本文将介绍在Tensorflow中使用预训练的inception_resnet_v2的详细情况，特别是关于tensorflowserving预处理的相关信息。我们将通过案例分析、数据研究等多种方式，帮
本文将介绍在Tensorflow中使用预训练的inception_resnet_v2的详细情况，特别是关于tensorflow serving 预处理的相关信息。我们将通过案例分析、数据研究等多种方式，帮助您更全面地了解这个主题，同时也将涉及一些关于inception v1(googlenet)--tensorflow version with tensorboard、python – 为什么在tensorflow中构建resnet模型时使用固定填充、python – 如何在Tensorflow中加载预训练的LSTM模型权重、tensorflow - 用 DASC 结合 Inception-v3 对 imagenet2012 聚类实现的知识。
本文目录一览：- 在Tensorflow中使用预训练的inception_resnet_v2（tensorflow serving 预处理）
- inception v1(googlenet)--tensorflow version with tensorboard
- python – 为什么在tensorflow中构建resnet模型时使用固定填充
- python – 如何在Tensorflow中加载预训练的LSTM模型权重
- tensorflow - 用 DASC 结合 Inception-v3 对 imagenet2012 聚类实现
在Tensorflow中使用预训练的inception_resnet_v2（tensorflow serving 预处理）
我一直在尝试使用Google发布的经过预先训练的inception_resnet_v2模型。我正在使用他们的模型定义（https://github.com/tensorflow/models/blob/master/slim/nets/inception_resnet_v2.py）和给定的检查点（http://download.tensorflow.org/models/inception_resnet_v2_2016_08_30.tar
.gz）将模型加载到tensorflow中，如下所示[下载提取检查点文件并下载示例图像dog.jpg和panda.jpg来测试此代码]-
import tensorflow as tfslim = tf.contrib.slimfrom PIL import Imagefrom inception_resnet_v2 import *import numpy as npcheckpoint_file = ''inception_resnet_v2_2016_08_30.ckpt''sample_images = [''dog.jpg'', ''panda.jpg'']#Load the modelsess = tf.Session()arg_scope = inception_resnet_v2_arg_scope()with slim.arg_scope(arg_scope): logits, end_points = inception_resnet_v2(input_tensor, is_training=False)saver = tf.train.Saver()saver.restore(sess, checkpoint_file)for image in sample_images: im = Image.open(image).resize((299,299)) im = np.array(im) im = im.reshape(-1,299,299,3) predict_values, logit_values = sess.run([end_points[''Predictions''], logits], feed_dict={input_tensor: im}) print (np.max(predict_values), np.max(logit_values)) print (np.argmax(predict_values), np.argmax(logit_values))但是，此模型代码的结果并未给出预期的结果（与输入图像无关，将预测类别918）。有人可以帮助我了解我要去哪里哪里吗？
答案1
小编典典盗梦空间网络期望输入图像具有从[-1，1]缩放的颜色通道。如这里所见。
您可以使用现有的预处理，也可以在示例中自行缩放图像：将图像im = 2*(im/255.0)-1.0馈送到网络之前。
如果不缩放比例，则输入[0-255]会比网络预期的要大得多，并且所有偏差都会非常强烈地预测类别918（漫画书）。
inception v1(googlenet)--tensorflow version with tensorboard
实现一个tensorflow（v>1.0）版本的inception v1，带有tensorboard。V4版本也将会很快出来。
代码详细地址：https://github.com/gu-yan/mlAlgorithms
inception v1的原论文：https://arxiv.org/abs/1409.4842v1
python – 为什么在tensorflow中构建resnet模型时使用固定填充
Tensorflow在github中正式实现了resnet.它使用固定填充而不是普通的tf.layers.conv2d.
像这样的东西：
def conv2d_fixed_padding(inputs,filters,kernel_size,strides,data_format):
"""Strided 2-D convolution with explicit padding."""
