-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathtrain.py
270 lines (239 loc) · 12.6 KB
/
train.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
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
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
#tensorflow 模块
import tensorflow as tf
#skimage模块下的io transform(图像的形变与缩放)模块
from skimage import io,transform
#glob 文件通配符模块
import glob
#os 处理文件和目录的模块
import os
#多维数据处理模块
import numpy as np
import time
import matplotlib.pyplot as plt
from model import inference
os.environ["CUDA_VISIBLE_DEVICES"] = '0' #use GPU with ID=0
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.5 # maximun alloc gpu50% of MEM
config.gpu_options.allow_growth = True #allocate dynamically
x_train=np.load('training_data.npy')
y_train=np.load('training_label.npy')
x_val=np.load('test_data1.npy')
y_val=np.load('test_label1.npy')
x_test=np.load('test_data2.npy')
y_test=np.load('test_label2.npy')
#将所有的图片resize成100*100
w=96
h=96
c=1
#-----------------构建网络----------------------
#占位符，设置输入参数的大小和格式
x=tf.placeholder(tf.float32,shape=[None,w,h,c],name='x')
y_=tf.placeholder(tf.int32,shape=[None,],name='y_')
def conv_image_visual(conv_image,image_weight,image_height,cy,cx,channels):
#slice off one image ande remove the image dimension
#original image is a 4d tensor[batche_size,weight,height,channels]
conv_image = tf.slice(conv_image,(0,0,0,0),(1,-1,-1,-1))
conv_image = tf.reshape(conv_image,(image_height,image_weight,channels))
#add a couple of pixels of zero padding around the image
image_weight += 4
image_height += 4
conv_image = tf.image.resize_image_with_crop_or_pad(conv_image,image_height,image_weight)
conv_image = tf.reshape(conv_image,(image_height,image_weight,cy,cx))
conv_image = tf.transpose(conv_image,(2,0,3,1))
conv_image = tf.reshape(conv_image,(1,cy*image_height,cx*image_weight,1))
return conv_image
#设置正则化参数为0.0001
regularizer = tf.contrib.layers.l2_regularizer(0.0001)
#将上述构建网络结构引入
logits,relu13,relu123 = inference(x,False,regularizer)
im1=conv_image_visual(relu13,96,96,4,8,32)
tf.summary.image('h_conv1',im1)
im2=conv_image_visual(relu123,96,96,16,4,64)
tf.summary.image('h_conv2',im2)
#(小处理)将logits乘以1赋值给logits_eval，定义name，方便在后续调用模型时通过tensor名字调用输出tensor
b = tf.constant(value=1,dtype=tf.float32)
logits_eval = tf.multiply(logits,b,name='logits_eval')
#设置损失函数，作为模型训练优化的参考标准，loss越小，模型越优
loss=tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=y_)
loss1=tf.add_n(tf.get_collection('losses'))
loss2=loss+loss1
#设置整体学习率为α为0.001
# batch_size=16
# global_step=tf.Variable(0,trainable=False)
# decay_ste=label1.shape[0]/batch_size
# learning_rate_base=0.01
# decay_rate=0.99
# learning_rate=tf.train.exponential_decay(learning_rate_base,global_step,decay_ste,decay_rate)
# train_op=tf.train.AdamOptimizer(learning_rate).minimize(loss,global_step=global_step)
train_op=tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss2)
#设置预测精度
correct_prediction = tf.equal(tf.cast(tf.argmax(logits,1),tf.int32), y_)
acc= tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
