x_test.reshape(len(x_test),28*28)

Now, In predict I want to import just one data into model to test it:

y_predicted = model.predict(x_test_flattened[0])

but I got these errors:

WARNING:tensorflow:Model was constructed with shape (None, 28, 28) for input Tensor("flatten_input:0", shape=(None, 28, 28), dtype=float32), but it was called on an input with incompatible shape (None, 1).
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-13-1bdc43d66a07> in <module>
----> 1 y_predicted = model.predict(x_test_flattened[0])
      2 # y_predicted[1]

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py in _method_wrapper(self, *args, **kwargs)
     86       raise ValueError('{} is not supported in multi-worker mode.'.format(
     87           method.__name__))
---> 88     return method(self, *args, **kwargs)
     89 
     90   return tf_decorator.make_decorator(

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py in predict(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)
   1266           for step in data_handler.steps():
   1267             callbacks.on_predict_batch_begin(step)
-> 1268             tmp_batch_outputs = predict_function(iterator)
   1269             # Catch OutOfRangeError for Datasets of unknown size.
   1270             # This blocks until the batch has finished executing.

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
    578         xla_context.Exit()
    579     else:
--> 580       result = self._call(*args, **kwds)
    581 
    582     if tracing_count == self._get_tracing_count():

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
    625       # This is the first call of __call__, so we have to initialize.
    626       initializers = []
--> 627       self._initialize(args, kwds, add_initializers_to=initializers)
    628     finally:
    629       # At this point we know that the initialization is complete (or less

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
    503     self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)
    504     self._concrete_stateful_fn = (
--> 505         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access
    506             *args, **kwds))
    507 

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
   2444       args, kwargs = None, None
   2445     with self._lock:
-> 2446       graph_function, _, _ = self._maybe_define_function(args, kwargs)
   2447     return graph_function
   2448 

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
   2775 
   2776       self._function_cache.missed.add(call_context_key)
-> 2777       graph_function = self._create_graph_function(args, kwargs)
   2778       self._function_cache.primary[cache_key] = graph_function
   2779       return graph_function, args, kwargs

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
   2655     arg_names = base_arg_names + missing_arg_names
   2656     graph_function = ConcreteFunction(
-> 2657         func_graph_module.func_graph_from_py_func(
   2658             self._name,
   2659             self._python_function,

~/anaconda3/lib/python3.8/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)
    979         _, original_func = tf_decorator.unwrap(python_func)
    980 
--> 981       func_outputs = python_func(*func_args, **func_kwargs)
    982 
    983       # invariant: `func_outputs` contains only Tensors, CompositeTensors,

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
    439         # __wrapped__ allows AutoGraph to swap in a converted function. We give
    440         # the function a weak reference to itself to avoid a reference cycle.
--> 441         return weak_wrapped_fn().__wrapped__(*args, **kwds)
    442     weak_wrapped_fn = weakref.ref(wrapped_fn)
    443 

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
    966           except Exception as e:  # pylint:disable=broad-except
    967             if hasattr(e, "ag_error_metadata"):
--> 968               raise e.ag_error_metadata.to_exception(e)
    969             else:
    970               raise

ValueError: in user code:

    /home/Alt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:1147 predict_function  *
        outputs = self.distribute_strategy.run(
    /home/Alt/anaconda3/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:951 run  **
        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
    /home/Alt/anaconda3/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:2290 call_for_each_replica
        return self._call_for_each_replica(fn, args, kwargs)
    /home/Alt/anaconda3/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:2649 _call_for_each_replica
        return fn(*args, **kwargs)
    /home/Alt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:1122 predict_step  **
        return self(x, training=False)
    /home/Alt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py:927 __call__
        outputs = call_fn(cast_inputs, *args, **kwargs)
    /home/Alt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/sequential.py:277 call
        return super(Sequential, self).call(inputs, training=training, mask=mask)
    /home/Alt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/network.py:717 call
        return self._run_internal_graph(
    /home/Alt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/network.py:888 _run_internal_graph
        output_tensors = layer(computed_tensors, **kwargs)
    /home/Alt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py:885 __call__
        input_spec.assert_input_compatibility(self.input_spec, inputs,
    /home/Alt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/input_spec.py:212 assert_input_compatibility
        raise ValueError(

    ValueError: Input 0 of layer dense is incompatible with the layer: expected axis -1 of input shape to have value 784 but received input with shape [None, 1]

But when I import all flatted x_test data everything is good

y_predicted = model.predict(x_test_flattened)
y_predicted[1]
array([6.1507606e-08, 1.2000690e-05, 5.1280117e-01, 1.5896080e-06,
       2.6905557e-08, 2.2364643e-06, 1.0007229e-06, 6.0152153e-08,
       2.0705515e-05, 2.9885057e-09], dtype=float32)

How Can I test just one data?

Answer 1

predict always takes a "batch" so you need to make sure your single item "becomes" a batch. Just do

y_predicted = model.predict(tf.expand_dims(x_test_flattened[0], axis=0))