1 #coding:utf-8
2 '''
3 Created by huxiaoman 2017.12.12
4 vggnet.py:用vgg网络实现cifar-10分类
5 '''
6
7 import paddle.v2 as paddle
8
9 def vgg(input):
10 def conv_block(ipt, num_filter, groups, dropouts, num_channels=None):
11 return paddle.networks.img_conv_group(
12 input=ipt,
13 num_channels=num_channels,
14 pool_size=2,
15 pool_stride=2,
16 conv_num_filter=[num_filter] * groups,
17 conv_filter_size=3,
18 conv_act=paddle.activation.Relu(),
19 conv_with_batchnorm=True,
20 conv_batchnorm_drop_rate=dropouts,
21 pool_type=paddle.pooling.Max())
22
23 conv1 = conv_block(input, 64, 2, [0.3, 0], 3)
24 conv2 = conv_block(conv1, 128, 2, [0.4, 0])
25 conv3 = conv_block(conv2, 256, 3, [0.4, 0.4, 0])
26 conv4 = conv_block(conv3, 512, 3, [0.4, 0.4, 0])
27 conv5 = conv_block(conv4, 512, 3, [0.4, 0.4, 0])
28
29 drop = paddle.layer.dropout(input=conv5, dropout_rate=0.5)
30 fc1 = paddle.layer.fc(input=drop, size=512, act=paddle.activation.Linear())
31 bn = paddle.layer.batch_norm(
32 input=fc1,
33 act=paddle.activation.Relu(),
34 layer_attr=paddle.attr.Extra(drop_rate=0.5))
35 fc2 = paddle.layer.fc(input=bn, size=512, act=paddle.activation.Linear())
36 return fc2

1 #coding:utf-8
2 '''
3 Created by huxiaoman 2017.12.12
4 train_vgg.py:训练vgg16对cifar10数据集进行分类
5 '''
6
7 import sys, os
8 import paddle.v2 as paddle
9 from vggnet import vgg
10
11 with_gpu = os.getenv('WITH_GPU', '0') != '1'
12
13 def main():
14 datadim = 3 * 32 * 32
15 classdim = 10
16
17 # PaddlePaddle init
18 paddle.init(use_gpu=with_gpu, trainer

_count=8)
19
20 image = paddle.layer.data(
21 name="image", type=paddle.data_type.

dense_vector(datadim))
22
23 net = vgg(image)
24
25 out = paddle.layer.fc(
26 input=net, size=classdim, act=paddle.

activation.Softmax())
27
28 lbl = paddle.layer.data(
29 name="label", type=paddle.data_type.integer_value(classdim))
30 cost = paddle.layer.classification_cost

(input=out, label=lbl)
31
32 # Create parameters
33 parameters = paddle.parameters.create(cost)
34
35 # Create optimizer
36 momentum_optimizer = paddle.optimizer.

Momentum(
37 momentum=0.9,
38 regularization=paddle.optimizer.L2Regular

ization(rate=0.0002 * 128),
39 learning_rate=0.1 / 128.0,
40 learning_rate_decay_a=0.1,
41 learning_rate_decay_b=50000 * 100,
42 learning_rate_schedule='discexp')
43
44 # End batch and end pass event handler
45 def event_handler(event):
46 if isinstance(event, paddle.event.EndIteration):
47 if event.batch_id % 100 == 0:
48 print "\nPass %d, Batch %d, Cost %f, %s" % (
49 event.pass_id, event.batch_id, event.cost,

event.metrics)
50 else:
51 sys.stdout.write('.')
52 sys.stdout.flush()
53 if isinstance(event, paddle.event.EndPass):
54 # save parameters
55 with open('params_pass_%d.tar' % event.pass

_id, 'w') as f:
56 parameters.to_tar(f)
57
58 result = trainer.test(
59 reader=paddle.batch(
60 paddle.dataset.cifar.test10(), batch_size=128),
61 feeding={'image': 0,
62 'label': 1})
63 print "\nTest with Pass %d, %s" % (event.pass_id, result.metrics)
64
65 # Create trainer
66 trainer = paddle.trainer.SGD(
67 cost=cost, parameters=parameters, update_equation=momentum_optimizer)
68
69 # Save the inference topology to protobuf.
70 inference_topology = paddle.topology.Topology

