python之Tensorflow:检查失败:NDIMS == new_sizes.size()(2 对 1)

我对 tensorflow 完全陌生。我正在做一个项目，我收到一条错误消息:2018-05-13 20:50:57.669722: F T:\src\github\tensorflow\tensorflow/core/framework/tensor.h:630] Check failed: NDIMS = = new_sizes.size()(2 对 1) Pycharm 说:进程已完成，退出代码为 -1073740791 (0xC0000409)

我不知道那是什么意思。我正在运行 Windows 和 Python 3.6。

这是我的代码:

import tensorflow as tf 
import gym 
import numpy as np 
 
env = gym.make("MountainCar-v0").env 
 
n_inputs = 2 
n_hidden = 3 
n_output = 3 
 
initializer = tf.contrib.layers.variance_scaling_initializer() 
 
learning_rate = 0.1 
 
X = tf.placeholder(tf.float32, shape=[None,n_inputs]) 
 
hidden = tf.layers.dense(X,n_hidden,activation=tf.nn.elu,kernel_initializer=initializer) 
logits = tf.layers.dense(hidden,n_output,kernel_initializer=initializer) 
outputs = tf.nn.softmax(logits) 
 
index,action = tf.nn.top_k(logits,1) 
y = tf.to_float(action) 
cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(labels=y,logits=logits) 
optimizer = tf.train.AdamOptimizer(learning_rate) 
 
grads_and_vars = optimizer.compute_gradients(cross_entropy) 
gradients = [grad for grad, variable in grads_and_vars] 
gradient_placeholders = [] 
grads_and_vars_feed = [] 
for grad, variable in grads_and_vars: 
    gradient_placeholder = tf.placeholder(tf.float32, shape=grad.get_shape()) 
    gradient_placeholders.append(gradient_placeholder) 
    grads_and_vars_feed.append((gradient_placeholder,variable)) 
training_op = optimizer.apply_gradients(grads_and_vars_feed) 
 
#Variablen und Speicher initialisieren 
init = tf.global_variables_initializer() 
saver = tf.train.Saver() 
 
#Belohnung der versch. Schritte abziehen 
def discount_rewards(rewards, discount_rate): 
    discounted_rewards = np.empty(len(rewards)) 
    comulative_rewards = 0 
    for step in reversed(range(len(rewards))): 
        comulative_rewards = rewards[step] + comulative_rewards * discount_rate 
        discounted_rewards[step] = comulative_rewards 
    return discounted_rewards 
 
 
def discount_and_normalize_rewards(all_rewards, discount_rate): 
    all_discounted_rewards = [discount_rewards(rewards, discount_rate) for rewards in all_rewards] 
    #Zusammenfügen aller rewards zu einem array 
    flat_rewards = np.concatenate(all_discounted_rewards) 
    reward_mean = flat_rewards.mean() 
    reward_std = flat_rewards.std() 
    return [(discount_rewards - reward_mean)/reward_std for discount_rewards in all_discounted_rewards] 
 
n_iterations = 25 
n_max_steps = 10000 
n_games_per_update = 10 
save_iteration = 10 
discount_rate = 0.95 
 
with tf.Session() as sess: 
    init.run() 
    for iteration in range(n_iterations): 
        all_rewards = [] 
        my_rewards = [] 
        all_gradients = [] 
 
        for game in range(n_games_per_update): 
            current_rewards = [] 
            current_gradients = [] 
            #env.render() 
            obs = env.reset() 
            for step in range(n_max_steps): 
                action_val,gradient_val = sess.run([action,gradients], feed_dict={X: obs.reshape(1, n_inputs)}) 
                obs, reward, done, info = env.step(action_val) 
                current_rewards.append(reward) 
                current_gradients.append(gradient_val) 
                if done: 
                    break 
            my_rewards.append(sum(current_rewards)) 
            print(iteration,": ", sum(current_rewards)) 
            all_rewards.append(current_rewards) 
            all_gradients.append(current_gradients) 
        all_rewards = discount_and_normalize_rewards(all_rewards,discount_rate) 
        feed_dict = {} 
        for var_index, grad_placeholder in enumerate(gradient_placeholders): 
            mean_gradients = np.mean([reward * all_gradients[game_index][step][var_index] for game_index,rewards in enumerate(all_rewards) for step,reward in enumerate(rewards)],axis=0) 
            feed_dict[grad_placeholder] = mean_gradients 
        sess.run(training_op, feed_dict=feed_dict) 
        if iteration % save_iteration == 0: 
            saver.save(sess, "./my_policy_net_pg.ckpt") 
 
    print("Average: ", sum(my_rewards) / len(my_rewards)) 
    print("Maximum: ", max(my_rewards)) 

请您参考如下方法:

这些行似乎包含多个错误:

index, action = tf.nn.top_k( logits, 1 ) 
y = tf.to_float( action ) 
cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2( labels = y, logits = logits ) 

首先，tf.nn.top_k()首先返回值，然后返回索引。因此，action 将保存索引，而不是 index。 y 然后成为索引( float )，您将它作为 labels 传递给 tf.nn.softmax_cross_entropy_with_logits_v2() .

这有两个主要问题。首先，您应该将标签作为单热向量而不是索引传递。我怀疑这就是您收到错误的原因，您传递的是 1 维张量而不是 2 维张量。

第二个问题是理论上的(与你的错误无关，但我想指出来):因为 logits 是你的预测，你从那里得到 y ，您基本上是在将 logits 与自身进行比较。不会有学习。您需要提供实际标签并以此为基础进行学习。

只是一个评论，发布整个错误回溯通常是有益的，而不仅仅是最后一行，因为我现在只是猜测错误在哪里，不能确定。