code: def fprop(self, x):
      
  
      # Feed forward through the network layers
      for layer_name in self.layer_names:
        if layer_name == 'input':
          prev_layer_act = x
          continue
        else:
          self.layer_acts[layer_name] = self.layers[layer_name](
              prev_layer_act)
          prev_layer_act = self.layer_acts[layer_name]
  
      # Adding softmax values to list of activations.
      self.layer_acts['probs'] = tf.nn.softmax(
          logits=self.layer_acts['logits'])
      return self.layer_acts
docs: Forward propagation throught the network
      :return: dictionary with layer names mapping to activation values.