title: Keras,models.add() missing 1 required positional argument: 'layer'
body: <p>I'm classifying digits of the MNIST dataset using a simple feed forward neural net with Keras. So I execute the code below.</p>
  
  <pre><code>import os
  import tensorflow as tf
  
  import keras
  from keras.models import Sequential
  from keras.layers import Dense, Activation
  
  from tensorflow.examples.tutorials.mnist import input_data
  
  mnist = input_data.read_data_sets('/tmp/data', one_hot=True)
  
  # Path to Computation graphs
  LOGDIR = './graphs_3'
  
  # start session
  sess = tf.Session()
  
  #Hyperparameters
  LEARNING_RATE = 0.01
  BATCH_SIZE = 1000
  EPOCHS = 10
  
  # Layers
  HL_1 = 1000
  HL_2 = 500
  
  # Other Parameters
  INPUT_SIZE = 28*28
  N_CLASSES = 10
  
  model = Sequential
  model.add(Dense(HL_1, input_dim=(INPUT_SIZE,), activation="relu"))
  #model.add(Activation(activation="relu"))
  model.add(Dense(HL_2, activation="relu"))
  #model.add(Activation("relu"))
  model.add(Dropout(rate=0.9))
  model.add(Dense(N_CLASSES, activation="softmax"))
  
  model.compile(
      optimizer="Adam",
      loss="categorical_crossentropy",
      metrics=['accuracy'])
  
  
  
  # one_hot_labels = keras.utils.to_categorical(labels, num_classes=10)
  
  model.fit(
      x=mnist.train.images, 
      y=mnist.train.labels, 
      epochs=EPOCHS, 
      batch_size=BATCH_SIZE)
  
  
  score = model.evaluate(
      x=mnist.test.images,
      y=mnist.test.labels)
  
  print("score = ", score)
  </code></pre>
  
  <p>However, I get the following error:</p>
  
  <pre><code>model.add(Dense(1000, input_dim=(INPUT_SIZE,), activation="relu"))
     TypeError: add() missing 1 required positional argument: 'layer'
  </code></pre>
  
  <p>The syntax is exactly as shown in the keras docs. I am using keras 2.0.9, so I don't think it's a version control problem. Did I do something wrong?</p>