TensorFlow Tensorboard
本文主要介绍 TensorFlow 的 Tensorboard 模块。
Tensorboard 可以看做是我们构建的 Graph 的可视化工具，对于我们初学者理解网络架构、每层网络的细节都是很有帮助的。由于前几天刚接触 TensorFlow，所以在尝试学习 Tensorboard 的过程中，遇到了一些问题。在此基础上，参考了 TensorFlow 官方的 Tensorboard Tutorials 以及网上的一些文章。由于前不久 TensorFlow 1.0 刚发布，网上的一些学习资源或者是 tensorboard 代码在新的版本中并不适用，所以自己改写并实现了官方网站上提及的三个实例的 Tensorboard 版本：
- 最基础简单的「linear model」
- 基于 MNIST 手写体数据集的 「softmax regression」模型
- 基于 MNIST 手写体数据集的「CNN」模型
文章不会详细介绍 TensorFlow 以及 Tensorboard 的知识，主要是模型的代码以及部分模型实验截图。
注意：文章前提默认读者们知晓 TensorFlow，知晓 Tensorboard，以及 TensorFlow 的一些主要概念「Variables」、「placeholder」。还有，默认你已经将需要用到的 MNIST 数据集下载到了你代码当前所在文件夹。
Environment
OS: macOS Sierra 10.12.x
Python Version: 3.4.x
TensorFlow: 1.0
Tensorboard
Tensorboard有几大模块：
- SCALARS：记录单一变量的，使用
tf.summary.scalar()收集构建。 - IMAGES：收集的图片数据，当我们使用的数据为图片时（选用）。
- AUDIO：收集的音频数据，当我们使用数据为音频时（选用）。
- GRAPHS：构件图，效果图类似流程图一样，我们可以看到数据的流向，使用
tf.name_scope()收集构建。 - DISTRIBUTIONS：用于查看变量的分布值，比如 W（Weights）变化的过程中，主要是在 0.5 附近徘徊。
- HISTOGRAMS：用于记录变量的历史值（比如 weights 值，平均值等），并使用折线图的方式展现，使用
tf.summary.histogram()进行收集构建。
Examples
- 最简单的线性回归模型（tensorboard 绘图）
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
def add_layer(layoutname, inputs, in_size, out_size, act = None):
with tf.name_scope(layoutname):
with tf.name_scope('weights'):
weights = tf.Variable(tf.random_normal([in_size, out_size]), name = 'weights')
w_hist = tf.summary.histogram('weights', weights)
with tf.name_scope('biases'):
biases = tf.Variable(tf.zeros([1, out_size]) + 0.1, name = 'biases')
b_hist = tf.summary.histogram('biases', biases)
with tf.name_scope('Wx_plus_b'):
Wx_plus_b = tf.add(tf.matmul(inputs, weights), biases)
if act is None:
outputs = Wx_plus_b
else :
outputs = act(Wx_plus_b)
return outputs
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
with tf.name_scope('Input'):
xs = tf.placeholder(tf.float32, [None, 1], name = "input_x")
ys = tf.placeholder(tf.float32, [None, 1], name = "target_y")
l1 = add_layer("first_layer", xs, 1, 10, act = tf.nn.relu)
l1_hist = tf.summary.histogram('l1', l1)
y = add_layer("second_layout", l1, 10, 1, act = None)
y_hist = tf.summary.histogram('y', y)
with tf.name_scope('loss'):
loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - y),
reduction_indices = [1]))
tf.summary.histogram('loss ', loss)
tf.summary.scalar('loss', loss)
with tf.name_scope('train'):
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
init = tf.global_variables_initializer()
merged = tf.summary.merge_all()
with tf.Session() as sess:
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.scatter(x_data, y_data)
plt.ion()
plt.show()
writer = tf.summary.FileWriter('logs/', sess.graph)
sess.run(init)
for train in range(1000):
sess.run(train_step, feed_dict = {xs: x_data, ys: y_data})
if train % 50 == 0:
try:
ax.lines.remove(lines[0])
except Exception:
pass
summary_str = sess.run(merged, feed_dict = {xs: x_data, ys: y_data})
writer.add_summary(summary_str, train)
print(train, sess.run(loss, feed_dict = {xs: x_data, ys: y_data}))
prediction_value = sess.run(y, feed_dict = {xs: x_data})
lines = ax.plot(x_data, prediction_value, 'r-', lw = 5)
plt.pause(1)
- 基於 Softmax Regressions 的 MNIST 数据集（tensorboard 绘图）