merged = tf.summary.merge_all()
#定义一个函数，按批次取数据
def minibatches(inputs=None, targets=None, batch_size=None, shuffle=False):
assert len(inputs) == len(targets)
if shuffle:
indices = np.arange(len(inputs))
np.random.shuffle(indices)
for start_idx in range(0, len(inputs) - batch_size + 1, batch_size):
if shuffle:
excerpt = indices[start_idx:start_idx + batch_size]
else:
excerpt = slice(start_idx, start_idx + batch_size)
yield inputs[excerpt], targets[excerpt]
#训练和测试数据，可将n_epoch设置更大一些
#迭代次数
n_epoch=30
#每次迭代输入的图片数据
batch_size=32
saver=tf.train.Saver(max_to_keep=1)
sess=tf.Session(config=config)
train_writer = tf.summary.FileWriter("log/",sess.graph)
#初始化全局参数
sess.run(tf.global_variables_initializer())
max_acc=0
#开始迭代训练，调用的都是前面设置好的函数或变量
train_cost=[]
train_accuracy=[]
val_cost=[]
val_accuracy=[]
for epoch in range(n_epoch):
start_time = time.time()
#training
train_loss, train_acc, n_batch = 0, 0, 0
i=0
for x_train_a, y_train_a in minibatches(x_train, y_train, batch_size, shuffle=True):
i=i+1
_,err,ac,summary=sess.run([train_op,loss,acc,merged], feed_dict={x: x_train_a, y_: y_train_a})
train_loss += err; train_acc += ac; n_batch += 1
train_writer.add_summary(summary,epoch)
print(" train loss: %f" % (np.sum(train_loss)/ n_batch))
print(" train acc: %f" % (np.sum(train_acc)/ n_batch))
train_cost.append(np.sum(train_loss)/ n_batch)
train_accuracy.append(np.sum(train_acc)/ n_batch)
#validation
val_loss, val_acc, n_batch = 0, 0, 0
for x_val_a, y_val_a in minibatches(x_val, y_val, batch_size, shuffle=False):
err, ac = sess.run([loss,acc], feed_dict={x: x_val_a, y_: y_val_a})
val_loss += err; val_acc += ac; n_batch += 1
print(" validation loss: %f" % (np.sum(val_loss)/ n_batch))
print(" validation acc: %f" % (np.sum(val_acc)/ n_batch))
val_cost.append(np.sum(val_loss)/ n_batch)
val_accuracy.append(np.sum(val_acc)/ n_batch)
test_loss, test_acc, n_batch = 0, 0, 0
for x_test_a, y_test_a in minibatches(x_test, y_test, batch_size, shuffle=False):
err, ac = sess.run([loss,acc], feed_dict={x: x_test_a, y_: y_test_a})
test_loss += err; test_acc += ac; n_batch += 1
print(" test loss: %f" % (np.sum(test_loss)/ n_batch))
print(" test acc: %f" % (np.sum(test_acc)/ n_batch))
if val_acc>=max_acc:
max_acc=val_acc;
saver.save(sess,'ckpt2/train.ckpt',global_step=epoch+1)
# fig1=plt.figure(1,figsize=(8,6))
# plt.plot(train_cost,'r',label='training loss')
# plt.plot(val_cost,'b',label='validation loss')
# plt.xlabel('Epochs',fontdict={'family':'Times New Roman','size':24})
# plt.ylabel('Loss',fontdict={'family':'Times New Roman','size':24})
# plt.xticks(fontproperties='Times New Roman', size=18)
# plt.yticks(fontproperties='Times New Roman', size=18)
# plt.legend(prop={'family':'Times New Roman','size':24})
# plt.show()
# # 绘制精度函数曲线
# fig2=plt.figure(1,figsize=(8,6))
# plt.plot(train_accuracy,'r*-',label='training accuracy')
# plt.plot(val_accuracy,'bo-',label='validation accuracy')
# plt.xlabel('Epochs',fontdict={'family':'Times New Roman','size':24})
# plt.ylabel('Accuracy',fontdict={'family':'Times New Roman','size':24})
