Windows10下TensorBoard可视化实例（附调整后代码）

继续。。。

tensorflow demo在windows调试通过后。开始调试Tensorboard可视化工具。

参考文章：http://wiki.jikexueyuan.com/project/tensorflow-zh/how_tos/summaries_and_tensorboard.html

所使用的Py代码:https://github.com/tensorflow/tensorflow/blob/r0.10/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py

要使用tensorboard查看整个tendorflow的卷积层，必须进行summaries，整个tensorbord的工作原理参看参考文章，下面是我在windows10上的调试记录

开工

一、导入数据，还是使用我自己编写input_data文件，使用本地的数据，代码在http://blog.csdn.net/kinsent/article/details/78180955

二、新建文件TestBoardDemo.py，复制粘贴mnist_with_summaries.py全部代码，开始修改：

         1.from tensorflow.examples.tutorials.mnist import input_data改为

import input_data（这样就是使用本地的Mnist数据）

2.由于mnist_with_summaries.py的代码使用的是tensorflow0.7,所以对一些函数进行更名；

1）tf.image_summary  改为 tf.summary.image

2) tf.scalar_summary  改为 tf.summary.scalar

3）tf.histogram_summary 改为 tf.summary.histogram

4）tf.train.SummaryWriter 改为 tf.summary.FileWriter

5）tf.merge_all_summaries() 改为 tf.summary.merge_all()

三、一个小坑

改完上述函数名，以为没问题，跑一次试试，报AssertionError断言错误，经检查发现是

def nn_layer(input_tensor, input_dim, output_dim, layer_name, act=tf.nn.relu):调用中报错，

debug跟踪，是红框处代码问题，查看python3.6文档，改为

activations = act(preactivate)，即可。断言错误是python3.6认为只应该传入一个参数，而原来的代码传入了两个。我遇到的

    AssertionError很多都是因为函数传参错误导致的

第四步、run一下。

第五步、运行tensorboard,注意红框处，logdir参数就是你python的设置的summaries_dir参数

第六步，访问tensorboard

代码：

# Copyright 2015 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the 'License');
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0 #
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an 'AS IS' BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================

"""A simple MNIST classifier which displays summaries in TensorBoard.

This is an unimpressive MNIST model, but it is a good example of using
tf.name_scope to make a graph legible in the TensorBoard graph explorer, and of
naming summary tags so that they are grouped meaningfully in TensorBoard.

It demonstrates the functionality of every TensorBoard dashboard.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf
#from tensorflow.examples.tutorials.mnist import input_data
import input_data

flags = tf.app.flags
FLAGS = flags.FLAGS
flags.DEFINE_boolean('fake_data', False, 'If true, uses fake data '
   'for unit testing.')
flags.DEFINE_integer('max_steps', 1000, 'Number of steps to run trainer.')
flags.DEFINE_float('learning_rate', 0.001, 'Initial learning rate.')
flags.DEFINE_float('dropout', 0.9, 'Keep probability for training dropout.')
flags.DEFINE_string('data_dir', '/tmp/data', 'Directory for storing data')
flags.DEFINE_string('summaries_dir', '/tmp/mnist_logs', 'Summaries directory')

def train():
# Import data
mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True,
fake_data=FLAGS.fake_data)

sess = tf.InteractiveSession()
# Create a multilayer model.
# Input placehoolders
with tf.name_scope('input'):
x = tf.placeholder(tf.float32, [None, 784], name='x-input')
image_shaped_input = tf.reshape(x, [-1, 28, 28, 1])
tf.summary.image('input', image_shaped_input, 10)
y_ = tf.placeholder(tf.float32, [None, 10], name='y-input')
keep_prob = tf.placeholder(tf.float32)
tf.summary.scalar('dropout_keep_probability', keep_prob)

