Python Tensorflow:检查失败：NDIMS==新的大小。大小（）（2对1）

我对tensorflow完全陌生。我在做一个项目，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]检查失败：NDIMS==new_size.size（）（2对1） Pycharm说：进程结束，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

这是我的密码：

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))

---

这些行似乎包含多个bug：

index，action=tf.nn.top\k（logits，1）
y=tf.to_浮动（动作）
交叉熵=tf.nn.softmax\u交叉熵\u与逻辑向量v2（标签=y，逻辑向量=logits）

首先，首先返回值，然后返回索引。因此，

操作

将保存索引，而不是

索引

y

然后成为索引（以浮点形式），您将其作为

labels

传递给

这有两个主要问题。首先，应该将标签作为一个热向量传递，而不是作为索引传递。我想这就是为什么你会得到这个错误，你传递的是一维张量而不是二维张量

第二个问题是理论上的（与你的错误无关，但我想指出）：因为

logits

是你的预测，你从那里得到

y

，你基本上是在比较你的

logits

。没有学习。你需要提供实际的标签，并以此为基础进行学习

---

只是一个注释，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，无法确定。

如何纠正导致模型无法学习的logits错误？只是为了去除它们？@MasonChoi在上述案例中，他也使用了预测作为标签。您需要使用培训集中的原始标签作为标签（地面真相），以便网络能够学习。仅仅删除它们就可以删除系统中的任何反馈，但这不会减少反馈。