# The padding is consistent and is based only on `kernel_size`,not on the
# dimensions of `inputs` (as opposed to using `tf.layers.conv2d` alone).
if strides > 1:
inputs = fixed_padding(inputs,data_format)
return tf.layers.conv2d(
inputs=inputs,filters=filters,kernel_size=kernel_size,strides=strides,padding=('SAME' if strides == 1 else 'VALID'),use_bias=False,kernel_initializer=tf.variance_scaling_initializer(),data_format=data_format)
这样做的目的是什么？如果我们输入一个大小为32×32的图像并使用tf.layer.conv2d设置填充方法到SAME,我们可以得到一个16×16的特征映射,步长为2.但在上面的代码中,它将在图像的两侧填充零,然后使用填充方法VALID.
最佳答案
假设我们的步幅为2,内核大小为3.
使用带填充的tf.layers.conv2d相同：
情况1：
pad| |pad
inputs: 0 |1 2 3 4 5 |0
|_______|
|_______|
|_______|
案例2：
|pad
inputs: 1 2 3 4 5 6 |0
|_______|
|_______|
|_______|
您可以看到填充将取决于输入大小.确定具有相同的填充,使得输出大小为Math.ceil(input_size / stride).您可以阅读有关该here的更多信息.
使用resnet的固定填充实现：
情况1：
pad| |pad
inputs: 0 |1 2 3 4 5 |0
|_______|
|_______|
|_______|
案例2：
pad| |pad
inputs: 0 |1 2 3 4 5 6 |0
|_______|
|_______|
|_______|
填充由内核大小唯一定义,并且与输入大小无关.
python – 如何在Tensorflow中加载预训练的LSTM模型权重
我想在Tensorflow中实现具有预训练权重的LSTM模型.这些重量可能来自Caffee或Torch.
我发现文件rnn_cell.py中有LSTM单元,例如rnn_cell.BasicLSTMCell和rnn_cell.MultiRNNCell.但是如何为这些LSTM单元加载预训练的权重.
我发现文件rnn_cell.py中有LSTM单元,例如rnn_cell.BasicLSTMCell和rnn_cell.MultiRNNCell.但是如何为这些LSTM单元加载预训练的权重.
解决方法
这是一个加载预先训练的Caffe模型的解决方案.见
full code here,在
this thread的讨论中引用.
net_caffe = caffe.Net(prototxt,caffemodel,caffe.TEST)
caffe_layers = {}
for i,layer in enumerate(net_caffe.layers):
layer_name = net_caffe._layer_names[i]
caffe_layers[layer_name] = layer
def caffe_weights(layer_name):
layer = caffe_layers[layer_name]
return layer.blobs[0].data
def caffe_bias(layer_name):
layer = caffe_layers[layer_name]
return layer.blobs[1].data
#tensorflow uses [filter_height,filter_width,in_channels,out_channels] 2-3-1-0
#caffe uses [out_channels,filter_height,filter_width] 0-1-2-3
def caffe2tf_filter(name):
f = caffe_weights(name)
return f.transpose((2,3,1,0))
class ModelFromCaffe():
def get_conv_filter(self,name):
w = caffe2tf_filter(name)
return tf.constant(w,dtype=tf.float32,name="filter")
def get_bias(self,name):
b = caffe_bias(name)
return tf.constant(b,name="bias")
def get_fc_weight(self,name):
cw = caffe_weights(name)
if name == "fc6":
assert cw.shape == (4096,25088)
cw = cw.reshape((4096,512,7,7))
cw = cw.transpose((2,0))
cw = cw.reshape(25088,4096)
else:
cw = cw.transpose((1,0))
return tf.constant(cw,name="weight")
images = tf.placeholder("float",[None,224,3],name="images")
m = ModelFromCaffe()
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
batch = cat.reshape((1,3))
out = sess.run([m.prob,m.relu1_1,m.pool5,m.fc6],Feed_dict={ images: batch })
...
