unident与任何以十为单位的外部缩进级别都不匹配

Question

您好，我只是为深入学习编写了以下代码：

import tensorflow as tf
from tensorflow.example.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

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_hl2,n_nodes_hl3])),
                   '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]))} 


    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, ouput_layer['weights']) + output_layer['biases']

    return output

def train_neural_network(x):
    prediction = neural_network_model(x)
    cost = tf.reduce.mean(tf.nn.softmax_cross_entropy_with_logits(prediction,y))
    optimizer = tf.train.AdamOptimizer().minimize(cost)


    hm_epochs = 10

    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_example/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)

但我有个错误：

^{pr2}$

文件“deep-net_2.py”，第28行 隐藏层＝{'weights'：tf.变量(tf.random_正常（[n_nodes_hl2，n_nodes_hl3]）， ^ 缩进错误：未缩进与任何外部缩进级别都不匹配

我做错什么了？在

Answer 1

Tensorflow在图形构造期间使用Python进行编译。因此，缩进需要遵循Python规则。你应该仔细检查有问题的行是否与前一行有相同的缩进。如有必要，请使用允许查看正在使用的间距字符的编辑器。在