Tensorflow 可视化 TensorBoard 尝试~
2018-01-10 10:56
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原文地址：blog.csdn.net/silver_sail/article/details/51899659
安装Tensorflow的过程就不必说了，安装官网或者google一下，很多资源。
这次实验是在Iris数据集进行的，下载链接
代码如下：
[python]
view plain
copy
import os
import cv2
import numpy as np
import sys
import tensorflow as tf
import random
import math
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
def load_iris(path):
#check file exist.
if not os.path.exists(path):
print "path is not exist"
return
return_data = []
return_label = []
my_map = {}
key = 0
iris_file = open(path);
for line in iris_file:
#cut the \n
line = line[:-1]
elements = line.split(',')
if len(elements) == 5:
temp = elements[:-1]
data = [float(x) for x in temp]
category = elements[4]
label = key
if my_map.has_key(category):
label = my_map[category]
else:
my_map[category] = key
key = key + 1
label_vector = [0] * 3;
label_vector[label] = 1;
return_data.append(data)
return_label.append(label_vector)
iris_file.close()
return return_data,return_label
def run(train_path):
#load data
img,label = load_iris(train_path)
sess = tf.InteractiveSession()
#first layer.
with tf.name_scope('input'):
x = tf.placeholder("float", shape=[None, 4],name='x-input')
y_ = tf.placeholder("float", shape=[None, 3],name='y-input')
def next_batch(img,label,size):
img_r =[]
label_r = []
for num in range(size):
index = random.randint(0,len(img)-1)
img_r.append(np.array(img[index]))
label_r.append(np.array(label[index]))
img_r = np.array(img_r)
label_r = np.array(label_r)
return {x:img_r,y_:label_r}
def variable_summaries(var, name):
with tf.name_scope('summaries'):
mean = tf.reduce_mean(var)
tf.scalar_summary('mean/' + name, mean)
with tf.name_scope('stddev'):
stddev = tf.sqrt(tf.reduce_sum(tf.square(var - mean)))
tf.scalar_summary('sttdev/' + name, stddev)
tf.scalar_summary('max/' + name, tf.reduce_max(var))
tf.scalar_summary('min/' + name, tf.reduce_min(var))
tf.histogram_summary(name, var)
#fully connection
def nn_layer(input,input_dim,output_dim,layer_name,act=tf.nn.relu):
with tf.name_scope(layer_name):
with tf.name_scope('W'):
f_w_1 = weight_variable([input_dim,output_dim])
variable_summaries(f_w_1, layer_name + '/weights')
with tf.name_scope('B'):
f_b_1 = bias_variable([output_dim])
variable_summaries(f_b_1, layer_name + '/bias')
with tf.name_scope('Wx_plus_b'):
input_drop = tf.reshape(input,[-1,input_dim])
f_r_1 = tf.matmul(input_drop,f_w_1) + f_b_1
tf.histogram_summary(layer_name + '/pre_activations', f_r_1)
activations = act(f_r_1, 'activation')
tf.histogram_summary(layer_name + '/activations', activations)
return activations
l1_output = nn_layer(x,4,100,'layer1')
l2_output = nn_layer(l1_output,100,3,'layer2',act=tf.nn.softmax)
#
with tf.name_scope('cross_entropy'):
cross_entropy = -tf.reduce_sum(y_*tf.log(l2_output))
tf.scalar_summary('cross entropy', cross_entropy)
with tf.name_scope('train'):
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(l2_output,1), tf.argmax(y_,1))
with tf.name_scope('accuracy'):
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
tf.scalar_summary('accuracy',accuracy)
merged = tf.merge_all_summaries()
train_writer = tf.train.SummaryWriter('/home/ubuntu/temp/log/train',sess.graph)
test_writer = tf.train.SummaryWriter('/home/ubuntu/temp/log/test')
tf.initialize_all_variables().run()
for i in range(200000):
if i % 100 == 0: # Record summaries and test-set accuracy
summary, acc = sess.run([merged, accuracy], feed_dict=next_batch(img,label,20))
test_writer.add_summary(summary, i)
print('Accuracy at step %s: %s' % (i, acc))
else: # Record train set summaries, and train
if i % 100 == 99: # Record execution stats
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
summary, _ = sess.run([merged, train_step],
feed_dict=next_batch(img,label,20),