tensorflow - 用 DASC 结合 Inception-v3 对 imagenet2012 聚类实现
一、目的
以 imagenet2012 作为数据集，用 Inception-v3 对图像提取特征作为输入，来训练一个自编码器。
以上作为预训练模型，随后在该自编码器的基础上，中间加入一个自表示层，将最终学习到的自表示层系数，作为相似度矩阵，对 imagenet2012 的 1000 类进行聚类。
二、预训练
1. 原理
inception-v3 + 自编码器
2. 代码
1 import tensorflow as tf
2 import os
3 import numpy as np
4 import random
5 import tensorflow.contrib.slim as slim
6 import shutil
7
8 tf.app.flags.DEFINE_string(''model_dir'', ''model/inception'', ''Inception-v3 pretrain model dir'')
9 tf.app.flags.DEFINE_string(''class_list'', ''imagenet12/train_class_list.txt'', ''ILSVRC2012 image class list'')
10 tf.app.flags.DEFINE_string(''img_path'', ''/media/gpu/bdc7606d-0e3c-4870-9a5d-4926fd9961c0/gpu/Works/imagenet/others/ILSVRC2012_img_train'', ''ILSVRC2012 image train path'')
11 tf.app.flags.DEFINE_integer(''max_train_steps_pre'', 200000, ''max train num'')
12 tf.app.flags.DEFINE_boolean(''restore'', True, ''wheather restore model and variable from previous saved'')
13 tf.app.flags.DEFINE_string(''checkpoint_path'', ''model/pre/'', ''model saved path'')
14 tf.app.flags.DEFINE_string(''feature_train_path'',''feature_train'',''ILSVRC2012 train feature save path'')
15 tf.app.flags.DEFINE_integer(''large_multi'', 100, ''enlarge the feature data'')
16 tf.app.flags.DEFINE_integer(''width'', 32, ''the width of feature input'')
17 tf.app.flags.DEFINE_integer(''inception_out_size'', 2048, ''the dim of feature input,inception out dim'')
18 tf.app.flags.DEFINE_integer(''train_num_of_every_batch'', 2000, ''change the data every 2000 epochs'')
19 FLAGS = tf.app.flags.FLAGS
20
21 kernel_num_list = [16, 32, 64] #channel num
22 kernel_size_list = [[3, 3], [3, 3], [3, 3]] #channel size
23 kernel_stride_list = [2, 2, 2] #stride
24 batch_size = 500
25
26 def get_inception_graph():
27 ''''''
28 load inception-v3 gragh for get_inception_output to
29 get the feature from Inception-v3
30 ''''''
31 with tf.gfile.FastGFile(os.path.join(FLAGS.model_dir, ''inception-v3.pb''), ''rb'') as f:
32 graph_def = tf.GraphDef()
33 graph_def.ParseFromString(f.read())
34 inception_out = tf.import_graph_def(graph_def,name='''',return_elements=[''pool_3/_reshape:0''])
35 return inception_out
36
37
38 def create_graph_pre():
39 ''''''
40 create graph and loss
41 ''''''
42 inception_input = tf.placeholder(tf.float32, [None, FLAGS.width, FLAGS.inception_out_size/FLAGS.width, 1], name=''inception_holder'')
43 with tf.variable_scope(''DSC''):
44 with tf.variable_scope(''encoder''):
45 net = slim.conv2d(inception_input, kernel_num_list[0], kernel_size_list[0], stride = kernel_stride_list[0], scope=''conv_0'')
46 net = slim.conv2d(net, kernel_num_list[1], kernel_size_list[1], stride=kernel_stride_list[1], scope=''conv_1'')
47 net = slim.conv2d(net, kernel_num_list[2], kernel_size_list[2], stride=kernel_stride_list[2], scope=''conv_2'')
48
49 with tf.variable_scope(''decoder''):
50 net = slim.conv2d_transpose(net, kernel_num_list[1], kernel_size_list[2], stride=kernel_stride_list[2], scope=''deconv_2'')
51 net = slim.conv2d_transpose(net, kernel_num_list[0], kernel_size_list[1], stride=kernel_stride_list[1], scope=''deconv_1'')
