gistfile1.txt

def train(mnist, max_layers):
    sess = tf.Session()
    global_step = tf.Variable(0, trainable=False)
    starter_learning_rate = 0.1
    learning_rate = tf.train.exponential_decay(0.99, global_step,
                                           500, 0.96, staircase=True)

    optimizer = tf.train.RMSPropOptimizer(learning_rate=learning_rate)

    reinforce = Reinforce(sess, optimizer, policy_network, args.max_layers, global_step)

    net_manager = NetManager(num_input=784,
                             num_classes=10,
                             learning_rate=0.001,
                             mnist=mnist)

    MAX_EPISODES = 250
    step = 0
    state = np.array( [[10.0, 128.0, 1.0, 1.0]*max_layers], dtype=np.float32)
    pre_acc = 0.0
    for i_episode in range(MAX_EPISODES):
  
            action = reinforce.get_action(state)
            print("current action:", action)
            if all(ai > 0 for ai in action[0][0]):
                reward, pre_acc = net_manager.get_reward(action, step, pre_acc)
            else:
                reward = -1.0

            # In our sample action is equal state
            state = action[0]
            reinforce.store_rollout(state, reward)

            step += 1
        ls = reinforce.train_step(MAX_STEPS)
        log_str = "current time:  "+str(datetime.datetime.now().time())+" episode:  "+str(i_episode)+" loss:  "+str(ls)+" last_state:  "+str(state)+" last_reward:  "+str(reward)
        print(log_str)

def main():
    max_layers = 3
    mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
    train(mnist, max_layers)

if __name__ == '__main__':
  main()