title: 'ValueError: Input 0 of layer sequential is incompatible with the layer: expected axis -1 of input shape to have value 3 but received input
body: <p>I am getting an error when I'm training my cnn on an array of skimage.feature.hog images. The data set I'm using has 3 RGB values and these have 1 value instead. Which is effectively a subset of the dataset I am using. I suspect the issue arises from me using the same code from my original cnn on the 'full' dataset, or something to do with the dimensions? I cannot figure it out even though there are similar threads, I apologize if this is simplistic question. I believe I have specified the dimensions, of the new hog training and testing subsets, correctly.
  Any help would be appreciated.
  The error is : ValueError: Input 0 of layer sequential is incompatible with the layer: expected axis -1 of input shape to have value 3 but received input with shape [None, 32, 32, 1]</p>
  <pre><code>hog_trn= np.zeros(10240000).reshape(10000,32,32,1)
  hog_tst = np.zeros(1024000).reshape(1000,32,32,1)
  for i in range(10000):
      hog_feature, hog_image = skimage.feature.hog(trn_images[i,:,:,:], pixels_per_cell=[2,2], cells_per_block=[3,3], visualize=True)
      hog_trn[i,:,:,0] = hog_image
  for i in range(1000):
      hog_feature, hog_image = skimage.feature.hog(tst_images[i,:,:,:], pixels_per_cell=[2,2], cells_per_block=[3,3], visualize=True)
      hog_tst[i,:,:,0] = hog_image
  
  model_hog = models.Sequential()
  model_hog.add(layers.Conv2D(32,(3,3),activation = 'relu',input_shape=(32,32,1)))
  model_hog.add(layers.MaxPool2D((2,2)))
  model_hog.add(layers.Conv2D(64, (3, 3), activation='relu'))
  model_hog.add(layers.MaxPool2D((2, 2)))
  model_hog.add(layers.Conv2D(64, (3, 3), activation='relu'))
  model_hog.add(layers.Flatten())
  model_hog.add(layers.Dense(64, activation='relu'))
  model_hog.add(layers.Dense(10))
  model_hog.summary()
  
  model_hog.compile(optimizer='Adam',
                loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
                metrics=['accuracy'])
  
  history_hog = model.fit(hog_trn, trn_labels, epochs=10, 
                      validation_data=(hog_tst, tst_labels))
  plt.plot(history_hog.history['accuracy'], label='accuracy')
  plt.plot(history_hog.history['val_accuracy'], label = 'val_accuracy')
  plt.xlabel('Epoch')
  plt.ylabel('Accuracy')
  plt.legend(loc='lower right')
  
  test_loss, test_acc = model.evaluate(hog_tst,  tst_labels, verbose=2)
  </code></pre>
  <p>And the error:</p>
  <pre><code>---------------------------------------------------------------------------
  ValueError                                Traceback (most recent call last)
  &lt;ipython-input-24-b829133dd6d6&gt; in &lt;module&gt;
       14               metrics=['accuracy'])
       15 
  ---&gt; 16 history_hog = model.fit(hog_trn, trn_labels, epochs=10, 
       17                     validation_data=(hog_tst, tst_labels))
       18 plt.plot(history_hog.history['accuracy'], label='accuracy')
  
  ~\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py in _method_wrapper(self, *args, **kwargs)
      106   def _method_wrapper(self, *args, **kwargs):
      107     if not self._in_multi_worker_mode():  # pylint: disable=protected-access
  --&gt; 108       return method(self, *args, **kwargs)
      109 
      110     # Running inside `run_distribute_coordinator` already.
  
