Skip to content
Navigation Menu
{{ message }}
forked from tensorlayer/TensorLayer
-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathtutorial_cifar10.py
More file actions
205 lines (178 loc) · 10.4 KB
/
Copy pathtutorial_cifar10.py
File metadata and controls
205 lines (178 loc) · 10.4 KB
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
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
#! /usr/bin/python
# -*- coding: utf8 -*-
""" tl.prepro for data augmentation """
import tensorflow as tf
import tensorlayer as tl
from tensorlayer.layers import *
import numpy as np
import time, os, io
from PIL import Image
sess = tf.InteractiveSession()
X_train, y_train, X_test, y_test = tl.files.load_cifar10_dataset(
shape=(-1, 32, 32, 3), plotable=False)
def model(x, y_, reuse):
W_init = tf.truncated_normal_initializer(stddev=5e-2)
W_init2 = tf.truncated_normal_initializer(stddev=0.04)
b_init2 = tf.constant_initializer(value=0.1)
with tf.variable_scope("model", reuse=reuse):
tl.layers.set_name_reuse(reuse)
net = InputLayer(x, name='input')
net = Conv2d(net, 64, (5, 5), (1, 1), padding='SAME',
W_init=W_init, name='cnn1')
# net = Conv2dLayer(net, act=tf.nn.relu, shape=[5, 5, 3, 64],
# strides=[1, 1, 1, 1], padding='SAME', # 64 features for each 5x5x3 patch
# W_init=W_init, name ='cnn1') # output: (batch_size, 24, 24, 64)
net = MaxPool2d(net, (3, 3), (2, 2), padding='SAME',name='pool1')
# net = PoolLayer(net, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1],
# padding='SAME', pool = tf.nn.max_pool, name ='pool1',)# output: (batch_size, 12, 12, 64)
net = LocalResponseNormLayer(net, depth_radius=4, bias=1.0,
alpha=0.001 / 9.0, beta=0.75, name='norm1')
# net.outputs = tf.nn.lrn(net.outputs, 4, bias=1.0, alpha=0.001 / 9.0,
# beta=0.75, name='norm1')
net = Conv2d(net, 64, (5, 5), (1, 1), padding='SAME',
W_init=W_init, name='cnn2')
# net = Conv2dLayer(net, act=tf.nn.relu, shape=[5, 5, 64, 64],
# strides=[1, 1, 1, 1], padding='SAME', # 64 features for each 5x5 patch
# W_init=W_init, name ='cnn2') # output: (batch_size, 12, 12, 64)
net = LocalResponseNormLayer(net, depth_radius=4, bias=1.0,
alpha=0.001 / 9.0, beta=0.75, name='norm2')
# net.outputs = tf.nn.lrn(net.outputs, 4, bias=1.0, alpha=0.001 / 9.0,
# beta=0.75, name='norm2')
net = MaxPool2d(net, (3, 3), (2, 2), padding='SAME',name='pool2')
# net = PoolLayer(net, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1],
# padding='SAME', pool = tf.nn.max_pool, name ='pool2') # output: (batch_size, 6, 6, 64)
net = FlattenLayer(net, name='flatten') # output: (batch_size, 2304)
net = DenseLayer(net, n_units=384, act=tf.nn.relu,
W_init=W_init2, b_init=b_init2, name='relu1') # output: (batch_size, 384)
net = DenseLayer(net, n_units=192, act=tf.nn.relu,
W_init=W_init2, b_init=b_init2, name='relu2') # output: (batch_size, 192)
net = DenseLayer(net, n_units=10, act=tf.identity,
W_init=tf.truncated_normal_initializer(stddev=1/192.0),
name='output') # output: (batch_size, 10)
y = net.outputs
ce = tl.cost.cross_entropy(y, y_, name='cost')
# L2 for the MLP, without this, the accuracy will be reduced by 15%.
L2 = tf.contrib.layers.l2_regularizer(0.004)(net.all_params[4]) + \
tf.contrib.layers.l2_regularizer(0.004)(net.all_params[6])
cost = ce + L2
correct_prediction = tf.equal(tf.argmax(y, 1), y_)
acc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
return net, cost, acc
def model_batch_norm(x, y_, reuse, is_train):
""" Batch normalization should be placed before rectifier. """
W_init = tf.truncated_normal_initializer(stddev=5e-2)
W_init2 = tf.truncated_normal_initializer(stddev=0.04)
b_init2 = tf.constant_initializer(value=0.1)
with tf.variable_scope("model", reuse=reuse):
tl.layers.set_name_reuse(reuse)
net = InputLayer(x, name='input')
net = Conv2d(net, 64, (5, 5), (1, 1), padding='SAME',
W_init=W_init, b_init=None, name='cnn1')
# net = Conv2dLayer(net, act=tf.identity, shape=[5, 5, 3, 64],
# strides=[1, 1, 1, 1], padding='SAME', # 64 features for each 5x5x3 patch
# W_init=W_init, b_init=None, name='cnn1') # output: (batch_size, 24, 24, 64)
net = BatchNormLayer(net, is_train, act=tf.nn.relu, name='batch1')
net = MaxPool2d(net, (3, 3), (2, 2), padding='SAME',name='pool1')
# net = PoolLayer(net, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1],
# padding='SAME', pool=tf.nn.max_pool, name='pool1',) # output: (batch_size, 12, 12, 64)
net = Conv2d(net, 64, (5, 5), (1, 1), padding='SAME',
W_init=W_init, b_init=None, name='cnn2')
# net = Conv2dLayer(net, act=tf.identity, shape=[5, 5, 64, 64],
# strides=[1, 1, 1, 1], padding='SAME', # 64 features for each 5x5 patch
# W_init=W_init, b_init=None, name ='cnn2') # output: (batch_size, 12, 12, 64)
net = BatchNormLayer(net, is_train, act=tf.nn.relu, name='batch2')
net = MaxPool2d(net, (3, 3), (2, 2), padding='SAME',name='pool2')
# net = PoolLayer(net, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1],
# padding='SAME', pool = tf.nn.max_pool, name ='pool2') # output: (batch_size, 6, 6, 64)
net = FlattenLayer(net, name='flatten') # output: (batch_size, 2304)
net = DenseLayer(net, n_units=384, act=tf.nn.relu,
W_init=W_init2, b_init=b_init2, name='d1relu') # output: (batch_size, 384)
net = DenseLayer(net, n_units=192, act = tf.nn.relu,
W_init=W_init2, b_init=b_init2, name='d2relu') # output: (batch_size, 192)
net = DenseLayer(net, n_units=10, act = tf.identity,
W_init=tf.truncated_normal_initializer(stddev=1/192.0),
name='output') # output: (batch_size, 10)
y = net.outputs
ce = tl.cost.cross_entropy(y, y_, name='cost')
