【tensorflow 大马哈鱼】 tensorboard可视化《莫凡》

 在当前目录下调用， 一个参数logdir， log是文件夹

$ tensorboard --logdir log

标准过程 

#存储变量
tf.summary.histogram('h_out', l1)
#存储loss
tf.summary.scalar('loss', loss) 
#operation to merge all summary  建立merge操作

#主要
writer = tf.summary.FileWriter('./log', sess.graph)
#merge_all操作
merge_op = tf.summary.merge_all()

for step in range(100):
    _, result = sess.run([train_op, merge_op], {tf_x: x, tf_y: y})
    #writer写入merge_op结果
    writer.add_summary(result, step)

示例代码一：

标准代码，注意：

# operation to merge all summary

merge_op = tf.summary.merge_all()                    是merge所有summary的操作

for step in range(100):
    # train and net output
    _, result = sess.run([train_op, merge_op], {tf_x: x, tf_y: y})      运行一下merge操作
    writer.add_summary(result, step)                     将结果存入

注意：

loss = tf.losses.mean_squared_error(tf_y, output, scope='loss')   这样loss才有单独模块

import tensorflow as tf
import numpy as np

tf.set_random_seed(1)
np.random.seed(1)

# fake data
x = np.linspace(-1, 1, 100)[:, np.newaxis]          # shape (100, 1)
noise = np.random.normal(0, 0.1, size=x.shape)
y = np.power(x, 2) + noise                          # shape (100, 1) + some noise

with tf.variable_scope('Inputs'):
    tf_x = tf.placeholder(tf.float32, x.shape, name='x')
    tf_y = tf.placeholder(tf.float32, y.shape, name='y')

with tf.variable_scope('Net'):
    l1 = tf.layers.dense(tf_x, 10, tf.nn.relu, name='hidden_layer')
    output = tf.layers.dense(l1, 1, name='output_layer')

    # add to histogram summary
    tf.summary.histogram('h_out', l1)
    tf.summary.histogram('pred', output)

loss = tf.losses.mean_squared_error(tf_y, output, scope='loss')
train_op = tf.train.GradientDescentOptimizer(learning_rate=0.5).minimize(loss)
tf.summary.scalar('loss', loss)     # add loss to scalar summary

sess = tf.Session()
sess.run(tf.global_variables_initializer())

writer = tf.summary.FileWriter('./log', sess.graph)     # write to file
merge_op = tf.summary.merge_all()                       # operation to merge all summary

for step in range(100):
    # train and net output
    _, result = sess.run([train_op, merge_op], {tf_x: x, tf_y: y})
    writer.add_summary(result, step)

# Lastly, in your terminal or CMD, type this :
# $ tensorboard --logdir path/to/log
# open you google chrome, type the link shown on your terminal or CMD. (something like this: http://localhost:6006)

示例代码二：

建立两个tf.summary.FileWriter，分别是训练集和测试集的summary

这个例子有些问题，因为没用命名空间，所以图很乱

import tensorflow as tf
from sklearn.datasets import load_digits
from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import LabelBinarizer

# load data
digits = load_digits()
X = digits.data
y = digits.target
y = LabelBinarizer().fit_transform(y)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.3)


def add_layer(inputs, in_size, out_size, layer_name, activation_function=None, ):
    # add one more layer and return the output of this layer
    Weights = tf.Variable(tf.random_normal([in_size, out_size]))
    biases = tf.Variable(tf.zeros([1, out_size]) + 0.1, )
    Wx_plus_b = tf.matmul(inputs, Weights) + biases
    # here to dropout
    Wx_plus_b = tf.nn.dropout(Wx_plus_b, keep_prob)
    if activation_function is None:
        outputs = Wx_plus_b
    else:
        outputs = activation_function(Wx_plus_b, )
    tf.summary.histogram(layer_name + '/outputs', outputs)
    return outputs


# define placeholder for inputs to network
keep_prob = tf.placeholder(tf.float32)
xs = tf.placeholder(tf.float32, [None, 64])  # 8x8
ys = tf.placeholder(tf.float32, [None, 10])

