-
Notifications
You must be signed in to change notification settings - Fork 1
/
Mnist-12.py
170 lines (138 loc) · 5.76 KB
/
Mnist-12.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
# -*- coding: utf-8 -*-
# http://blog.csdn.net/u010089444/article/details/60963053
# python3.4
import tensorflow as tf
from tensorflow.contrib import rnn
import numpy as np
# import input_data
from tensorflow.examples.tutorials.mnist import input_data
# configuration variables
input_vec_size = lstm_size = 28 # 输入向量的维度
time_step_size = 28 # 循环层长度
batch_size = 128
test_size = 256
def init_weights(shape):
return tf.Variable(tf.random_normal(shape, stddev=0.01))
def model(X, W, B, lstm_size):
# X, input shape: (batch_size, time_step_size, input_vec_size)
# XT shape: (time_step_size, batch_size, input_vec_size)
XT = tf.transpose(X, [1, 0, 2]) # permute time_step_size and batch_size,[28, 128, 28]
# XR shape: (time_step_size * batch_size, input_vec_size)
XR = tf.reshape(XT, [-1, lstm_size]) # each row has input for each lstm cell (lstm_size=input_vec_size)
# Each array shape: (batch_size, input_vec_size)
X_split = tf.split(XR, time_step_size, 0) # split them to time_step_size (28 arrays),shape = [(128, 28),(128, 28)...]
# Make lstm with lstm_size (each input vector size). num_units=lstm_size; forget_bias=1.0
lstm = rnn.BasicLSTMCell(lstm_size, forget_bias=1.0, state_is_tuple=True)
# Get lstm cell output, time_step_size (28) arrays with lstm_size output: (batch_size, lstm_size)
# rnn..static_rnn()的输出对应于每一个timestep，如果只关心最后一步的输出，取outputs[-1]即可
outputs, _states = rnn.static_rnn(lstm, X_split, dtype=tf.float32) # 时间序列上每个Cell的输出:[... shape=(128, 28)..]
# Linear activation
# Get the last output
return tf.matmul(outputs[-1], W) + B, lstm.state_size # State size to initialize the stat
# 载入数据集
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) # 读取数据
# mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
# mnist.train.images是一个55000 * 784维的矩阵, mnist.train.labels是一个55000 * 10维的矩阵
trX, trY, teX, teY = mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labels
# 将每张图用一个28x28的矩阵表示,(55000,28,28,1)
trX = trX.reshape(-1, 28, 28)
teX = teX.reshape(-1, 28, 28)
X = tf.placeholder("float", [None, 28, 28])
Y = tf.placeholder("float", [None, 10])
# get lstm_size and output 10 labels
W = init_weights([lstm_size, 10]) # 输出层权重矩阵28×10
B = init_weights([10]) # 输出层bais
py_x, state_size = model(X, W, B, lstm_size)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=py_x, labels=Y))
train_op = tf.train.RMSPropOptimizer(0.001, 0.9).minimize(cost)
predict_op = tf.argmax(py_x, 1)
session_conf = tf.ConfigProto()
session_conf.gpu_options.allow_growth = True
# Launch the graph in a session
with tf.Session(config=session_conf) as sess:
# you need to initialize all variables
tf.global_variables_initializer().run()
for i in range(100):
for start, end in zip(range(0, len(trX), batch_size), range(batch_size, len(trX)+1, batch_size)):
sess.run(train_op, feed_dict={X: trX[start:end], Y: trY[start:end]})
test_indices = np.arange(len(teX)) # Get A Test Batch
np.random.shuffle(test_indices)
test_indices = test_indices[0:test_size]
print(i, np.mean(np.argmax(teY[test_indices], axis=1) ==
sess.run(predict_op, feed_dict={X: teX[test_indices]})))
# configuration
# O * W + b -> 10 labels for each image, O[? 28], W[28 10], B[10]
# ^ (O: output 28 vec from 28 vec input)
# |
# +-+ +-+ +--+
# |1|->|2|-> ... |28| time_step_size = 28
# +-+ +-+ +--+
# ^ ^ ... ^
# | | |
# img1:[28] [28] ... [28]
# img2:[28] [28] ... [28]
# img3:[28] [28] ... [28]
# ...
# img128 or img256 (batch_size or test_size 256)
# each input size = input_vec_size=lstm_size=28
# 没有rnn模块 #python2.7-tensorflow-V0.80
# /usr/bin/python3.4 /home/chuwei/PycharmProjects/MNIST/Mnist-12.py
# Extracting MNIST_data/train-images-idx3-ubyte.gz
# Extracting MNIST_data/train-labels-idx1-ubyte.gz
# Extracting MNIST_data/t10k-images-idx3-ubyte.gz
# Extracting MNIST_data/t10k-labels-idx1-ubyte.gz
# 2017-11-28 10:47:04.917418: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
# 2017-11-28 10:47:04.917494: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
# 2017-11-28 10:47:04.917511: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
# 0 0.57421875
# 1 0.84375
# 2 0.84765625
# 3 0.921875
# 4 0.92578125
# 5 0.921875
# 6 0.94921875
# 7 0.95703125
# 8 0.953125
# 9 0.9375
# 10 0.94921875
# 11 0.9375
# 12 0.9453125
# 13 0.96484375
# 14 0.9609375
# 15 0.95703125
# 16 0.96875
# 17 0.95703125
# 18 0.96484375
# 19 0.953125
# 20 0.9765625
# 21 0.9609375
# 22 0.96484375
# 23 0.9765625
# 24 0.97265625
# 25 0.98046875
# 26 0.97265625
# 27 0.94921875
# 28 0.98046875
# 29 0.98828125
# 30 0.96875
# 31 0.97265625
# 32 0.9765625
# 33 0.96875
# 34 0.98046875
# 35 0.96875
# 36 0.9765625
# 37 0.99609375
# 38 0.98046875
# 39 0.984375
# 40 0.98828125
# 41 0.97265625
# 42 0.984375
# 43 0.9765625
# 44 0.96875
# 45 0.96875
# 46 0.984375
# 47 0.9609375
# 48 0.984375
# 49 0.9765625
# 50 0.9765625
# 未完待续.....