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?
https://stackoverflow.com/questions/65768689/deeplearning-got-error-when-try-to-import-one-test-data-into-model January 18, 2021 at 12:05PM
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