52 net = slim.conv2d_transpose(net, 1, kernel_size_list[0], stride=kernel_stride_list[0], scope=''deconv_0'')
53
54 restruct_loss = tf.losses.mean_squared_error(net, inception_input)
55 return restruct_loss,inception_input,net
56
57
58 def get_inception_output(sess, img, txt_name,inception_out,save):
59 ''''''
60 get the inception-v3 feature for img and save in txt_name
61 ''''''
62 image_data = tf.gfile.FastGFile(img, ''rb'').read()
63 output = sess.run(inception_out, feed_dict={''DecodeJpeg/contents:0'': image_data})
64 output = np.squeeze(output)
65 output = output.reshape(FLAGS.width,-1)
66 if save == True:
67 np.savetxt(txt_name, output, fmt=''%.6f'')
68 return output
69
70
71 def get_inception_batch(sess,inception_out,save=True):
72 ''''''
73 get inception-v3 feature for a batch as input of the new graph(create_graph_pre)
74 ''''''
75 class_list = np.loadtxt(FLAGS.class_list, dtype= str)[0:batch_size]
76 batch = []
77
78 for i, item in enumerate(class_list):
79 class_img_path = os.path.join(FLAGS.img_path, item)
80 class_img_list = os.listdir(class_img_path)
81
82 img_name = random.choice(class_img_list)
83 txt_name = os.path.join(FLAGS.feature_train_path, item, img_name[:-4]+''txt'')
84 img = os.path.join(class_img_path, img_name)
85
86 if os.path.exists(txt_name):
87 print(''%s Found!'' % os.path.join(item, img_name[:-4]+''txt''))
88 batch_i = np.loadtxt(txt_name)
89 else:
90 #print(''%s Extracting!'' % os.path.join(item, img_name[:-4]+''txt''))
91 dir_name = os.path.join(FLAGS.feature_train_path, item)
92 if not os.path.exists(dir_name):
93 os.makedirs(dir_name)
94 batch_i = get_inception_output(sess, img,txt_name, inception_out,save=save)
95 batch.append(batch_i)
96 large_batch = np.array(batch) * FLAGS.large_multi
97
98 return large_batch
99
100
101 def reconstruct(sess, net, img_inception):
102 ''''''
103 get the loss for the input(img_inception) to varify the result of reconstruct
104 ''''''
105 output = sess.run([net], feed_dict={''inception_holder:0'': img_inception})
106 img_inception=np.squeeze(img_inception)
107 output=np.squeeze(np.array(output))
108 test_loss = pow(img_inception-output,2)
109
110 return output, sum(sum(test_loss))/(32*64)
111
112
113 def interface_pre():
114
115 total_loss, inception_input, net = create_graph_pre()
116
117 global_step = tf.Variable(0)
118 learning_rate = tf.train.exponential_decay(1e-3, global_step, decay_steps=100, decay_rate=0.98, staircase=True)
119 train_op = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(total_loss)
120
121 saver = tf.train.Saver(max_to_keep=3)
122
123 with tf.Session() as sess:
124
125 if FLAGS.restore:
126 print(''continue training from previous checkpoint'')
127 ckpt = tf.train.latest_checkpoint(FLAGS.checkpoint_path)
128 pre_step = int(ckpt.replace(FLAGS.checkpoint_path + ''-'', ''''))
129 saver.restore(sess, ckpt)
130 else:
131 #remove previous model
132 if os.path.exists(FLAGS.checkpoint_path):
133 shutil.rmtree(FLAGS.checkpoint_path)
134 os.makedirs(FLAGS.checkpoint_path)
135 sess.run(tf.global_variables_initializer())
136 pre_step = 0
137
138 inception_out = get_inception_graph()
