各位有遇到這個報錯的情況嗎？

import?os
import?model
import?tensorflow?as?tf
import?input_data

data?=?input_data.read_data_sets('MNIST_data',?one_hot=True)

#?model
with?tf.variable_scope("convolutional"):
????x?=?tf.placeholder(tf.float32,?[None,?784],?name='x')
????keep_prob?=?tf.placeholder(tf.float32)
????y,?variables?=?model.convolutional(x,?keep_prob)

#?train
y_?=?tf.placeholder(tf.float32,?[None,?10],?name='y')
cross_entropy?=?-tf.reduce_sum(y_?*?tf.log(y))
train_step?=?tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction?=?tf.equal(tf.argmax(y,?1),?tf.argmax(y_,?1))
accuracy?=?tf.reduce_mean(tf.cast(correct_prediction,?tf.float32))

saver?=?tf.train.Saver(variables)

with?tf.Session()?as?sess:
????merged_summary_op?=?tf.summary.merge_all()
????summary_writer?=?tf.summary.FileWriter('/tmp/mnist_log/1',?sess.graph)
????summary_writer.add_graph(sess.graph)
????sess.run(tf.global_variables_initializer())

????for?i?in?range(20000):
????????batch?=?data.train.next_batch(50)
????????if?i?%?100?==?0:
????????????????#?發(fā)現(xiàn)了，是這里末尾少寫參數(shù)了,加上?<?,?keep_prob:?1.0?>就ok啦
????????????train_accuracy?=?accuracy.eval(feed_dict={x:?batch[0],?y_:?batch[1],?keep_prob:?1.0})
????????????print("Step?%d,?training?accuracy?%g"?%?(i,?train_accuracy))
????????sess.run(train_step,?feed_dict={x:?batch[0],?y_:?batch[1],?keep_prob:?0.5})

????print(sess.run(accuracy,?feed_dict={x:?data.test.images,?y_:?data.test.labels,?keep_prob:?1.0}))

????path?=?saver.save(
????????sess,?os.path.join(os.path.dirname(__file__),?'data_convolutional',?'convolutional.ckpt'),
????????write_meta_graph=False,?write_state=False)

????print("Saved:?",?path)