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
import numpy as np
def add_layer(layoutname, inputs, in_size, out_size, act = None):
with tf.name_scope(layoutname):
with tf.name_scope('weights'):
weights = tf.Variable(tf.zeros([in_size, out_size]), name = 'weights')
w_hist = tf.summary.histogram("weights", weights)
with tf.name_scope('biases'):
biases = tf.Variable(tf.zeros(out_size), name = 'biases')
b_hist = tf.summary.histogram("biases", biases)
with tf.name_scope('Wx_plus_b'):
Wx_plus_b = tf.add(tf.matmul(inputs, weights), biases)
if act is None:
outputs = Wx_plus_b
else:
outputs = act(Wx_plus_b)
return outputs
# Import data
mnist_data_path = 'MNIST_data/'
mnist = input_data.read_data_sets(mnist_data_path, one_hot = True)
with tf.name_scope('Input'):
x = tf.placeholder(tf.float32, [None, 28 * 28], name = 'input_x')
y_ = tf.placeholder(tf.float32, [None, 10], name = 'target_y')
y = add_layer("hidden_layout", x, 28*28, 10, act = tf.nn.softmax)
y_hist = tf.summary.histogram('y', y)
# labels 真实值 logits 预测值
with tf.name_scope('loss'):
cross_entroy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels = y_,
logits = y))
tf.summary.histogram('cross entropy', cross_entroy)
tf.summary.scalar('cross entropy', cross_entroy)
with tf.name_scope('train'):
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entroy)
# Test trained model
with tf.name_scope('test'):
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
tf.summary.scalar('accuracy', accuracy)
init = tf.global_variables_initializer()
merged = tf.summary.merge_all()
with tf.Session() as sess:
#logpath = r'/Users/randolph/PycharmProjects/TensorFlow/logs'
writer = tf.summary.FileWriter('logs/', sess.graph)
sess.run(init)
for i in range(1000):
if i % 10 == 0:
feed = {x: mnist.test.images, y_: mnist.test.labels}
result = sess.run([merged, accuracy], feed_dict = feed)
summary_str = result[0]
acc = result[1]
writer.add_summary(summary_str, i)
print(i, acc)
else:
batch_xs, batch_ys = mnist.train.next_batch(100)
feed = {x: batch_xs, y_: batch_ys}
sess.run(train_step, feed_dict = feed)
print('final result: ', sess.run(accuracy, feed_dict = {x: mnist.test.images, y_: mnist.test.labels}))
- 基於 CNN 的 MNIST 数据集（tensorboard 绘图）
# 基于 MNIST 数据集 的 「CNN」（tensorboard 绘图）
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
import numpy as np
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 conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
def max_pool_2x2(x):
return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
def variable_summaries(var):
"""Attach a lot of summaries to a Tensor (for TensorBoard visualization)."""
with tf.name_scope('summaries'):
mean = tf.reduce_mean(var)
tf.summary.scalar('mean', mean)
with tf.name_scope('stddev'):
stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
tf.summary.scalar('stddev', stddev)
tf.summary.scalar('max', tf.reduce_max(var))
tf.summary.scalar('min', tf.reduce_min(var))
tf.summary.histogram('histogram', var)
def add_layer(input_tensor, weights_shape, biases_shape, layer_name, act = tf.nn.relu, flag = 1):
"""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.