139
140 for step in range(FLAGS.max_train_steps_pre):
141 if step % FLAGS.train_num_of_every_batch == 0:
142 inception_output = get_inception_batch(sess, inception_out, save=False)
143 inception_output = inception_output.reshape(-1,inception_output.shape[1], inception_output.shape[2], 1)
144 perm = np.arange(batch_size)
145 np.random.shuffle(perm)
146 inception_output = inception_output[perm]
147
148 inception_output = inception_output.reshape(-1,inception_output.shape[1], inception_output.shape[2], 1)
149 _, loss_value= sess.run([train_op, total_loss],feed_dict={''inception_holder:0'':inception_output})
150 if step % 100 == 0:
151 print("step %d :total_loss= %f" % (step, loss_value))
152 if step % 500 == 0 and step > 0:
153 # save model
154 if step > 500 :
155 write_meta_graph = False
156 else:
157 write_meta_graph = True
158 all_step = pre_step + step
159 saver.save(sess, FLAGS.checkpoint_path, global_step=all_step, write_meta_graph=write_meta_graph)
160 #construct
161 img_inception = get_inception_output(sess, ''cropped_panda.jpg'', ''cropped_panda.txt'',inception_out,False)
162 img_out, test_loss = reconstruct(sess, net, FLAGS.large_multi*img_inception.reshape(-1,32,64,1))
163 print("test loss= %.5f" % test_loss)
164
165 if __name__ == ''__main__'':
166 interface_pre()
三、训练
1. 原理
以 imagenet2012 在 inception-v3 特征上的类平均向量作为输入，来训练模型，获得自表示系数作为聚类输入，从而获得聚类结果并可视化。
2. 代码
1 import tensorflow as tf
2 import os
3 import numpy as np
4 import random
5 import tensorflow.contrib.slim as slim
6 import tensorflow.contrib.slim.nets as nets
7 import shutil
8 from scipy.sparse import coo_matrix
9 from sklearn.cluster import spectral_clustering
10 from scipy.sparse.linalg import svds
11 from sklearn import cluster
12 from sklearn.preprocessing import normalize
13
14 tf.app.flags.DEFINE_string(''class_list'', ''../imagenet12/train_class_list.txt'', ''ILSVRC2012 image class list'')
15 tf.app.flags.DEFINE_string(''img_path'', ''/media/gpu/bdc7606d-0e3c-4870-9a5d-4926fd9961c0/gpu/Works/imagenet/others/ILSVRC2012_img_train'', ''ILSVRC2012 image train path'')
16 tf.app.flags.DEFINE_integer(''max_train_steps'', 200000, ''max train num'')
17 tf.app.flags.DEFINE_boolean(''restore'', False, ''wheather restore model and variable from previous saved'')
18 tf.app.flags.DEFINE_string(''pretrain_path'', ''../model/pre/'', ''pretrain model path'')
19 tf.app.flags.DEFINE_string(''train_path'', ''model/train/'', ''train model path'')
20 tf.app.flags.DEFINE_string(''Coef_path'',''Coef/'',''save path of self_express xishu'')
21 tf.app.flags.DEFINE_integer(''large_multi'', 100, '''')
22 tf.app.flags.DEFINE_integer(''width'', 32, '''')
23 tf.app.flags.DEFINE_integer(''inception_out_size'', 2048, '''')
24 tf.app.flags.DEFINE_float(''self_express_loss_weight'',1,'''')
25 tf.app.flags.DEFINE_float(''regularizer_loss_weight'',0.01,'''')
26 tf.app.flags.DEFINE_integer(''train_num_of_every_batch'', 5000, '''')
27 tf.app.flags.DEFINE_string(''cluster_path'',''cluster'',''cluster result path'')
28 tf.app.flags.DEFINE_string(''data_path'',''avg_train_vector'',''imagenet2012 average feature path'')
