“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

Answer

This error indicates that, you have defined an activation function that is not interpretable. In your definition of a dense layer you have passed two argument 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])