gistfile1.txt

def get_reward(self, action, step, pre_acc):
        action = [action[0][0][x:x+4] for x in range(0, len(action[0][0]), 4)]
        cnn_drop_rate = [c[3] for c in action]
Then we formed bathc with hyperparameters for every layer in "action" and we created cnn_drop_rate – list of dropout rates for every layer. 
Now let's create new CNN with new architecture: 
        with tf.Graph().as_default() as g:
            with g.container('experiment'+str(step)):
                model = CNN(self.num_input, self.num_classes, action)
                loss_op = tf.reduce_mean(model.loss)
                optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate)
                train_op = optimizer.minimize(loss_op)