feed forward builder function

feedforwardbuilder.py

def feed_forward_builder(config):
    input_layer = Input(shape=(config['layers']['input dim'],1), dtype = config['layers']['dtype'], name = 'InputLayer')
    if config['layers']['number'] == 0:
        print('No Hidden Layers')
    else:
        layer = Dense(config['layers']['dims'][0], 
                      dtype = config['layers']['dtype'], 
                      name = config['layers']['names'][0], 
                      activation = config['layers']['activations'][0], 
                      kernel_initializer = config['layers']['initializers'][0], 
                      bias_initializer = config['layers']['bias initializers'][0], 
                      kernel_regularizer = config['layers']['kernel regulizers'][0])(input_layer)
        for n in range(1,config['layers']['number'],1):
            layer =   Dense(config['layers']['dims'][n], 
                      dtype = config['layers']['dtype'], 
                      name = config['layers']['names'][n], 
                      activation = config['layers']['activations'][n], 
                      kernel_initializer = config['layers']['initializers'][n], 
                      bias_initializer = config['layers']['bias initializers'][n], 
                      kernel_regularizer = config['layers']['kernel regulizers'][n])(layer)
    output_layer = Dense(config['layers']['output dim'], 
                         dtype =  config['layers']['dtype'], 
                         name = 'OutputLayer', 
                         activation = config['layers']['output activation'], 
                         kernel_initializer = config['layers']['ouput initializer'], 
                         bias_initializer = config['layers']['output bias initializer'], 
                         kernel_regularizer = config['layers']['output regulizer'])(layer)
    fnn = Model(inputs = input_layer, outputs = output_layer)
    fnn.compile(optimizer = config['compile']['optimizer'], 
                metrics = config['compile']['metrics'], 
                loss = config['compile']['loss'])
    return fnn