title: model.fit(X_train, y_train, epochs=5, validation_data=(X_test, y_test) isn't working
body: <p>I'm trying to write a simple neural network to classify dog breeds. Here is the link to the dataset: <a href="https://www.kaggle.com/c/dog-breed-identification" rel="nofollow noreferrer">https://www.kaggle.com/c/dog-breed-identification</a></p>
  <p>This is my code:</p>
  <pre><code>import os
  import pandas as pd
  import numpy
  from keras.layers import Dense, Conv2D, MaxPooling2D, Flatten, Dropout, BatchNormalization
  from keras.models import Sequential
  from keras.optimizers import Adam
  from sklearn.model_selection import train_test_split
  
  dogs = pd.read_csv('C:/Users/Natha/Downloads/dog-breed-identification/labels.csv')
  
  ImageHeight = 128
  ImageWidth = 128
  Depth = 3
  
  from PIL import Image
  import os, sys
  
  path = ('C:/Users/Natha/Downloads/dog-breed-identification/train')
  def resize():
      for item in os.listdir(path):
          if os.path.isfile(item):
              im = Image.open(item)
              f, e = os.path.splitext(item)
              imResize = im.resize((ImageHeight,ImageWidth), Image.ANTIALIAS)
              imResize.save(f + ' resized.jpg', 'JPEG', quality=90)
              return imResize
  resize()
  
  X = dogs['id']
  y = dogs['breed']
  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
  
  model = Sequential()
  model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(ImageHeight, ImageWidth, Depth)))
  model.add(BatchNormalization())
  model.add(MaxPooling2D(pool_size=(2, 2)))
  model.add(Dropout(0.25))
  
  model.add(Conv2D(64, (3, 3), activation='relu'))
  model.add(BatchNormalization())
  model.add(MaxPooling2D(pool_size=(2, 2)))
  model.add(Dropout(0.25))
  
  model.add(Conv2D(128, (3, 3), activation='relu'))
  model.add(BatchNormalization())
  model.add(MaxPooling2D(pool_size=(2, 2)))
  model.add(Dropout(0.25))
  
  model.add(Flatten())
  model.add(Dense(256, activation='relu'))
  model.add(Dense(2, activation='sigmoid'))
  model.compile(optimizer=Adam(learning_rate=0.001), loss='categorical_crossentropy', metrics=['accuracy'])
  
  history = model.fit(X_train, y_train, epochs=10, validation_data=(X_test, y_test))
  </code></pre>
  <hr />
  <pre><code>ValueError                                Traceback (most recent call last)
  &lt;ipython-input-83-bab577512e44&gt; in &lt;module&gt;
  ----&gt; 1 model.fit(X_train, y_train, epochs=10, validation_data=(X_test, y_test))
  
  ~\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)
     1098                 _r=1):
     1099               callbacks.on_train_batch_begin(step)
  -&gt; 1100               tmp_logs = self.train_function(iterator)
     1101               if data_handler.should_sync:
     1102                 context.async_wait()
  
  ~\anaconda3\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:
  --&gt; 828       result = self._call(*args, **kwds)
      829       compiler = &quot;xla&quot; if self._experimental_compile else &quot;nonXla&quot;
      830       new_tracing_count = self.experimental_get_tracing_count()
  
  ~\anaconda3\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 = []
  --&gt; 871       self._initialize(args, kwds, add_initializers_to=initializers)
      872     finally:
      873       # At this point we know that the initialization is complete (or less
  
  ~\anaconda3\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 = (
  --&gt; 725         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access
      726             *args, **kwds))
      727 
  
  ~\anaconda3\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:
  -&gt; 2969       graph_function, _ = self._maybe_define_function(args, kwargs)
     2970     return graph_function
     2971 
  
  ~\anaconda3\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)
  -&gt; 3361           graph_function = self._create_graph_function(args, kwargs)
     3362           self._function_cache.primary[cache_key] = graph_function
     3363 
  
  ~\anaconda3\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(
  -&gt; 3196         func_graph_module.func_graph_from_py_func(
     3197             self._name,
     3198             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)
      988         _, original_func = tf_decorator.unwrap(python_func)
      989 
  --&gt; 990       func_outputs = python_func(*func_args, **func_kwargs)
      991 
      992       # invariant: `func_outputs` contains only Tensors, CompositeTensors,
  
  ~\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py in wrapped_fn(*args, **kwds)
      632             xla_context.Exit()
      633         else:
  --&gt; 634           out = weak_wrapped_fn().__wrapped__(*args, **kwds)
      635         return out
      636 
  
  ~\anaconda3\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, &quot;ag_error_metadata&quot;):
  --&gt; 977               raise e.ag_error_metadata.to_exception(e)
      978             else:
      979               raise
  
  ValueError: in user code:
  
      C:\Users\Natha\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:805 train_function  *
          return step_function(self, iterator)
      C:\Users\Natha\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:795 step_function  **
          outputs = model.distribute_strategy.run(run_step, args=(data,))
      C:\Users\Natha\anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:1259 run
          return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
      C:\Users\Natha\anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2730 call_for_each_replica
          return self._call_for_each_replica(fn, args, kwargs)
      C:\Users\Natha\anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:3417 _call_for_each_replica
          return fn(*args, **kwargs)
      C:\Users\Natha\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:788 run_step  **
          outputs = model.train_step(data)
      C:\Users\Natha\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:754 train_step
          y_pred = self(x, training=True)
      C:\Users\Natha\anaconda3\lib\site-packages\tensorflow\python\keras\engine\base_layer.py:998 __call__
          input_spec.assert_input_compatibility(self.input_spec, inputs, self.name)
      C:\Users\Natha\anaconda3\lib\site-packages\tensorflow\python\keras\engine\input_spec.py:234 assert_input_compatibility
          raise ValueError('Input ' + str(input_index) + ' of layer ' +
  
      ValueError: Input 0 of layer sequential_7 is incompatible with the layer: : expected min_ndim=4, found ndim=2. Full shape received: (None, 1)
  </code></pre>
  <p>These are the versions of the modules:</p>
  <p>Keras: 2.4.2</p>
  <p>Tensorflow: 2.4.0</p>
  <p>Pandas: 1.1.3</p>
  <p>I'm new at neural networks and whenever I try to do a project, this shows up when I try to train the model. If anyone knows how to fix this, please answer this. Thank you in advance.</p>