昨天突击了这个教程的前20课，记一些笔记，整理下收获。学到的东西主要体现在下面的代码和其中的注释中。对tensorboard的用法我再在下方补一些内容。
代码：
# This program simulates y = x^2 - 0.5
# Our leading actor
import tensorflow as tf
# In this program, array structure in numpy is used
import numpy as np
# This module is used to plot training process
import matplotlib.pyplot as plt
# This function add layer to current network
# Inputs represents a layer
# in_size represents how many attributes we care in the input layer
# same for out_size
# n_layer is an integer, hepling to generate layer name for tensorboard output
# Activation function "activate" certain output. Using it according to situation
def add_layer(inputs, in_size, out_size, n_layer, activation_function=None):
# The usage of % is just like printf() in C language
layer_name = "layer%s" % n_layer
# Use namescope to help generate graph in tensorboard
with tf.name_scope("layer"):
# Ditto, omit this usage in following code
with tf.name_scope("weights"):
# random_normal is in tensorflow module, generating random number according to
# normal distribution
# Use [a, b] to represent a a*b matrix
# Assign string to name argument for visualization
weights = tf.Variable(tf.random_normal([in_size, out_size]), name="W")
# Use summary.histogram([name_string], variable) to show this variable in tensorboard's graph
tf.summary.histogram('/weights', weights)
with tf.name_scope("bias"):
# Use zeros function to fill zeros in a vector or matrix
# Suggested to use non-zero val for bias initially
biases = tf.Variable(tf.zeros([1, out_size]) + 0.1, name="b")
# Also is
tf.summary.histogram('/biases', biases)
# Recall matrix multiplication rules and apply it to here:
# A 1*in_size vector multiplies in_size*out_size matrix, then plus a 1*out_size vector
# Get the result: a 1*out_size vector. Think horizontally.
with tf.name_scope("Wx_plus_b"):
wx_plus_b = tf.matmul(inputs, weights) + biases
if(activation_function is None):
outputs = wx_plus_b
else:
outputs = activation_function(wx_plus_b)
tf.summary.histogram('/outputs', outputs)
return outputs
# Get 300 points in [-1, 1] on x axis
# Use np.newaxis to ascend dimension
# View it as a convension: if you use the one-dimension data for following
# matrix calculation, then you need to use newaxis method to reshape the array
# Like adding dimension after ":"
x_data = np.linspace(-1, 1, 300)[:, np.newaxis]
# Genereate corresponding decimal as bias
noise = np.random.normal(0, 0.05, x_data.shape)
# The theoretical value of y
y_data = np.square(x_data) - 0.5 + noise
with tf.name_scope("inputs"):
# [None, 1]: Ths input matrix's row number is mutable, and column number is 1
xs = tf.placeholder(tf.float32, [None, 1], name="x_input")
ys = tf.placeholder(tf.float32, [None, 1], name="y_input")
# Input size is equal to the number of attributes of data
# L1's output is to the hidden layer, so out_size equals to 10
l1 = add_layer(xs, 1, 10, n_layer=-1, activation_function=tf.nn.relu)
prediction = add_layer(l1, 10, 1, n_layer=2)
with tf.name_scope("loss"):
# The basic theory is LMSE
# reduction_indices=[1] means get rows' sum. =[0] means get cols' sum
# The standard understanding is to reduce the dimension of input tensor
# 0 represents the hightest dimension, 1, 2 likewise. The default argument is to reduce dimension to 0,
# which is a constant num
loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction), reduction_indices=[1]))
tf.summary.scalar("loss/", loss)
with tf.name_scope("train"):
# 0.1 is learning rate. If it's too big, then the network is unable to reach the optimized solution,
# otherwise the network may converges too slow.
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
# As long as you defined any Variable in tensorflow,
# u need to use this function to initialize all variables
init = tf.global_variables_initializer()
# Use with syntax to omit sess.close()
with tf.Session() as sess:
# These 2 are used for visualization
merged = tf.summary.merge_all()
writer = tf.summary.FileWriter("logs/", sess.graph)
# View sess as a pointer, points sess to init and "dereference" it
sess.run(init)
fig = plt.figure()
# Divide the canvas to 1*1 and plot in the 1st space
ax = fig.add_subplot(1, 1, 1)
# scatter function is used to specify input data in 2 dimensions
ax.scatter(x_data, y_data)
# ion function forces painter to paint without waiting interaction
plt.ion()
plt.show()
# Train the network for 1000 times
for i in range(1000):
# For now, just remember these visualization statements...
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)
# Points sess to loss and run it
print(sess.run(loss, feed_dict={xs:x_data, ys:y_data}))
# Use try-except to catch the error raised by first draw(At that time, no line for deletion)
try:
# Remove previous line each time start drawing for better looking
ax.lines.remove(lines[0])
except Exception:
pass
# Points sess to prediction and run it
prediction_val = sess.run(prediction, feed_dict={xs:x_data})
# "-": solid line "r": red color lw: line width
lines = ax.plot(x_data, prediction_val, "r-", lw=5)
# Use pause to see the drawing process
plt.pause(0.1)
tensorboard的使用：
- 有些时候因为防火墙等某些奇怪原因，搞的用它默认的0.0.0.0无法连接上。这时候在原来命令的后方加上这个flag：
--host=127.0.0.1
然后在浏览器里就用127.0.0.1这个地址去连接。
- windows CMD下，–logdir= 后面跟上的路径不需要双引号。
- 自己尝试的时候，并没有按照教程，把shell的当前路径进入到logs文件夹里面，而是在它上一层目录，运行tensorboard的命令。这样自己认为更make sense吧。
最后放几个有关这个网络的截图：
