"Could not interpret activation function identifier: 256" error in Keras

I'm trying to run the following code but I got an error. Did I miss something in the codes?

from keras.layers.core import Dense, Activation, Dropout
from keras.layers.recurrent import LSTM
from keras.models import Sequential
from keras.callbacks import ModelCheckpoint
from keras.models import load_model
from keras.optimizers import Adam
from keras.regularizers import l2
from keras.activations import relu, elu, linear, sigmoid

def build_fc_model(layers):
    fc_model = Sequential()
    for i in range(len(layers)-1):
        fc_model.add( Dense(layers[i],layers[i+1]) )#, W_regularizer=l2(0.1)) )
        fc_model.add( Dropout(0.5) )
        if i < (len(layers) - 2):
            fc_model.add( Activation('relu') )
    fc_model.summary()
    return fc_model
fc_model_1 = build_fc_model([2, 256, 512, 1024, 1])

and here is the error message:

TypeError: Could not interpret activation function identifier: 256

The second argument in the Dense layer is considered as activation function, not the number of neurons. You have passed the number of neurons as an activation function to the dense layer object.

In your definition of a dense layer you have passed two arguments as layers[i] and layers[i+1].

Based on the docs here for the Dense function: The first argument is number of units (neurons) and the second parameter is activation function. So, it considers layers[i+1] as an activation function that could not be recognized by the Dense function.

Inference: You do not need to pass next layer neurons to your dense layer. So remove layers[i+1] argument.

Furthermore, you have to define an input layer for your model and pass the input shape to it for your model.

Therefore, modified code should be like this:

from keras.layers.core import Dense, Activation, Dropout
from keras.layers.recurrent import LSTM
from keras.models import Sequential
from keras.callbacks import ModelCheckpoint
from keras.models import load_model
from keras.optimizers import Adam
from keras.regularizers import l2
from keras.activations import relu, elu, linear, sigmoid
from keras.layers import InputLayer  #import input layer 

def build_fc_model(layers):
    fc_model = Sequential()
    fc_model.add(InputLayer(input_shape=(784,))) #add input layer and specify it's shape
    for i in range(len(layers)-1):
        fc_model.add( Dense(layers[i]) ) #remove unnecessary second argument 
        if i < (len(layers) - 2):
            fc_model.add( Activation('relu') )
        fc_model.add( Dropout(0.5) )
    fc_model.summary()
    return fc_model
fc_model_1 = build_fc_model([2, 256, 512, 1024, 1])