"""
with tf.name_scope(layer_name):
with tf.name_scope('weights'):
weights = weight_variable(weights_shape)
variable_summaries(weights)
with tf.name_scope('biases'):
biases = bias_variable(biases_shape)
variable_summaries(biases)
with tf.name_scope('Wx_plus_b'):
if flag == 1:
preactivate = tf.add(conv2d(input_tensor, weights), biases)
else:
preactivate = tf.add(tf.matmul(input_tensor, weights), biases)
tf.summary.histogram('pre_activations', preactivate)
if act == None:
outputs = preactivate
else:
outputs = act(preactivate, name = 'activation')
tf.summary.histogram('activation', outputs)
return outputs
def main():
# Import data
mnist_data_path = 'MNIST_data/'
mnist = input_data.read_data_sets(mnist_data_path, one_hot = True)
with tf.name_scope('Input'):
x = tf.placeholder(tf.float32, [None, 28*28], name = 'input_x')
y_ = tf.placeholder(tf.float32, [None, 10], name = 'target_y')
# First Convolutional Layer
x_image = tf.reshape(x, [-1, 28, 28 ,1])
conv_1 = add_layer(x_image, [5, 5, 1, 32], [32], 'First_Convolutional_Layer', flag = 1)
# First Pooling Layer
pool_1 = max_pool_2x2(conv_1)
# Second Convolutional Layer
conv_2 = add_layer(pool_1, [5, 5, 32, 64], [64], 'Second_Convolutional_Layer', flag = 1)
# Second Pooling Layer
pool_2 = max_pool_2x2(conv_2)
# Densely Connected Layer
pool_2_flat = tf.reshape(pool_2, [-1, 7*7*64])
dc_1 = add_layer(pool_2_flat, [7*7*64, 1024], [1024], 'Densely_Connected_Layer', flag = 0)
# Dropout
keep_prob = tf.placeholder(tf.float32)
dc_1_drop = tf.nn.dropout(dc_1, keep_prob)
# Readout Layer
y = add_layer(dc_1_drop, [1024, 10], [10], 'Readout_Layer', flag = 0)
# Optimizer
with tf.name_scope('cross_entroy'):
cross_entroy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels = y_,
logits = y))
tf.summary.scalar('cross_entropy', cross_entroy)
tf.summary.histogram('cross_entropy', cross_entroy)
# Train
with tf.name_scope('train'):
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entroy)
# Test
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)
sess = tf.InteractiveSession()
merged = tf.summary.merge_all()
train_writer = tf.summary.FileWriter('train/', sess.graph)
test_writer = tf.summary.FileWriter('test/')
tf.global_variables_initializer().run()
def feed_dict(train):
if train:
batch_xs, batch_ys = mnist.train.next_batch(100)
k = 0.5
else:
batch_xs, batch_ys = mnist.test.images, mnist.test.labels
k = 1.0
return {x: batch_xs, y_: batch_ys, keep_prob: k}
# 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
for i in range(10000):
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("step %d, training accuracy %g" %(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:
summary, _ = sess.run([merged, train_step], feed_dict = feed_dict(True))
train_writer.add_summary(summary, i)
main()
可能对于最后一个模型 CNN 的代码，需要一些 CNN 卷积神经网络的一些知识。例如什么是卷积、池化，还需要了解 TensorFlow 中用到的相应函数，例如tf.nn.conv2d()，tf.nn.max_pool()，这里不再赘述。
贴上最后一个模型的部分截图：
- 代码部分：
说明：上图右侧是 CNN 网络训练的步数以及对应的结果，程序需要运行挺久时间的，CPU 占用率也很高，建议挂在晚上跑，人去休息睡觉。总之，你们可以修改那个 range(10000)，请量力而为。
上述代码运行完成之后，命令行中跳转到代码生成的「train」文件夹中（其和代码文件存在于同一文件夹中），然后输入 tensorboard --logdir .，等待程序反应之后，浏览器访问localhost:6006（当然你也可以自己定义端口）。如果不出意外，你会得到以下内容：
-
Scalars:
-
Graphs:
-
Distributions:
-
Histograms:
关于各个模块的作用，以及各个变量的意义，我在此就不再赘述了。
如果有读者对于 CNN 卷积神经网络有些陌生或者是遗忘，可以参考我的另外一篇文章 CNN on TensorFlow。
另外，如果读者们想在模型训练期间，做些「有趣的」事情，可以参考我的另一篇文章 Use WeChat to Monitor Your Network
