TensorBoard——Tensor与Graph可视化

blake · 发表于 2017-9-18 07:54:56

# -*- coding=utf-8 -*-
# @author: 陈水平
# @date: 2017-02-09
# @description: implement a softmax regression model upon MNIST handwritten digits
# @ref: http://yann.lecun.com/exdb/mnist/
import gzip
import struct
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn import preprocessing
from sklearn.metrics import accuracy_score
import tensorflow as tf
# MNIST data is stored in binary format,
# and we transform them into numpy ndarray objects by the following two utility functions
def read_image(file_name):
with gzip.open(file_name, 'rb') as f:
buf = f.read()
index = 0
magic, images, rows, columns = struct.unpack_from('>IIII' , buf , index)
index += struct.calcsize('>IIII')
image_size = '>' + str(images*rows*columns) + 'B'
ims = struct.unpack_from(image_size, buf, index)
im_array = np.array(ims).reshape(images, rows, columns)
return im_array
def read_label(file_name):
with gzip.open(file_name, 'rb') as f:
buf = f.read()
index = 0
magic, labels = struct.unpack_from('>II', buf, index)
index += struct.calcsize('>II')
label_size = '>' + str(labels) + 'B'
labels = struct.unpack_from(label_size, buf, index)
label_array = np.array(labels)
return label_array
print "Start processing MNIST handwritten digits data..."
train_x_data = read_image("MNIST_data/train-images-idx3-ubyte.gz")
train_x_data = train_x_data.reshape(train_x_data.shape[0], -1).astype(np.float32)
train_y_data = read_label("MNIST_data/train-labels-idx1-ubyte.gz")
test_x_data = read_image("MNIST_data/t10k-images-idx3-ubyte.gz")
test_x_data = test_x_data.reshape(test_x_data.shape[0], -1).astype(np.float32)
test_y_data = read_label("MNIST_data/t10k-labels-idx1-ubyte.gz")
train_x_minmax = train_x_data / 255.0
test_x_minmax = test_x_data / 255.0
# Of course you can also use the utility function to read in MNIST provided by tensorflow
# from tensorflow.examples.tutorials.mnist import input_data
# mnist = input_data.read_data_sets("MNIST_data/", one_hot=False)
# train_x_minmax = mnist.train.images
# train_y_data = mnist.train.labels
# test_x_minmax = mnist.test.images
# test_y_data = mnist.test.labels
# We evaluate the softmax regression model by sklearn first
eval_sklearn = False
if eval_sklearn:
print "Start evaluating softmax regression model by sklearn..."
reg = LogisticRegression(solver="lbfgs", multi_class="multinomial")
reg.fit(train_x_minmax, train_y_data)
np.savetxt('coef_softmax_sklearn.txt', reg.coef_, fmt='%.6f') # Save coefficients to a text file
test_y_predict = reg.predict(test_x_minmax)
print "Accuracy of test set: %f" % accuracy_score(test_y_data, test_y_predict)
eval_tensorflow = True
batch_gradient = False
def variable_summaries(var):
with tf.name_scope('summaries'):
mean = tf.reduce_mean(var)
tf.summary.scalar('mean', mean)
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)
if eval_tensorflow:
print "Start evaluating softmax regression model by tensorflow..."
# reformat y into one-hot encoding style
lb = preprocessing.LabelBinarizer()
lb.fit(train_y_data)
train_y_data_trans = lb.transform(train_y_data)
test_y_data_trans = lb.transform(test_y_data)
x = tf.placeholder(tf.float32, [None, 784])
with tf.name_scope('weights'):
W = tf.Variable(tf.zeros([784, 10]))
variable_summaries(W)
with tf.name_scope('biases'):
b = tf.Variable(tf.zeros([10]))
variable_summaries(b)
with tf.name_scope('Wx_plus_b'):
V = tf.matmul(x, W) + b
tf.summary.histogram('pre_activations', V)
with tf.name_scope('softmax'):
y = tf.nn.softmax(V)
tf.summary.histogram('activations', y)
y_ = tf.placeholder(tf.float32, [None, 10])
with tf.name_scope('cross_entropy'):
loss = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
tf.summary.scalar('cross_entropy', loss)
with tf.name_scope('train'):
optimizer = tf.train.GradientDescentOptimizer(0.5)
train = optimizer.minimize(loss)
with tf.name_scope('evaluate'):
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)
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
merged = tf.summary.merge_all()
train_writer = tf.summary.FileWriter('log/train', sess.graph)
test_writer = tf.summary.FileWriter('log/test')
if batch_gradient:
for step in range(300):
sess.run(train, feed_dict={x: train_x_minmax, y_: train_y_data_trans})
if step % 10 == 0:
print "Batch Gradient Descent processing step %d" % step
print "Finally we got the estimated results, take such a long time..."
else:
for step in range(1000):
if step % 10 == 0:
summary, acc = sess.run([merged, accuracy], feed_dict={x: test_x_minmax, y_: test_y_data_trans})
test_writer.add_summary(summary, step)
print "Stochastic Gradient Descent processing step %d accuracy=%.2f" % (step, acc)
else:
sample_index = np.random.choice(train_x_minmax.shape[0], 100)
batch_xs = train_x_minmax[sample_index, :]
batch_ys = train_y_data_trans[sample_index, :]
summary, _ = sess.run([merged, train], feed_dict={x: batch_xs, y_: batch_ys})
train_writer.add_summary(summary, step)
np.savetxt('coef_softmax_tf.txt', np.transpose(sess.run(W)), fmt='%.6f') # Save coefficients to a text file
print "Accuracy of test set: %f" % sess.run(accuracy, feed_dict={x: test_x_minmax, y_: test_y_data_trans})

复制代码

主要修改点有：

Summary：所有需要在TensorBoard上展示的统计结果。
tf.name_scope()：为Graph中的Tensor添加层级，TensorBoard会按照代码指定的层级进行展示，初始状态下只绘制最高层级的效果，点击后可展开层级看到下一层的细节。
tf.summary.scalar()：添加标量统计结果。
tf.summary.histogram()：添加任意shape的Tensor，统计这个Tensor的取值分布。
tf.summary.merge_all()：添加一个操作，代表执行所有summary操作，这样可以避免人工执行每一个summary op。
tf.summary.FileWrite：用于将Summary写入磁盘，需要制定存储路径logdir，如果传递了Graph对象，则在Graph Visualization会显示Tensor Shape Information。执行summary op后，将返回结果传递给add_summary()方法即可。

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