title: Error when checking target: expected dense_2 to have 2 dimensions, but got array with shape (1, 1226, 2)
body: <p>Here is the code that I am trying to run: </p>
  
  <pre><code>y = Df[['label']]
  
  y_train = np.column_stack((y_train['label']))
  
  y_test = np.column_stack((y_test['label']))
  
  data_dim = 18
  timesteps = 1
  num_classes = 2
  
  model = Sequential()
  
  model.add(LSTM(19, return_sequences=True,
                 input_shape=(timesteps, data_dim)))  
  model.add(LSTM(19, return_sequences=True))  
  model.add(LSTM(19))  # return a single vector of dimension 30
  model.add(Dense(1, activation='softmax'))
  
  model.compile(loss='binary_crossentropy',
                optimizer='rmsprop',
                metrics=['accuracy'])
  
  model.summary()
  
  model.fit(X_train, y_train, batch_size = 400, epochs = 20, verbose = 
  
  accuracy_train = accuracy_score(y_train, model.predict(X_train))
  accuracy_test = accuracy_score(y_test, model.predict(X_test))
  print('\nTrain Accuracy:{: .2f}%'.format(accuracy_train*100))
  print('Test Accuracy:{: .2f}%'.format(accuracy_test*100))
  </code></pre>
  
  <p>I am getting the following error: </p>
  
  <pre><code>Error when checking target: expected dense_1 to have shape (1,) but got array with shape (1226,)
  </code></pre>
  
  <p>Model Summary:</p>
  
  <pre><code>_________________________________________________________________
  Layer (type)                 Output Shape              Param #   
  =================================================================
  lstm_37 (LSTM)               (None, 1, 19)             2888      
  _________________________________________________________________
  lstm_38 (LSTM)               (None, 1, 19)             2964      
  _________________________________________________________________
  lstm_39 (LSTM)               (None, 19)                2964      
  _________________________________________________________________
  dense_13 (Dense)             (None, 1)                 20        
  =================================================================
  Total params: 8,836
  Trainable params: 8,836
  Non-trainable params: 0
  </code></pre>
  
  <p><code>X_train</code>: </p>
  
  <pre><code>[[[-0.02444971  0.02444971  1.          1.          1.
      1.         -1.          2.          3.          4.
      3.          1.          0.          0.06938705 -0.04329106
      0.02458854  0.06025883  0.01439807]]]
  [[[ 0.00733477  0.02033006 -1.          1.          1.
      1.         -1.          0.          1.          2.
      1.          0.          0.          0.03837079 -0.00829683
     -0.00734757  0.00985466 -0.04543226]]]
  </code></pre>
  
  <p><code>y_train</code>: </p>
  
  <pre><code>[[ 1  1 -1 ...  1  1 -1]]
  </code></pre>