# plt.xticks(fontproperties='Times New Roman', size=18)
# plt.yticks(fontproperties='Times New Roman', size=18)
# plt.legend(prop={'family':'Times New Roman','size':24})
# plt.show()
# # #保存模型及模型参数
# # #各层特征可视化
# fig3, ax3=plt.subplots(figsize=(2,2))
# ax3.imshow(np.reshape(x_train[11],(96,96)))
# plt.show()
# #第一个卷积层可视化
# input_image = x_train[11:13]
# conv1_16 = sess.run(relu13, feed_dict={x:input_image}) # [1, 28, 28 ,16]
# conv1_transpose = sess.run(tf.transpose(conv1_16, [3, 0, 1, 2]))
# fig4,ax4 = plt.subplots(nrows=1, ncols=32, figsize = (32,1))
# for i in range(32):
# ax4[i].imshow(conv1_transpose[i][0],cmap=plt.cm.gray) # tensor的切片[row, column]
# #第一个卷积层可视化
# input_image = x_test[11:13]
# conv1_16 = sess.run(relu13, feed_dict={x:input_image}) # [1, 28, 28 ,16]
# conv1_transpose = sess.run(tf.transpose(conv1_16, [3, 0, 1, 2]))
# fig4,ax4 = plt.subplots(nrows=1, ncols=32, figsize = (32,1))
# plt.xticks([])
# plt.yticks([])
# plt.axis('off')
# for i in range(32):
# ax4[i].set_xticks([])
# ax4[i].set_yticks([])
# ax4[i].imshow(conv1_transpose[i][0]) # tensor的切片[row, column]
# plt.xticks([])
# plt.yticks([])
# plt.axis('off')
# plt.show()
# #第二个卷积层可视化
# input_image = x_test[11:13]
# conv2_16 = sess.run(relu123, feed_dict={x:input_image}) # [1, 28, 28 ,16]
# conv2_transpose = sess.run(tf.transpose(conv2_16, [3, 0, 1, 2]))
# fig4,ax4 = plt.subplots(nrows=1, ncols=12, figsize = (12,1))
# plt.xticks([])
# plt.yticks([])
# plt.axis('off')
# for i in range(12):
# ax4[i].set_xticks([])
# ax4[i].set_yticks([])
# ax4[i].imshow(conv2_transpose[i][0]) # tensor的切片[row, column]
# plt.show()
# #第三个卷积层可视化
# input_image = x_test[11:13]
# conv3_16 = sess.run(relu13, feed_dict={x:input_image}) # [1, 28, 28 ,16]
# conv3_transpose = sess.run(tf.transpose(conv3_16, [3, 0, 1, 2]))
# fig4,ax4 = plt.subplots(nrows=1, ncols=12, figsize = (12,1))
# plt.xticks([])
# plt.yticks([])
# plt.axis('off')
# for i in range(12):
# plt.xticks([])
# plt.yticks([])
# plt.axis('off')
# ax4[i].set_xticks([])
# ax4[i].set_yticks([])
# ax4[i].imshow(conv3_transpose[i][0])
# plt.xticks([])
# plt.yticks([])
# plt.axis('off')
# # tensor的切片[row, column]
# plt.show()
# #第四个卷积层可视化
# input_image = x_train[11:13]
# conv4_16 = sess.run(relu14, feed_dict={x:input_image}) # [1, 28, 28 ,16]
# conv4_transpose = sess.run(tf.transpose(conv4_16, [3, 0, 1, 2]))
# fig4,ax4 = plt.subplots(nrows=1, ncols=16, figsize = (16,1))
# plt.xticks([])
# plt.yticks([])
# plt.axis('off')
# for i in range(16):
# ax4[i].imshow(conv4_transpose[i][0]) # tensor的切片[row, column]
#最后一个卷积层可视化
# input_image = x_test[11:13]
# conv5_16 = sess.run(relu4, feed_dict={x:input_image}) # [1, 28, 28 ,16]
# conv5_transpose = sess.run(tf.transpose(conv5_16, [3, 0, 1, 2]))
# fig4,ax4 = plt.subplots(nrows=1, ncols=16, figsize = (16,1))
# plt.xticks([])
# plt.yticks([])
# plt.axis('off')
# for i in range(16):
# ax4[i].set_xticks([])
# ax4[i].set_yticks([])
# ax4[i].imshow(conv5_transpose[i][0])
# # tensor的切片[row, column]
# plt.show()
# plt.title('Conv1 16x100x100')
# plt.show()
# sess.close()