  ~\anaconda3\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)
     1096                 batch_size=batch_size):
     1097               callbacks.on_train_batch_begin(step)
  -&gt; 1098               tmp_logs = train_function(iterator)
     1099               if data_handler.should_sync:
     1100                 context.async_wait()
  
  ~\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py in __call__(self, *args, **kwds)
      778       else:
      779         compiler = &quot;nonXla&quot;
  --&gt; 780         result = self._call(*args, **kwds)
      781 
      782       new_tracing_count = self._get_tracing_count()
  
  ~\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py in _call(self, *args, **kwds)
      805       # In this case we have created variables on the first call, so we run the
      806       # defunned version which is guaranteed to never create variables.
  --&gt; 807       return self._stateless_fn(*args, **kwds)  # pylint: disable=not-callable
      808     elif self._stateful_fn is not None:
      809       # Release the lock early so that multiple threads can perform the call
  
  ~\anaconda3\lib\site-packages\tensorflow\python\eager\function.py in __call__(self, *args, **kwargs)
     2826     &quot;&quot;&quot;Calls a graph function specialized to the inputs.&quot;&quot;&quot;
     2827     with self._lock:
  -&gt; 2828       graph_function, args, kwargs = self._maybe_define_function(args, kwargs)
     2829     return graph_function._filtered_call(args, kwargs)  # pylint: disable=protected-access
     2830 
  
  ~\anaconda3\lib\site-packages\tensorflow\python\eager\function.py in _maybe_define_function(self, args, kwargs)
     3208           and self.input_signature is None
     3209           and call_context_key in self._function_cache.missed):
  -&gt; 3210         return self._define_function_with_shape_relaxation(args, kwargs)
     3211 
     3212       self._function_cache.missed.add(call_context_key)
  
  ~\anaconda3\lib\site-packages\tensorflow\python\eager\function.py in _define_function_with_shape_relaxation(self, args, kwargs)
     3139           expand_composites=True)
     3140 
  -&gt; 3141     graph_function = self._create_graph_function(
     3142         args, kwargs, override_flat_arg_shapes=relaxed_arg_shapes)
     3143     self._function_cache.arg_relaxed[rank_only_cache_key] = graph_function
  
  ~\anaconda3\lib\site-packages\tensorflow\python\eager\function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
     3063     arg_names = base_arg_names + missing_arg_names
     3064     graph_function = ConcreteFunction(
  -&gt; 3065         func_graph_module.func_graph_from_py_func(
     3066             self._name,
     3067             self._python_function,
  
  ~\anaconda3\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)
      984         _, original_func = tf_decorator.unwrap(python_func)
      985 
  --&gt; 986       func_outputs = python_func(*func_args, **func_kwargs)
      987 
      988       # invariant: `func_outputs` contains only Tensors, CompositeTensors,
  
  ~\anaconda3\lib\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.
  --&gt; 600         return weak_wrapped_fn().__wrapped__(*args, **kwds)
      601     weak_wrapped_fn = weakref.ref(wrapped_fn)
      602 
  
  ~\anaconda3\lib\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, &quot;ag_error_metadata&quot;):
  --&gt; 973               raise e.ag_error_metadata.to_exception(e)
      974             else:
      975               raise
  
  ValueError: in user code:
  
      C:\Users\User\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:806 train_function  *
          return step_function(self, iterator)
      C:\Users\User\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:796 step_function  **
          outputs = model.distribute_strategy.run(run_step, args=(data,))
      C:\Users\User\anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:1211 run
          return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
      C:\Users\User\anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2585 call_for_each_replica
          return self._call_for_each_replica(fn, args, kwargs)
      C:\Users\User\anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2945 _call_for_each_replica
          return fn(*args, **kwargs)
      C:\Users\User\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:789 run_step  **
          outputs = model.train_step(data)
      C:\Users\User\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:747 train_step
          y_pred = self(x, training=True)
      C:\Users\User\anaconda3\lib\site-packages\tensorflow\python\keras\engine\base_layer.py:975 __call__
          input_spec.assert_input_compatibility(self.input_spec, inputs,
      C:\Users\User\anaconda3\lib\site-packages\tensorflow\python\keras\engine\input_spec.py:212 assert_input_compatibility
          raise ValueError(
  
      ValueError: Input 0 of layer sequential is incompatible with the layer: expected axis -1 of input shape to have value 3 but received input with shape [None, 32, 32, 1]
  </code></pre>