(layers=out)
71 with open("inference_topology.pkl", 'wb') as f:
72 inference_topology.serialize_for_inference(f)
73
74 trainer.train(
75 reader=paddle.batch(
76 paddle.reader.shuffle(
77 paddle.dataset.cifar.train10(), buf_size

=50000),
78 batch_size=128),
79 num_passes=200,
80 event_handler=event_handler,
81 feeding={'image': 0,
82 'label': 1})
83
84 # inference
85 from PIL import Image
86 import numpy as np
87 import os
88
89 def load_image(file):
90 im = Image.open(file)
91 im = im.resize((32, 32), Image.ANTIALIAS)
92 im = np.array(im).astype(np.float32)
93 im = im.transpose((2, 0, 1)) # CHW
94 im = im[(2, 1, 0), :, :] # BGR
95 im = im.flatten()
96 im = im / 255.0
97 return im
98
99 test_data = []
100 cur_dir = os.path.dirname(os.path.realpath

(__file__))
101 test_data.append((load_image(cur_dir + '/

image/dog.png'), ))
102
103 probs = paddle.infer(
104 output_layer=out, parameters=parameters,

input=test_data)
105 lab = np.argsort(-probs) # probs and lab

are the results of one batch data
106 print "Label of image/dog.png is: %d" %

lab[0][0]
107
108
109 if __name__ == '__main__':
110 main()

1 def inference_op(input_op, keep_prob):
2 p = []
3 # 第一块 conv1_1-conv1_2-pool1
4 conv1_1 = conv_op(input_op, name='conv1_1', kh=3, kw=3,
5 n_out = 64, dh = 1, dw = 1, p = p)
6 conv1_2 = conv_op(conv1_1, name='conv1_2', kh=3, kw=3,
7 n_out = 64, dh = 1, dw = 1, p = p)
8 pool1 = mpool_op(conv1_2, name = 'pool1', kh = 2, kw = 2,
9 dw = 2, dh = 2)
10 # 第二块 conv2_1-conv2_2-pool2
11 conv2_1 = conv_op(pool1, name='conv2_1', kh=3, kw=3,
12 n_out = 128, dh = 1, dw = 1, p = p)
13 conv2_2 = conv_op(conv2_1, name='conv2_2', kh=3, kw=3,
14 n_out = 128, dh = 1, dw = 1, p = p)
15 pool2 = mpool_op(conv2_2, name = 'pool2', kh = 2, kw = 2,
16 dw = 2, dh = 2)
17 # 第三块 conv3_1-conv3_2-conv3_3-pool3
18 conv3_1 = conv_op(pool2, name='conv3_1', kh=3, kw=3,
19 n_out = 256, dh = 1, dw = 1, p = p)
20 conv3_2 = conv_op(conv3_1, name='conv3_2', kh=3, kw=3,
21 n_out = 256, dh = 1, dw = 1, p = p)
22 conv3_3 = conv_op(conv3_2, name='conv3_3', kh=3, kw=3,
23 n_out = 256, dh = 1, dw = 1, p = p)
24 pool3 = mpool_op(conv3_3, name = 'pool3', kh = 2, kw = 2,
25 dw = 2, dh = 2)
26 # 第四块 conv4_1-conv4_2-conv4_3-pool4
27 conv4_1 = conv_op(pool3, name='conv4_1', kh=3, kw=3,
28 n_out = 512, dh = 1, dw = 1, p = p)
29 conv4_2 = conv_op(conv4_1, name='conv4_2', kh=3, kw=3,
30 n_out = 512, dh = 1, dw = 1, p = p)
31 conv4_3 = conv_op(conv4_2, name='conv4_3', kh=3, kw=3,
32 n_out = 512, dh = 1, dw = 1, p = p)
33 pool4 = mpool_op(conv4_3, name = 'pool4', kh = 2, kw = 2,
34 dw = 2, dh = 2)
35 # 第五块 conv5_1-conv5_2-conv5_3-pool5
36 conv5_1 = conv_op(pool4, name='conv5_1', kh=3, kw=3,
37 n_out = 512, dh = 1, dw = 1, p = p)
38 conv5_2 = conv_op(conv5_1, name='conv5_2', kh=3, kw=3,
39 n_out = 512, dh = 1, dw = 1, p = p)
40 conv5_3 = conv_op(conv5_2, name='conv5_3', kh=3, kw=3,
41 n_out = 512, dh = 1, dw = 1, p = p)
42 pool5 = mpool_op(conv5_3, name = 'pool5', kh = 2, kw = 2,
43 dw = 2, dh = 2)
44 # 把pool5 ( [7, 7, 512] ) 拉成向量
45 shp = pool5.get_shape()
46 flattened_shape = shp[1].value * shp[2].value * shp[3].value
47 resh1 = tf.reshape(pool5, [-1, flattened_shape], name = 'resh1')
48
49 # 全连接层1 添加了 Droput来防止过拟合
50 fc1 = fc_op(resh1, name = 'fc1', n_out = 2048, p = p)
51 fc1_drop = tf.nn.dropout(fc1, keep_prob, name = 'fc1_drop')
52
53 # 全连接层2 添加了 Droput来防止过拟合
54 fc2 = fc_op(fc1_drop, name = 'fc2', n_out = 2048, p = p)
55 fc2_drop = tf.nn.dropout(fc2, keep_prob, name = 'fc2_drop')
56
57 # 全连接层3 加一个softmax求给类别的概率
58 fc3 = fc_op(fc2_drop, name = 'fc3', n_out = 1000, p = p)
59 softmax = tf.nn.softmax(fc3)
60 predictions = tf.argmax(softmax, 1)
61 return predictions, softmax, fc3, p
复制代码