29 FLAGS = tf.app.flags.FLAGS
30
31 kernel_num_list = [16, 32, 64]
32 kernel_size_list = [[3, 3], [3, 3], [3, 3]]
33 kernel_stride_list = [2, 2, 2]
34 batch_size = 1000
35 learn_rate=0.001
36
37
38 def create_graph_pre():
39
40 inception_input = tf.placeholder(tf.float32, [None, FLAGS.width, int(FLAGS.inception_out_size/FLAGS.width), 1], name=''inception_holder'')
41 with tf.variable_scope(''DSC''):
42 with slim.arg_scope([slim.conv2d], weights_regularizer=slim.l2_regularizer(0.0005)):
43 with tf.variable_scope(''encoder''):
44 net = slim.conv2d(inception_input, kernel_num_list[0], kernel_size_list[0], stride = kernel_stride_list[0], scope=''conv_0'')
45 net = slim.conv2d(net, kernel_num_list[1], kernel_size_list[1], stride=kernel_stride_list[1], scope=''conv_1'')
46 net = slim.conv2d(net, kernel_num_list[2], kernel_size_list[2], stride=kernel_stride_list[2], scope=''conv_2'')
47 self_express_x = net
48 net = tf.reshape(net, [batch_size, -1], name=''reshape_to_flat'')
49 Coef = slim.model_variable(''Coef'',
50 shape=[batch_size, batch_size],
51 initializer=tf.truncated_normal_initializer(stddev=0.1),
52 regularizer=slim.l2_regularizer(0.0005), trainable=True)
53 net = tf.matmul(Coef, net, name=''mutmul'')
54
55 with tf.variable_scope(''decoder''):
56 net = tf.reshape(net, [batch_size, int(FLAGS.width/8), int(FLAGS.inception_out_size/FLAGS.width/8), kernel_num_list[2]], name=''reshape_to_normal'')
57 self_express_x_c = net
58 net = slim.conv2d_transpose(net, kernel_num_list[1], kernel_size_list[2], stride=kernel_stride_list[2], scope=''deconv_2'')
59 net = slim.conv2d_transpose(net, kernel_num_list[0], kernel_size_list[1], stride=kernel_stride_list[1], scope=''deconv_1'')
60 net = slim.conv2d_transpose(net, 1, kernel_size_list[0], stride=kernel_stride_list[0], scope=''deconv_0'')
61
62 reconstruct_loss = tf.losses.mean_squared_error(net, inception_input)
63 self_express_loss = FLAGS.self_express_loss_weight *tf.losses.mean_squared_error(self_express_x, self_express_x_c)
64 regularizer_loss = FLAGS.regularizer_loss_weight * tf.reduce_sum(tf.pow(Coef, 2.0))
65 #regularizer_loss = tf.add_n(tf.losses.get_regularization_losses())
66
67 loss = reconstruct_loss + self_express_loss + regularizer_loss
68 #loss = self_express_loss
69 return net, loss, Coef,reconstruct_loss, self_express_loss, regularizer_loss
70
71 def get_inception_batch_avg():
72 class_list = np.loadtxt(FLAGS.class_list, dtype=str)[0:batch_size]
73 res=[]
74 for i in range(len(class_list)):
75 data_path = os.path.join(FLAGS.data_path,class_list[i]+''.txt'')
76 data = np.loadtxt(data_path)
77 data = data.reshape(32,64)
78 res.append(data*100)
79 return np.array(res)
80
81 def interface():
82 net, total_loss, Coef, reconstruct_loss, self_express_loss, regularizer_loss = create_graph_pre()
83
84 global_step = tf.Variable(0)
85 learning_rate = tf.train.exponential_decay(1e-4, global_step, decay_steps=100, decay_rate=0.98, staircase=True)
86
87 train_op = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(total_loss)
88 saver = tf.train.Saver(max_to_keep=3)
89 with tf.Session() as sess:
90 if FLAGS.restore:
91 print(''continue training from previous checkpoint'')
92 ckpt = tf.train.latest_checkpoint(FLAGS.train_path)
93 pre_step = int(ckpt.replace(FLAGS.train_path+''-'', ''''))
94 saver.restore(sess, ckpt)
95 else:
96 # remove previous model and Coef
97 if os.path.exists(FLAGS.train_path):
98 shutil.rmtree(FLAGS.train_path)
99 if os.path.exists(FLAGS.Coef_path):
100 shutil.rmtree(FLAGS.Coef_path)
101 os.makedirs(FLAGS.train_path)
102 os.makedirs(FLAGS.Coef_path)
103 # restore from pretrain
104 sess.run(tf.global_variables_initializer())
105 pre_step = 0
106 ckpt = tf.train.latest_checkpoint(FLAGS.pretrain_path)
107 variable_restore_op = slim.assign_from_checkpoint_fn(ckpt,slim.get_variables_to_restore(),ignore_missing_vars=True)
108 variable_restore_op(sess)
109
110 inception_out = get_inception_graph()
111 inception_output = get_inception_batch_avg()
112 inception_output = inception_output.reshape(-1, inception_output.shape[1], inception_output.shape[2], 1)
113 for step in range(FLAGS.max_train_steps):
114 _, loss_value, Coef_val, rec_val, see_val, reg_val= \
115 sess.run([train_op, total_loss, Coef, reconstruct_loss, self_express_loss, regularizer_loss],
116 feed_dict={''inception_holder:0'':inception_output})
117 if step % 100 == 0:
118 print("step %d :total_loss= %f,rec_loss= %f,see_val=%f,reg_val=%f"
119 % (step,loss_value,rec_val, see_val,reg_val))
120
121 if step % 1000 == 0 and step > 0:
122 if step > 500 :
123 write_meta_graph = False
124 else:
125 write_meta_graph = True
126 all_step = pre_step+step
127 saver.save(sess, FLAGS.train_path, global_step=all_step,write_meta_graph=write_meta_graph)
128 np.savetxt(FLAGS.Coef_path+str(all_step)+''.txt'',Coef_val,fmt=''%.6f'')
129
130
131 def thrC(C):
132 row,col = C.shape
133 for i in range(row):
134 for j in range(col):
135 C[i,j]=abs(C[i,j])
136 return C
137
138
139 def post_proC(C,N):
140 # C: coefficient matrix
141 C = 0.5 * (C + C.T)
142 np.savetxt(FLAGS.cluster_path + ''C_abs.txt'', C, fmt=''%.6f'')
143 graph = coo_matrix(C)
144 labels = spectral_clustering(graph, n_clusters=N)
145 return labels
146
147 def vis(N,labels):
148 ## visual
149 for i in range(N):
150 print(i)
151 index = [j for j in range(len(labels)) if labels[j]==i]
152 class_list=np.loadtxt(FLAGS.class_list,dtype=str)
153
154 sub_class_list = class_list[index]
155 np.savetxt(os.path.join(FLAGS.cluster_path, str(i) + ''.txt''), sub_class_list, fmt=''%s'')
156 if vis:
157 dir_path = os.path.join(FLAGS.cluster_path, str(i))
158 if os.path.exists(dir_path):
159 shutil.rmtree(dir_path)
160 os.makedirs(dir_path)
161 # copy an example to dir_path
162 for sub_class_item in sub_class_list:
163 img_path = os.path.join(FLAGS.img_path, sub_class_item)
164 random_img = random.choice(os.listdir(img_path))
165 src = os.path.join(img_path, random_img)
166 dst = os.path.join(dir_path, random_img)
167
168 shutil.copyfile(src, dst)
169
170 if __name__ == ''__main__'':
171 interface()
172
173 C=np.loadtxt(''Coef/199000.txt'') #系数，相似度矩阵
174 C=thrC(C)
175 N=32
176 grp = post_proC(C,N)
177
178 vis(N,grp)
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