TensorFlow-TensorBoard 관련 문제

Question

다음은 this tutorial입니다. 코드를 그대로 복사 했으므로 오류가 표시되지 않습니다. 내가 오류로 전체 코드를 파이썬 수익률을 실행하면

logs_path = '/tensor_board' 
writer = tf.summary.FileWriter(logs_path, graph=tf.get_default_graph()) 
# RUN 
sess.run(init, writer) 

가 :

Traceback (most recent call last): 
    File "tf_number_recon.py", line 39, in <module> 
    sess.run(init, writer) 
    File "C:\Python35\lib\site-packages\tensorflow\python\client\session.py", line 766, in run 
    run_metadata_ptr) 
    File "C:\Python35\lib\site-packages\tensorflow\python\client\session.py", line 913, in _run 
    feed_dict = nest.flatten_dict_items(feed_dict) 
    File "C:\Python35\lib\site-packages\tensorflow\python\util\nest.py", line 171, in flatten_dict_items 
    raise TypeError("input must be a dictionary") 
TypeError: input must be a dictionary 

내가 볼 수없는 내가 TensorBoard 위해 파일을 만들려면이 줄을 추가 할 때 오류는 올 왜 그것이 exxpected로 작동하지 않습니다. 도와 줄 수 있겠 니?

from __future__ import absolute_import 
from __future__ import division 
from __future__ import print_function 

# importing the dataset used to train the Neural Network 
from tensorflow.examples.tutorials.mnist import input_data 
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) 

# importing Tensorflow 
import tensorflow as tf               

import argparse 
import sys 

# Declaring some imjmportant variables 
x = tf.placeholder(tf.float32, [None, 784])         # x is 
W = tf.Variable(tf.zeros([784, 10]))          # W creará 10 vectores de evidencia, uno para cada numero entre 0-9 
b = tf.Variable(tf.zeros([10]))            # b is 
y = tf.nn.softmax(tf.matmul(x, W) + b)          # y será la salida. Aqui definimos el modelo 
y_ = tf.placeholder(tf.float32, [None, 10])         # 

# Cross Entropy: mide lo lejos que nuestra predicción está de la realidad, para así mejorar la red neuronal (no controla lo bien que lo hace, sino más bien lo mal que lo hace) 
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1])) 

# Se pide que durante el proceso se minimize el cross entropy 
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy) 

# initializing the variables 
init = tf.global_variables_initializer() 

# Run a session and initialize the operations 
sess = tf.Session() 
# Tensor Board 
logs_path = '/tensor_board' 
writer = tf.summary.FileWriter(logs_path, graph=tf.get_default_graph()) 
# RUN 
sess.run(init, writer) 

# Loop for training 
for i in range(1000): 
    batch_xs, batch_ys = mnist.train.next_batch(100) 
    sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys}) 

# Evaluate the model 
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1)) 
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) 
eficacia = sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}) 
print(eficacia) 

Answer 1

아무 문제없이 코드가 실행 이러한 변화를 만들기

logs_path = './tensor_board' 

sess.run(init)