SyntaxError：標識符中的無效字符Python 3.5.2＆tensorflow

我現在開始學習tensorflow..was跟隨一個youtube vid在此並跟隨程序，但得到一個SyntaxError：無效字符在標識符-line 53 sess.run（tf.global_variables_initializer（））...看節目below..any援助讚賞：SyntaxError：標識符中的無效字符Python 3.5.2＆tensorflow

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 width 
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]))} 

    # (input_data * 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 = neural_network_model(x) 
    cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=y)) 
    optimizer = tf.train.AdamOptimizer().minimize(cost) 

    hm_epochs = 10 

    with tf.Session() as sess: 
     sess.run(tf.global_variables_initializer()) 

     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)

來源

2017-02-19 Ken M

你的問題可以使用循環的下劃線字符？這似乎是一種奇怪的循環方式，特別是因爲作爲一個外人看起來像一個沒有意義的變量名。 – briblue3

這是說你不使用變量的正常方式。 –

我前一段時間得到了同樣的錯誤。請不要複製粘貼來自不同編輯器或網頁的代碼。如果你在你的編輯器中鍵入它，它不會給你那個錯誤。

來源

2017-07-23 07:28:15

SyntaxError：標識符中的無效字符Python 3.5.2＆tensorflow

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