1 # -*- coding: utf-8 -*-
2 """
3 Created by huxiaoman 2017.12.12
4 vgg_tf.py:训练tensorflow版的vgg16网络，对cifar-10shuju进行分类
5 """
6 from datetime import datetime
7 import math
8 import time
9 import tensorflow as tf
10 import cifar10
11
12 batch_size = 128
13 num_batches = 200
14
15 # 定义函数对卷积层进行初始化
16 # input_op : 输入数据
17 # name : 该卷积层的名字，用tf.name_scope()来命名
18 # kh,kw : 分别是卷积核的高和宽
19 # n_out : 输出通道数
20 # dh,dw : 步长的高和宽
21 # p ： 是参数列表，存储VGG所用到的参数
22 # 采用xavier方法对卷积核权值进行初始化
23 def conv_op(input_op, name, kh, kw, n_out, dh, dw, p):
24 n_in = input_op.get_shape()[-1].value # 获得输入图像的通道数
25 with tf.name_scope(name) as scope:
26 kernel = tf.get_variable(scope+'w',
27 shape = [kh, kw, n_in, n_out], dtype = tf.float32,
28 initializer = tf.contrib.layers.xavier_initializer_conv2d())
29 # 卷积层计算
30 conv = tf.nn.conv2d(input_op, kernel, (1, dh, dw, 1), padding = 'SAME')
31 bias_init_val = tf.constant(0.0, shape = [n_out], dtype = tf.float32)
32 biases = tf.Variable(bias_init_val, trainable = True, name = 'b')
33 z = tf.nn.bias_add(conv, biases)
34 activation = tf.nn.relu(z, name = scope)
35 p += [kernel, biases]
36 return activation
37
38 # 定义函数对全连接层进行初始化
39 # input_op : 输入数据
40 # name : 该全连接层的名字
41 # n_out : 输出的通道数
42 # p : 参数列表
43 # 初始化方法用 xavier方法
44 def fc_op(input_op, name, n_out, p):
45 n_in = input_op.get_shape()[-1].value
46
47 with tf.name_scope(name) as scope:
48 kernel = tf.get_variable(scope+'w',
49 shape = [n_in, n_out], dtype = tf.float32,
50 initializer = tf.contrib.layers.xavier_initializer())
51 biases = tf.Variable(tf.constant(0.1, shape = [n_out],
52 dtype = tf.float32), name = 'b')
53 activation = tf.nn.relu_layer(input_op, kernel, # ???????????????
54 biases, name = scope)
55 p += [kernel, biases]
56 return activation
57
58 # 定义函数 创建 maxpool层
59 # input_op : 输入数据
60 # name : 该卷积层的名字，用tf.name_scope()来命名
61 # kh,kw : 分别是卷积核的高和宽
62 # dh,dw : 步长的高和宽
63 def mpool_op(input_op, name, kh, kw, dh, dw):
64 return tf.nn.max_pool(input_op, ksize = [1,kh,kw,1],
65 strides = [1, dh, dw, 1], padding = 'SAME', name = name)
66
67 #---------------创建 VGG-16------------------
68
69 def inference_op(input_op, keep_prob):
70 p = []
71 # 第一块 conv1_1-conv1_2-pool1
72 conv1_1 = conv_op(input_op, name='conv1_1', kh=3, kw=3,
73 n_out = 64, dh = 1, dw = 1, p = p)
74 conv1_2 = conv_op(conv1_1, name='conv1_2', kh=3, kw=3,
75 n_out = 64, dh = 1, dw = 1, p = p)
76 pool1 = mpool_op(conv1_2, name = 'pool1', kh = 2, kw = 2,
77 dw = 2, dh = 2)
78 # 第二块 conv2_1-conv2_2-pool2
79 conv2_1 = conv_op(pool1, name='conv2_1', kh=3, kw=3,
80 n_out = 128, dh = 1, dw = 1, p = p)
81 conv2_2 = conv_op(conv2_1, name='conv2_2', kh=3, kw=3,
82 n_out = 128, dh = 1, dw = 1, p = p)
83 pool2 = mpool_op(conv2_2, name = 'pool2', kh = 2, kw = 2,
84 dw = 2, dh = 2)
85 # 第三块 conv3_1-conv3_2-conv3_3-pool3
86 conv3_1 = conv_op(pool2, name='conv3_1', kh=3, kw=3,
87 n_out = 256, dh = 1, dw = 1, p = p)
88 conv3_2 = conv_op(conv3_1, name='conv3_2', kh=3, kw=3,
89 n_out = 256, dh = 1, dw = 1, p = p)
90 conv3_3 = conv_op(conv3_2, name='conv3_3', kh=3, kw=3,
91 n_out = 256, dh = 1, dw = 1, p = p)
92 pool3 = mpool_op(conv3_3, name = 'pool3', kh = 2, kw = 2,
93 dw = 2, dh = 2)
94 # 第四块 conv4_1-conv4_2-conv4_3-pool4