options=run_options,
run_metadata=run_metadata)
train_writer.add_run_metadata(run_metadata, 'step%d' % i)
train_writer.add_summary(summary, i)
print('Adding run metadata for', i)
else: # Record a summary
summary, _ = sess.run([merged, train_step], feed_dict=next_batch(img,label,20))
train_writer.add_summary(summary, i)
if __name__ == '__main__':
run('iris.data.set.txt')
代码是参考tensorflow官网的例子进行实验的，官网例子如下：
[python]
view plain
copy
# Copyright 2015 The TensorFlow Authors. 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
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')
y_ = tf.placeholder(tf.float32, [None, 10], name='y-input')
with tf.name_scope('input_reshape'):
image_shaped_input = tf.reshape(x, [-1, 28, 28, 1])
tf.image_summary('input', image_shaped_input, 10)
# 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.scalar_summary('mean/' + name, mean)
with tf.name_scope('stddev'):
stddev = tf.sqrt(tf.reduce_sum(tf.square(var - mean)))
tf.scalar_summary('sttdev/' + name, stddev)
tf.scalar_summary('max/' + name, tf.reduce_max(var))
tf.scalar_summary('min/' + name, tf.reduce_min(var))
tf.histogram_summary(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.histogram_summary(layer_name + '/pre_activations', preactivate)
activations = act(preactivate, 'activation')
tf.histogram_summary(layer_name + '/activations', activations)
return activations
hidden1 = nn_layer(x, 784, 500, 'layer1')
with tf.name_scope('dropout'):
keep_prob = tf.placeholder(tf.float32)
tf.scalar_summary('dropout_keep_probability', keep_prob)
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.scalar_summary('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.scalar_summary('accuracy', accuracy)
# Merge all the summaries and write them out to /tmp/mnist_logs (by default)
merged = tf.merge_all_summaries()
train_writer = tf.train.SummaryWriter(FLAGS.summaries_dir + '/train',
sess.graph)
test_writer = tf.train.SummaryWriter(FLAGS.summaries_dir + '/test')
tf.initialize_all_variables().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 summaries, and train
if i % 100 == 99: # Record execution stats
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
summary, _ = sess.run([merged, train_step],
feed_dict=feed_dict(True),
options=run_options,
run_metadata=run_metadata)
train_writer.add_run_metadata(run_metadata, 'step%d' % i)
train_writer.add_summary(summary, i)
print('Adding run metadata for', i)
else: # Record a summary
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()
有了这个代码，就可以运行了。因为自己设置的目录是/home/ubuntu/temp/log，所以在tensorboard运行的时候要指定这个目录。
命令如下：
[plain]
view plain
copy
python tensorboard.py --logdir=/home/ubuntu/temp/log
之后访问一下指定地址：
如果访问没有数据，可以在命令后面加上--debug来查看详细信息，
红色标记的是tensorboard监视的目录，查看一下是否正确。
如果还是不正确。。。就只能安装官网Readme来排查了：
就是这里
安装Tensorflow的过程就不必说了，安装官网或者google一下，很多资源。
这次实验是在Iris数据集进行的，下载链接
代码如下：
[python]
view plain
copy
import os
import cv2
import numpy as np
import sys
import tensorflow as tf
import random
import math
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
def load_iris(path):
#check file exist.
if not os.path.exists(path):
print "path is not exist"
return
return_data = []
return_label = []
my_map = {}
key = 0
iris_file = open(path);
for line in iris_file:
#cut the \n
line = line[:-1]
elements = line.split(',')
if len(elements) == 5:
temp = elements[:-1]
data = [float(x) for x in temp]
category = elements[4]
label = key
if my_map.has_key(category):
label = my_map[category]
else:
my_map[category] = key
key = key + 1
label_vector = [0] * 3;
label_vector[label] = 1;
return_data.append(data)
return_label.append(label_vector)
iris_file.close()
return return_data,return_label
def run(train_path):
#load data
img,label = load_iris(train_path)
sess = tf.InteractiveSession()
#first layer.