# L2 for the MLP, without this, the accuracy will be reduced by 15%.
L2 = 0
for p in tl.layers.get_variables_with_name('relu/W', True, True):
L2 += tf.contrib.layers.l2_regularizer(0.004)(p)
cost = ce + L2
correct_prediction = tf.equal(tf.argmax(y, 1), y_)
acc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
return net, cost, acc
def distort_fn(x, is_train=False):
"""
Description
-----------
The images are processed as follows:
.. They are cropped to 24 x 24 pixels, centrally for evaluation or randomly for training.
.. They are approximately whitened to make the model insensitive to dynamic range.
For training, we additionally apply a series of random distortions to
artificially increase the data set size:
.. Randomly flip the image from left to right.
.. Randomly distort the image brightness.
"""
# print('begin',x.shape, np.min(x), np.max(x))
x = tl.prepro.crop(x, 24, 24, is_random=is_train)
# print('after crop',x.shape, np.min(x), np.max(x))
if is_train:
# x = tl.prepro.zoom(x, zoom_range=(0.9, 1.0), is_random=True)
# print('after zoom', x.shape, np.min(x), np.max(x))
x = tl.prepro.flip_axis(x, axis=1, is_random=True)
# print('after flip',x.shape, np.min(x), np.max(x))
x = tl.prepro.brightness(x, gamma=0.1, gain=1, is_random=True)
# print('after brightness',x.shape, np.min(x), np.max(x))
# tmp = np.max(x)
# x += np.random.uniform(-20, 20)
# x /= tmp
# normalize the image
x = (x - np.mean(x)) / max(np.std(x), 1e-5) # avoid values divided by 0
# print('after norm', x.shape, np.min(x), np.max(x), np.mean(x))
return x
x = tf.placeholder(tf.float32, shape=[None, 24, 24, 3], name='x')
y_ = tf.placeholder(tf.int64, shape=[None, ], name='y_')
## using local response normalization
# network, cost, _ = model(x, y_, False)
# _, cost_test, acc = model(x, y_, True)
## you may want to try batch normalization
network, cost, _ = model_batch_norm(x, y_, False, is_train=True)
_, cost_test, acc = model_batch_norm(x, y_, True, is_train=False)
## train
n_epoch = 50000
learning_rate = 0.0001
print_freq = 1
batch_size = 128
train_params = network.all_params
train_op = tf.train.AdamOptimizer(learning_rate, beta1=0.9, beta2=0.999,
epsilon=1e-08, use_locking=False).minimize(cost, var_list=train_params)
tl.layers.initialize_global_variables(sess)
network.print_params(False)
network.print_layers()
print(' learning_rate: %f' % learning_rate)
print(' batch_size: %d' % batch_size)
for epoch in range(n_epoch):
start_time = time.time()
for X_train_a, y_train_a in tl.iterate.minibatches(
X_train, y_train, batch_size, shuffle=True):
X_train_a = tl.prepro.threading_data(X_train_a, fn=distort_fn, is_train=True) # data augmentation for training
sess.run(train_op, feed_dict={x: X_train_a, y_: y_train_a})
if epoch + 1 == 1 or (epoch + 1) % print_freq == 0:
print("Epoch %d of %d took %fs" % (epoch + 1, n_epoch, time.time() - start_time))
# train_loss, train_acc, n_batch = 0, 0, 0
# for X_train_a, y_train_a in tl.iterate.minibatches(
# X_train, y_train, batch_size, shuffle=True):
# X_train_a = tl.prepro.threading_data(X_train_a, fn=distort_fn, is_train=False) # central crop
# err, ac = sess.run([cost_test, acc], feed_dict={x: X_train_a, y_: y_train_a})
# train_loss += err; train_acc += ac; n_batch += 1
# print(" train loss: %f" % (train_loss/ n_batch))
# print(" train acc: %f" % (train_acc/ n_batch))
test_loss, test_acc, n_batch = 0, 0, 0
for X_test_a, y_test_a in tl.iterate.minibatches(
X_test, y_test, batch_size, shuffle=False):
X_test_a = tl.prepro.threading_data(X_test_a, fn=distort_fn, is_train=False) # central crop
err, ac = sess.run([cost_test, acc], feed_dict={x: X_test_a, y_: y_test_a})
test_loss += err; test_acc += ac; n_batch += 1
print(" test loss: %f" % (test_loss/ n_batch))
print(" test acc: %f" % (test_acc/ n_batch))
You can’t perform that action at this time.