I am creating an encoder on the fashion MNIST dataset. The encoder consists of three layers, Each input image is flattened into a dimensionality of 784. The three encoder layers are with output dimensionality of 128, 64, 32. mBut after fitting the model, it throws a value error - ValueError: Input 0 is incompatible with layer model_7: expected shape=(None, 784), found shape=(32, 28, 28)
the code:-
#Encoder
input1 = Input(shape = (784,))
hidden1 = Dense(128, activation = 'relu')(input1)
hidden2 = Dense(64, activation = 'relu')(hidden1)
hidden3 = Dense(32, activation = 'relu')(hidden2)
model = Model(inputs = input1, outputs = hidden3)
model summary:
Model: "model_7"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_17 (InputLayer) [(None, 784)] 0
_________________________________________________________________
dense_43 (Dense) (None, 128) 100480
_________________________________________________________________
dense_44 (Dense) (None, 64) 8256
_________________________________________________________________
dense_45 (Dense) (None, 32) 2080
=================================================================
Total params: 110,816
Trainable params: 110,816
Non-trainable params: 0
The error after fitting the model:-
Epoch 1/3
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-42-3295f6ac1688> in <module>
----> 1 model.fit(x_train, y_train, epochs = 3)
C:\Anaconda\lib\site-packages\tensorflow\python\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
1098 _r=1):
1099 callbacks.on_train_batch_begin(step)
-> 1100 tmp_logs = self.train_function(iterator)
1101 if data_handler.should_sync:
1102 context.async_wait()
C:\Anaconda\lib\site-packages\tensorflow\python\eager\def_function.py in __call__(self, *args, **kwds)
826 tracing_count = self.experimental_get_tracing_count()
827 with trace.Trace(self._name) as tm:
--> 828 result = self._call(*args, **kwds)
829 compiler = "xla" if self._experimental_compile else "nonXla"
830 new_tracing_count = self.experimental_get_tracing_count()
C:\Anaconda\lib\site-packages\tensorflow\python\eager\def_function.py in _call(self, *args, **kwds)
869 # This is the first call of __call__, so we have to initialize.
870 initializers = []
--> 871 self._initialize(args, kwds, add_initializers_to=initializers)
872 finally:
873 # At this point we know that the initialization is complete (or less
C:\Anaconda\lib\site-packages\tensorflow\python\eager\def_function.py in _initialize(self, args, kwds, add_initializers_to)
723 self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)
724 self._concrete_stateful_fn = (
--> 725 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
726 *args, **kwds))
727
C:\Anaconda\lib\site-packages\tensorflow\python\eager\function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
2967 args, kwargs = None, None
2968 with self._lock:
-> 2969 graph_function, _ = self._maybe_define_function(args, kwargs)
2970 return graph_function
2971
C:\Anaconda\lib\site-packages\tensorflow\python\eager\function.py in _maybe_define_function(self, args, kwargs)
3359
3360 self._function_cache.missed.add(call_context_key)
-> 3361 graph_function = self._create_graph_function(args, kwargs)
3362 self._function_cache.primary[cache_key] = graph_function
3363
C:\Anaconda\lib\site-packages\tensorflow\python\eager\function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3194 arg_names = base_arg_names + missing_arg_names
3195 graph_function = ConcreteFunction(
-> 3196 func_graph_module.func_graph_from_py_func(
3197 self._name,
3198 self._python_function,
C:\Anaconda\lib\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)
988 _, original_func = tf_decorator.unwrap(python_func)
989
--> 990 func_outputs = python_func(*func_args, **func_kwargs)
991
992 # invariant: `func_outputs` contains only Tensors, CompositeTensors,
C:\Anaconda\lib\site-packages\tensorflow\python\eager\def_function.py in wrapped_fn(*args, **kwds)
632 xla_context.Exit()
633 else:
--> 634 out = weak_wrapped_fn().__wrapped__(*args, **kwds)
635 return out
636
C:\Anaconda\lib\site-packages\tensorflow\python\framework\func_graph.py in wrapper(*args, **kwargs)
975 except Exception as e: # pylint:disable=broad-except
976 if hasattr(e, "ag_error_metadata"):
--> 977 raise e.ag_error_metadata.to_exception(e)
978 else:
979 raise
ValueError: in user code:
C:\Anaconda\lib\site-packages\tensorflow\python\keras\engine\training.py:805 train_function *
return step_function(self, iterator)
C:\Anaconda\lib\site-packages\tensorflow\python\keras\engine\training.py:795 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
C:\Anaconda\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:1259 run
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
C:\Anaconda\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2730 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
C:\Anaconda\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:3417 _call_for_each_replica
return fn(*args, **kwargs)
C:\Anaconda\lib\site-packages\tensorflow\python\keras\engine\training.py:788 run_step **
outputs = model.train_step(data)
C:\Anaconda\lib\site-packages\tensorflow\python\keras\engine\training.py:754 train_step
y_pred = self(x, training=True)
C:\Anaconda\lib\site-packages\tensorflow\python\keras\engine\base_layer.py:998 __call__
input_spec.assert_input_compatibility(self.input_spec, inputs, self.name)
C:\Anaconda\lib\site-packages\tensorflow\python\keras\engine\input_spec.py:271 assert_input_compatibility
raise ValueError('Input ' + str(input_index) +
ValueError: Input 0 is incompatible with layer model_7: expected shape=(None, 784), found shape=(32, 28, 28)
What's the meaning of this error, do I need to change the input dimension. If yes then what will be my input dimension?
Thnaks in advance.
Thnaks in advance.