with tf.name_scope('input'):
x = tf.placeholder("float", shape=[None, 4],name='x-input')
y_ = tf.placeholder("float", shape=[None, 3],name='y-input')
def next_batch(img,label,size):
img_r =[]
label_r = []
for num in range(size):
index = random.randint(0,len(img)-1)
img_r.append(np.array(img[index]))
label_r.append(np.array(label[index]))
img_r = np.array(img_r)
label_r = np.array(label_r)
return {x:img_r,y_:label_r}
def variable_summaries(var, name):
with tf.name_scope('summaries'):
mean = tf.reduce_mean(var)
tf.scalar_summary('mean/' + name, mean)
with tf.name_scope('stddev'):
stddev = tf.sqrt(tf.reduce_sum(tf.square(var - mean)))
tf.scalar_summary('sttdev/' + name, stddev)
tf.scalar_summary('max/' + name, tf.reduce_max(var))
tf.scalar_summary('min/' + name, tf.reduce_min(var))
tf.histogram_summary(name, var)
#fully connection
def nn_layer(input,input_dim,output_dim,layer_name,act=tf.nn.relu):
with tf.name_scope(layer_name):
with tf.name_scope('W'):
f_w_1 = weight_variable([input_dim,output_dim])
variable_summaries(f_w_1, layer_name + '/weights')
with tf.name_scope('B'):
f_b_1 = bias_variable([output_dim])
variable_summaries(f_b_1, layer_name + '/bias')
with tf.name_scope('Wx_plus_b'):
input_drop = tf.reshape(input,[-1,input_dim])
f_r_1 = tf.matmul(input_drop,f_w_1) + f_b_1
tf.histogram_summary(layer_name + '/pre_activations', f_r_1)
activations = act(f_r_1, 'activation')
tf.histogram_summary(layer_name + '/activations', activations)
return activations
l1_output = nn_layer(x,4,100,'layer1')
l2_output = nn_layer(l1_output,100,3,'layer2',act=tf.nn.softmax)
#
with tf.name_scope('cross_entropy'):
cross_entropy = -tf.reduce_sum(y_*tf.log(l2_output))
tf.scalar_summary('cross entropy', cross_entropy)
with tf.name_scope('train'):
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(l2_output,1), tf.argmax(y_,1))
with tf.name_scope('accuracy'):
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
tf.scalar_summary('accuracy',accuracy)
merged = tf.merge_all_summaries()
train_writer = tf.train.SummaryWriter('/home/ubuntu/temp/log/train',sess.graph)
test_writer = tf.train.SummaryWriter('/home/ubuntu/temp/log/test')
tf.initialize_all_variables().run()
for i in range(200000):
if i % 100 == 0: # Record summaries and test-set accuracy
summary, acc = sess.run([merged, accuracy], feed_dict=next_batch(img,label,20))
test_writer.add_summary(summary, i)
print('Accuracy at step %s: %s' % (i, acc))
else: # Record train set summaries, and train
if i % 100 == 99: # Record execution stats
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
summary, _ = sess.run([merged, train_step],
feed_dict=next_batch(img,label,20),
options=run_options,
run_metadata=run_metadata)
train_writer.add_run_metadata(run_metadata, 'step%d' % i)
train_writer.add_summary(summary, i)
print('Adding run metadata for', i)
else: # Record a summary
summary, _ = sess.run([merged, train_step], feed_dict=next_batch(img,label,20))
train_writer.add_summary(summary, i)
if __name__ == '__main__':
run('iris.data.set.txt')
代码是参考tensorflow官网的例子进行实验的，官网例子如下：
[python]
view plain
copy
# Copyright 2015 The TensorFlow Authors. 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
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')
y_ = tf.placeholder(tf.float32, [None, 10], name='y-input')
with tf.name_scope('input_reshape'):
image_shaped_input = tf.reshape(x, [-1, 28, 28, 1])
tf.image_summary('input', image_shaped_input, 10)
# 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.scalar_summary('mean/' + name, mean)
with tf.name_scope('stddev'):
stddev = tf.sqrt(tf.reduce_sum(tf.square(var - mean)))
tf.scalar_summary('sttdev/' + name, stddev)
tf.scalar_summary('max/' + name, tf.reduce_max(var))
tf.scalar_summary('min/' + name, tf.reduce_min(var))
tf.histogram_summary(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.histogram_summary(layer_name + '/pre_activations', preactivate)
activations = act(preactivate, 'activation')
tf.histogram_summary(layer_name + '/activations', activations)
return activations
hidden1 = nn_layer(x, 784, 500, 'layer1')
with tf.name_scope('dropout'):
keep_prob = tf.placeholder(tf.float32)
tf.scalar_summary('dropout_keep_probability', keep_prob)
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.scalar_summary('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.scalar_summary('accuracy', accuracy)
# Merge all the summaries and write them out to /tmp/mnist_logs (by default)
merged = tf.merge_all_summaries()
train_writer = tf.train.SummaryWriter(FLAGS.summaries_dir + '/train',
sess.graph)
test_writer = tf.train.SummaryWriter(FLAGS.summaries_dir + '/test')
tf.initialize_all_variables().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 summaries, and train
if i % 100 == 99: # Record execution stats
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
summary, _ = sess.run([merged, train_step],
feed_dict=feed_dict(True),
options=run_options,
run_metadata=run_metadata)
train_writer.add_run_metadata(run_metadata, 'step%d' % i)
train_writer.add_summary(summary, i)
print('Adding run metadata for', i)
else: # Record a summary
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()
有了这个代码，就可以运行了。因为自己设置的目录是/home/ubuntu/temp/log，所以在tensorboard运行的时候要指定这个目录。
命令如下：
[plain]
view plain
copy
python tensorboard.py --logdir=/home/ubuntu/temp/log
之后访问一下指定地址：
如果访问没有数据，可以在命令后面加上--debug来查看详细信息，
红色标记的是tensorboard监视的目录，查看一下是否正确。
如果还是不正确。。。就只能安装官网Readme来排查了：
就是这里
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