python之tensorflow中的MNIST分类，RecursionError : maximum recursion depth exceeded

我运行了一个用于 MNIST 分类的神经网络模型并收到了错误-

RecursionError: maximum recursion depth exceeded 

我检查了一些关于 stackoverflow 的问题并尝试将递归限制增加到 1500 但没有奏效。 我应该如何增加限制？我怎么知道什么限制会导致堆栈溢出？

我遵循了 here 中的教程

我的 Windows 10 机器上有 Anaconda 3.5 发行版。

完整代码在这里-

import tensorflow as tf 
from tensorflow.examples.tutorials.mnist import input_data 
 
mnist= input_data.read_data_sets("/tmp/data/", one_hot=True) 
 
n_nodes_hl1 = 500 
n_nodes_hl2 = 500 
n_nodes_hl3 =500 
 
n_classes = 10 
batch_size = 100 
 
#height x weight 
x = tf.placeholder('float', [None, 784]) 
y = tf.placeholder('float') 
 
def neural_network_model(data): 
 
    hidden_1_layer= {'weights': tf.Variable(tf.random_normal([784, n_nodes_hl1])), 
                 'biases': tf.Variable(tf.random_normal([n_nodes_hl1])) 
                 } 
    hidden_2_layer = {'weights': tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2])), 
                  'biases': tf.Variable(tf.random_normal([n_nodes_hl2])) 
                  } 
    hidden_3_layer = {'weights': tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2])), 
                  'biases': tf.Variable(tf.random_normal([n_nodes_hl3])) 
                  } 
    output_layer = {'weights': tf.Variable(tf.random_normal([n_nodes_hl3,n_classes])), 
                  'biases': tf.Variable(tf.random_normal([n_classes])) 
                } 
 
#our model= (input_data x weights) + biases 
 
    l1 = tf.add(tf.matmul(data, hidden_1_layer['weights']), hidden_1_layer['biases']) 
    l1 = tf.nn.relu(l1) 
 
    l2 = tf.add(tf.matmul(l1, hidden_2_layer['weights']), hidden_2_layer['biases']) 
    l2 = tf.nn.relu(l2) 
 
    l3 = tf.add(tf.matmul(l2, hidden_3_layer['weights']), hidden_3_layer['biases']) 
    l3 = tf.nn.relu(l3) 
 
    output = tf.matmul(l3, output_layer['weights']) + output_layer['biases'] 
 
    return output 
 
 
def train_neural_network(x): 
    prediction = train_neural_network(x) 
    cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(prediction,y)) 
    optimizer= tf.train.AdamOptimizer().minimize(cost) #default learning rate for adamoptimizer= 0.001 
 
    hm_epochs = 5 
    with tf.Session() as sess: 
        sess.run(tf.initialize_all_variables()) 
        for epoch in range(hm_epochs): 
            epoch_loss = 0 
            for _ in range(int(mnist.train.num_examples / batch_size)): 
                epoch_x, epoch_y = mnist.train.next_batch(batch_size) 
                _, c = sess.run([optimizer, cost], feed_dict={x: epoch_x, y: epoch_y}) 
                epoch_loss += c 
 
    print(('Epoch', epoch), ('completed out of', hm_epochs), ('loss:', epoch_loss)) 
 
    correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1)) 
    accuracy = tf.reduce_mean(tf.cast(correct, 'float')) 
    print(('Accuracy:', accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))) 
 
train_neural_network(x) 

请您参考如下方法:

我不知道确切的代码应该是什么，但我很确定以下几行是错误的:

def train_neural_network(x): 
    prediction = train_neural_network(x) 

这会造成无限递归，增加递归限制也解决不了问题。