95 conv4_1 = conv_op(pool3, name='conv4_1', kh=3, kw=3,
96 n_out = 512, dh = 1, dw = 1, p = p)
97 conv4_2 = conv_op(conv4_1, name='conv4_2', kh=3, kw=3,
98 n_out = 512, dh = 1, dw = 1, p = p)
99 conv4_3 = conv_op(conv4_2, name='conv4_3', kh=3, kw=3,
100 n_out = 512, dh = 1, dw = 1, p = p)
101 pool4 = mpool_op(conv4_3, name = 'pool4', kh = 2, kw = 2,
102 dw = 2, dh = 2)
103 # 第五块 conv5_1-conv5_2-conv5_3-pool5
104 conv5_1 = conv_op(pool4, name='conv5_1', kh=3, kw=3,
105 n_out = 512, dh = 1, dw = 1, p = p)
106 conv5_2 = conv_op(conv5_1, name='conv5_2', kh=3, kw=3,
107 n_out = 512, dh = 1, dw = 1, p = p)
108 conv5_3 = conv_op(conv5_2, name='conv5_3', kh=3, kw=3,
109 n_out = 512, dh = 1, dw = 1, p = p)
110 pool5 = mpool_op(conv5_3, name = 'pool5', kh = 2, kw = 2,
111 dw = 2, dh = 2)
112 # 把pool5 ( [7, 7, 512] ) 拉成向量
113 shp = pool5.get_shape()
114 flattened_shape = shp[1].value * shp[2].value * shp[3].value
115 resh1 = tf.reshape(pool5, [-1, flattened_shape], name = 'resh1')
116
117 # 全连接层1 添加了 Droput来防止过拟合
118 fc1 = fc_op(resh1, name = 'fc1', n_out = 2048, p = p)
119 fc1_drop = tf.nn.dropout(fc1, keep_prob, name = 'fc1_drop')
120
121 # 全连接层2 添加了 Droput来防止过拟合
122 fc2 = fc_op(fc1_drop, name = 'fc2', n_out = 2048, p = p)
123 fc2_drop = tf.nn.dropout(fc2, keep_prob, name = 'fc2_drop')
124
125 # 全连接层3 加一个softmax求给类别的概率
126 fc3 = fc_op(fc2_drop, name = 'fc3', n_out = 1000, p = p)
127 softmax = tf.nn.softmax(fc3)
128 predictions = tf.argmax(softmax, 1)
129 return predictions, softmax, fc3, p
130
131 # 定义评测函数
132
133 def time_tensorflow_run(session, target, feed, info_string):
134 num_steps_burn_in = 10
135 total_duration = 0.0
136 total_duration_squared = 0.0
137
138 for i in range(num_batches + num_steps_burn_in):
139 start_time = time.time()
140 _ = session.run(target, feed_dict = feed)
141 duration = time.time() - start_time
142 if i >= num_steps_burn_in:
143 if not i % 10:
144 print('%s: step %d, duration = %.3f' %
145 (datetime.now(), i-num_steps_burn_in, duration))
146 total_duration += duration
147 total_duration_squared += duration * duration
148 mean_dur = total_duration / num_batches
149 var_dur = total_duration_squared / num_batches - mean_dur * mean_dur
150 std_dur = math.sqrt(var_dur)
151 print('%s: %s across %d steps, %.3f +/- %.3f sec / batch' %(datetime.now(), info_string, num_batches, mean_dur, std_dur))
152
153
154 def train_vgg16():
155 with tf.Graph().as_default():
156 image_size = 224 # 输入图像尺寸
157 # 生成随机数测试是否能跑通
158 #images = tf.Variable(tf.random_normal([batch_size, image_size, image_size, 3], dtype=tf.float32, stddev=1e-1))
159 with tf.device('/cpu:0'):
160 images, labels = cifar10.distorted_inputs()
161 keep_prob = tf.placeholder(tf.float32)
162 prediction,softmax,fc8,p = inference_op(images,keep_prob)
163 init = tf.global_variables_initializer()
164 sess = tf.Session()
165 sess.run(init)
166 time_tensorflow_run(sess, prediction,{keep_prob:1.0}, "Forward")
167 # 用以模拟训练的过程
168 objective = tf.nn.l2_loss(fc8) # 给一个loss
169 grad = tf.gradients(objective, p) # 相对于loss的 所有模型参数的梯度
170 time_tensorflow_run(sess, grad, {keep_prob:0.5},"Forward-backward")
171
172
173
174
175 if __name__ == '__main__':
176 train_vgg16()

