TensorFlow-TensorBoard的问题

Question

我正在关注本教程 。 我已经按原样复制了代码，所以看不到任何错误。 当我尝试添加以下行以为TensorBoard创建文件时，就会出现错误：

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

当我运行整个代码时，Python返回此错误：

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

我不明白为什么它不能正常运行。 有人可以帮我吗？

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

进行这些更改后，代码可以毫无问题地运行

logs_path = './tensor_board' 

sess.run(init)