# add output layer
l1 = add_layer(xs, 64, 50, 'l1', activation_function=tf.nn.tanh)
prediction = add_layer(l1, 50, 10, 'l2', activation_function=tf.nn.softmax)

# the loss between prediction and real data
cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction),
                                              reduction_indices=[1]))  # loss
tf.summary.scalar('loss', cross_entropy)
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

sess = tf.Session()
merged = tf.summary.merge_all()
# summary writer goes in here
train_writer = tf.summary.FileWriter("logs/train", sess.graph)
test_writer = tf.summary.FileWriter("logs/test", sess.graph)

# tf.initialize_all_variables() no long valid from
# 2017-03-02 if using tensorflow >= 0.12
if int((tf.__version__).split('.')[1]) < 12 and int((tf.__version__).split('.')[0]) < 1:
    init = tf.initialize_all_variables()
else:
    init = tf.global_variables_initializer()
sess.run(init)
for i in range(500):
    # here to determine the keeping probability
    sess.run(train_step, feed_dict={xs: X_train, ys: y_train, keep_prob: 0.5})
    if i % 10 == 0:
        # record loss
        train_result = sess.run(merged, feed_dict={xs: X_train, ys: y_train, keep_prob: 1})
        test_result = sess.run(merged, feed_dict={xs: X_test, ys: y_test, keep_prob: 1})
        train_writer.add_summary(train_result, i)
        test_writer.add_summary(test_result, i)

示例代码三：

# -*- coding: utf-8 -*-

from __future__ import print_function
import tensorflow as tf
import numpy as np


def add_layer(inputs, in_size, out_size, n_layer, activation_function=None):
    # add one more layer and return the output of this layer
    layer_name = 'layer%s' % n_layer
    with tf.name_scope(layer_name):
        with tf.name_scope('weights'):
            Weights = tf.Variable(tf.random_normal([in_size, out_size]), name='W')
            tf.summary.histogram(layer_name + '/weights', Weights)
        with tf.name_scope('biases'):
            biases = tf.Variable(tf.zeros([1, out_size]) + 0.1, name='b')
            tf.summary.histogram(layer_name + '/biases', biases)
        with tf.name_scope('Wx_plus_b'):
            Wx_plus_b = tf.add(tf.matmul(inputs, Weights), biases)
        if activation_function is None:
            outputs = Wx_plus_b
        else:
            outputs = activation_function(Wx_plus_b, )
        tf.summary.histogram(layer_name + '/outputs', outputs)
    return outputs


# Make up some real data
x_data = np.linspace(-1, 1, 300)[:, np.newaxis]
noise = np.random.normal(0, 0.05, x_data.shape)
y_data = np.square(x_data) - 0.5 + noise

# define placeholder for inputs to network
with tf.name_scope('inputs'):
    xs = tf.placeholder(tf.float32, [None, 1], name='x_input')
    ys = tf.placeholder(tf.float32, [None, 1], name='y_input')

# add hidden layer
l1 = add_layer(xs, 1, 10, n_layer=1, activation_function=tf.nn.relu)
# add output layer
prediction = add_layer(l1, 10, 1, n_layer=2, activation_function=None)

# the error between prediciton and real data
with tf.name_scope('loss'):
    loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction),
                                        reduction_indices=[1]))
    tf.summary.scalar('loss', loss)

with tf.name_scope('train'):
    train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)

sess = tf.Session()
merged = tf.summary.merge_all()

writer = tf.summary.FileWriter("logs/", sess.graph)

init = tf.global_variables_initializer()
sess.run(init)

for i in range(1000):
    sess.run(train_step, feed_dict={xs: x_data, ys: y_data})
    if i % 50 == 0:
        result = sess.run(merged,
                          feed_dict={xs: x_data, ys: y_data})
        writer.add_summary(result, i)

# direct to the local dir and run this in terminal:
# $ tensorboard --logdir logs