1 def vgg16(inputs):
2 with slim.arg_scope([slim.conv2d, slim.fully_connected],
3 activation_fn=tf.nn.relu,
4 weights_initializer=tf.truncated_normal_

initializer(0.0, 0.01),
5 weights_regularizer=slim.l2_regularizer

(0.0005)):
6 net = slim.repeat(inputs, 2, slim.conv2d,

64, [3, 3], scope='conv1')
7 net = slim.max_pool2d(net, [2, 2], scope='

pool1')
8 net = slim.repeat(net, 2, slim.conv2d, 128,

[3, 3], scope='conv2')
9 net = slim.max_pool2d(net, [2, 2], scope='

pool2')
10 net = slim.repeat(net, 3, slim.conv2d, 256,

[3, 3], scope='conv3')
11 net = slim.max_pool2d(net, [2, 2], scope='

pool3')
12 net = slim.repeat(net, 3, slim.conv2d, 512,

[3, 3], scope='conv4')
13 net = slim.max_pool2d(net, [2, 2], scope=

'pool4')
14 net = slim.repeat(net, 3, slim.conv2d, 512,

[3, 3], scope='conv5')
15 net = slim.max_pool2d(net, [2, 2], scope='

pool5')
16 net = slim.fully_connected(net, 4096,

scope='fc6')
17 net = slim.dropout(net, 0.5, scope='

dropout6')
18 net = slim.fully_connected(net, 4096,

scope='fc7')
19 net = slim.dropout(net, 0.5, scope='

dropout7')
20 net = slim.fully_connected(net, 1000, activation_fn=None, scope='fc8')