title: TypeError: __init__() missing 1 required positional argument: 'units'
body: <p>I am working in python and tensor flow but I miss 'units' argument and I do not know how to solve it, It looks like your post is mostly code; please add some more details.It looks like your post is mostly code; please add some more details.</p>
  
  <p>here the code</p>
  
  <pre class="lang-py prettyprint-override"><code>def createModel():
      model = Sequential()
      # first set of CONV =&gt; RELU =&gt; MAX POOL layers
      model.add(Conv2D(32, (3, 3), padding='same', activation='relu', input_shape=inputShape))
      model.add(Conv2D(32, (3, 3), activation='relu'))
      model.add(MaxPooling2D(pool_size=(2, 2)))
      model.add(Dropout(0.25))
  
      model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))
      model.add(Conv2D(64, (3, 3), activation='relu'))
      model.add(MaxPooling2D(pool_size=(2, 2)))
      model.add(Dropout(0.25)) 
      model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))
      model.add(Conv2D(64, (3, 3), activation='relu'))
      model.add(MaxPooling2D(pool_size=(2, 2)))
      model.add(Dropout(0.25))
  
      model.add(Flatten())
      model.add(Dense(512, activation='relu'))
      model.add(Dropout(0.5))
      model.add(Dense(output_dim=NUM_CLASSES, activation='softmax'))
      # returns our fully constructed deep learning + Keras image classifier 
      opt = Adam(lr=INIT_LR, decay=INIT_LR / EPOCHS)
      # use binary_crossentropy if there are two classes
      model.compile(loss="categorical_crossentropy", optimizer=opt, metrics=["accuracy"])
      return model
  
  print("Reshaping trainX at..."+ str(datetime.now()))
  #print(trainX.sample()) 
  print(type(trainX)) # &lt;class 'pandas.core.series.Series'&gt;
  print(trainX.shape) # (750,)
  from numpy import zeros
  Xtrain = np.zeros([trainX.shape[0],HEIGHT, WIDTH, DEPTH])
  for i in range(trainX.shape[0]): # 0 to traindf Size -1
      Xtrain[i] = trainX[i]
  print(Xtrain.shape) # (750,128,128,3)
  print("Reshaped trainX at..."+ str(datetime.now()))
  
  print("Reshaping valX at..."+ str(datetime.now()))
  print(type(valX)) # &lt;class 'pandas.core.series.Series'&gt;
  print(valX.shape) # (250,)
  from numpy import zeros
  Xval = np.zeros([valX.shape[0],HEIGHT, WIDTH, DEPTH])
  for i in range(valX.shape[0]): # 0 to traindf Size -1
      Xval[i] = valX[i]
  print(Xval.shape) # (250,128,128,3)
  print("Reshaped valX at..."+ str(datetime.now()))
  
  # initialize the model
  print("compiling model...")
  sys.stdout.flush()
  model = createModel()
  
  # print the summary of model
  from keras.utils import print_summary
  print_summary(model, line_length=None, positions=None, print_fn=None)
  
  # add some visualization
  from IPython.display import SVG
  from keras.utils.vis_utils import model_to_dot
  SVG(model_to_dot(model).create(prog='dot', format='svg'))
  
  </code></pre>