# We can't initialize these variables to 0 - the network will get stuck.
def weight_variable(shape):
"""Create a weight variable with appropriate initialization."""
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)

def bias_variable(shape):
"""Create a bias variable with appropriate initialization."""
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)

def variable_summaries(var, name):
"""Attach a lot of summaries to a Tensor."""
with tf.name_scope('summaries'):
mean = tf.reduce_mean(var)
tf.summary.scalar('mean/' + name, mean)
with tf.name_scope('stddev'):
stddev = tf.sqrt(tf.reduce_sum(tf.square(var - mean)))
tf.summary.scalar('sttdev/' + name, stddev)
tf.summary.scalar('max/' + name, tf.reduce_max(var))
tf.summary.scalar('min/' + name, tf.reduce_min(var))
tf.summary.histogram(name, var)

def nn_layer(input_tensor, input_dim, output_dim, layer_name, act=tf.nn.relu):
"""Reusable code for making a simple neural net layer.
It does a matrix multiply, bias add, and then uses relu to nonlinearize.
It also sets up name scoping so that the resultant graph is easy to read, and
adds a number of summary ops.
"""
# Adding a name scope ensures logical grouping of the layers in the graph.
with tf.name_scope(layer_name):
# This Variable will hold the state of the weights for the layer
with tf.name_scope('weights'):
weights = weight_variable([input_dim, output_dim])
variable_summaries(weights, layer_name + '/weights')
with tf.name_scope('biases'):
biases = bias_variable([output_dim])
variable_summaries(biases, layer_name + '/biases')
with tf.name_scope('Wx_plus_b'):
preactivate = tf.matmul(input_tensor, weights) + biases
tf.summary.histogram(layer_name + '/pre_activations', preactivate)
activations = act(preactivate)
tf.summary.histogram(layer_name + '/activations', activations)
return activations

hidden1 = nn_layer(x, 784, 500, 'layer1')
dropped = tf.nn.dropout(hidden1, keep_prob)
y = nn_layer(dropped, 500, 10, 'layer2', act=tf.nn.softmax)

with tf.name_scope('cross_entropy'):
diff = y_ * tf.log(y)
with tf.name_scope('total'):
cross_entropy = -tf.reduce_mean(diff)
tf.summary.scalar('cross entropy', cross_entropy)

with tf.name_scope('train'):
train_step = tf.train.AdamOptimizer(
FLAGS.learning_rate).minimize(cross_entropy)

with tf.name_scope('accuracy'):
with tf.name_scope('correct_prediction'):
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
with tf.name_scope('accuracy'):
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
tf.summary.scalar('accuracy', accuracy)

# Merge all the summaries and write them out to /tmp/mnist_logs (by default)
merged = tf.summary.merge_all()
train_writer = tf.summary.FileWriter(FLAGS.summaries_dir + '/train', sess.graph)
test_writer = tf.summary.FileWriter(FLAGS.summaries_dir + '/test')
tf.global_variables_initializer().run()

# Train the model, and also write summaries.
# Every 10th step, measure test-set accuracy, and write test summaries
# All other steps, run train_step on training data, & add training summaries

def feed_dict(train):
"""Make a TensorFlow feed_dict: maps data onto Tensor placeholders."""
if train or FLAGS.fake_data:
xs, ys = mnist.train.next_batch(100, fake_data=FLAGS.fake_data)
k = FLAGS.dropout
else:
xs, ys = mnist.test.images, mnist.test.labels
k = 1.0
return {x: xs, y_: ys, keep_prob: k}

for i in range(FLAGS.max_steps):
if i % 10 == 0:  # Record summaries and test-set accuracy
summary, acc = sess.run([merged, accuracy], feed_dict=feed_dict(False))
test_writer.add_summary(summary, i)
print('Accuracy at step %s: %s' % (i, acc))
else: # Record train set summarieis, and train
summary, _ = sess.run([merged, train_step], feed_dict=feed_dict(True))
train_writer.add_summary(summary, i)

def main(_):
if tf.gfile.Exists(FLAGS.summaries_dir):
tf.gfile.DeleteRecursively(FLAGS.summaries_dir)
tf.gfile.MakeDirs(FLAGS.summaries_dir)
train()

if __name__ == '__main__':
tf.app.run()