minimpl.py

import keras

def mlp(n_features, n_classes, first, hidden, activation):
    from keras.layers import Dense

    shape = (n_features, 1)

    model = keras.Sequential()
    model.add(Dense(first, input_shape=shape))
    for neurons in hidden:
        model.add(Dense(neurons, activation=activation))

    out_activation = 'sigmoid' if n_classes == 2 else 'softmax'
    out_units = 1 if n_classes == 2 else n_classes
    model.add(Dense(out_units, activation=out_activation))

    return model


def main():
    hidden_neurons = [ 650//30 ] * 14 
    m = mlp(50, n_classes=2, first=650//20, hidden=hidden_neurons, activation='tanh')
    m.summary()

if __name__ == '__main__':
    main()

output.txt

$ python temp/minimlp.py 
Using TensorFlow backend.
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_1 (Dense)              (None, 50, 32)            64        
_________________________________________________________________
dense_2 (Dense)              (None, 50, 21)            693       
_________________________________________________________________
dense_3 (Dense)              (None, 50, 21)            462       
_________________________________________________________________
dense_4 (Dense)              (None, 50, 21)            462       
_________________________________________________________________
dense_5 (Dense)              (None, 50, 21)            462       
_________________________________________________________________
dense_6 (Dense)              (None, 50, 21)            462       
_________________________________________________________________
dense_7 (Dense)              (None, 50, 21)            462       
_________________________________________________________________
dense_8 (Dense)              (None, 50, 21)            462       
_________________________________________________________________
dense_9 (Dense)              (None, 50, 21)            462       
_________________________________________________________________
dense_10 (Dense)             (None, 50, 21)            462       
_________________________________________________________________
dense_11 (Dense)             (None, 50, 21)            462       
_________________________________________________________________
dense_12 (Dense)             (None, 50, 21)            462       
_________________________________________________________________
dense_13 (Dense)             (None, 50, 21)            462       
_________________________________________________________________
dense_14 (Dense)             (None, 50, 21)            462       
_________________________________________________________________
dense_15 (Dense)             (None, 50, 21)            462       
_________________________________________________________________
dense_16 (Dense)             (None, 50, 1)             22        
=================================================================
Total params: 6,785
Trainable params: 6,785
Non-trainable params: 0
_________________________________________________________________