-
Notifications
You must be signed in to change notification settings - Fork 0
/
M_InceptionV3.py
223 lines (189 loc) · 6.61 KB
/
M_InceptionV3.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
# -*- coding: utf-8 -*-
"""
Created on Sun Oct 2 20:48:56 2022
@author: Huibo Zhang
"""
import numpy as np
import glob
import cv2
import os
import tensorflow as tf
#from tensorflow import keras
#from keras.applications import InceptionV3
from keras.models import Sequential
from keras.layers import Flatten
from keras.layers import Dense#, Dropout, Activation, GlobalAveragePooling2D
import matplotlib.pyplot as plt
####method1
InceptionV3_model = Sequential()
pretrained_model= tf.keras.applications.InceptionV3(include_top=False,
input_shape=(224,224,3),
pooling='avg',classes=3,
weights='imagenet')
for layer in pretrained_model.layers:
layer.trainable=False
InceptionV3_model.add(pretrained_model)
InceptionV3_model.add(Flatten())
InceptionV3_model.add(Dense(512, activation='relu'))
InceptionV3_model.add(Dense(3, activation='softmax'))
InceptionV3_model.summary()
"""
######method2
Inc = InceptionV3(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
for layer in Inc.layers:
layer.trainable = False
x = Flatten()(Inc.output)
prediction = Dense(3, activation="softmax")(x)
InceptionV3_model = Model(inputs=Inc.input, outputs=prediction)
InceptionV3_model.summary()
"""
"""
###input data
"""
##### Training set:
#Read input images and assign labels based on folder names
print(os.listdir("training/train/"))
IMG_WIDTH = 224
IMG_HEIGHT = 224
IMG_CHANNELS = 3
SIZE = 224 #Resize images
#Capture training data and labels into respective lists
train_images = []
train_labels = []
for directory_path in glob.glob("training/train/*"):
label = directory_path.split("\\")[-1]
#print(label)
for img_path in glob.glob(os.path.join(directory_path, "*.tif")):
#print(img_path)
img = cv2.imread(img_path, cv2.IMREAD_COLOR)
img = cv2.resize(img, (SIZE, SIZE))
img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
train_images.append(img)
train_labels.append(label)
#Convert lists to arrays
X_train = np.array(train_images)
X_train = X_train / 255.0
y_train = np.array(train_labels)
len(train_images)
#Encode labels from text to integers.
from sklearn import preprocessing
le = preprocessing.LabelEncoder()
le.fit(train_labels)
Y_train = le.transform(train_labels)
#####validation set
print(os.listdir("training/validation/"))
SIZE = 224 #Resize images
#Capture training data and labels into respective lists
val_images = []
val_labels = []
for directory_path in glob.glob("training/validation/*"):
label = directory_path.split("\\")[-1]
#print(label)
for img_path in glob.glob(os.path.join(directory_path, "*.tif")):
#print(img_path)
img = cv2.imread(img_path, cv2.IMREAD_COLOR)
img = cv2.resize(img, (SIZE, SIZE))
img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
val_images.append(img)
val_labels.append(label)
#Convert lists to arrays
X_val = np.array(val_images)
X_val = X_val / 255.0
y_val = np.array(val_labels)
#Encode labels from text to integers.
from sklearn import preprocessing
le = preprocessing.LabelEncoder()
le.fit(y_val)
Y_val = le.transform(y_val)
####training
InceptionV3_model.compile(optimizer='adam', loss = "sparse_categorical_crossentropy",metrics = ['accuracy'])
checkpointer = tf.keras.callbacks.ModelCheckpoint('InceptionV3_model_for_classification.h5', verbose=1, save_best_only=True)
callbacks = [
tf.keras.callbacks.EarlyStopping(patience=4, monitor='loss',mode="auto"),
tf.keras.callbacks.TensorBoard(log_dir='logs'),
checkpointer]
history = InceptionV3_model.fit(X_train,Y_train,
epochs=50,
validation_data=(X_val,Y_val),
#validation_split=0.2,
verbose = 1,
callbacks=callbacks)
"""
### accuracy and loss plot
"""
accu = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(accu))
plt.plot(epochs,accu, 'b', label='Training accuracy')
plt.plot(epochs, val_acc, 'r', label='validation accuracy')
plt.title('Training and validation set accuracy')
plt.legend(loc='lower right')
plt.savefig('./plot/InceptionV3_accuracy.pdf', dpi = 1000)
plt.figure()
plt.plot(epochs, loss, 'r', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.savefig('./plot/InceptionV3_loss.pdf', dpi = 1000)
plt.show()
"""
利用confusion matirx 评估模型
"""
#####confusion matirx
from sklearn.metrics import confusion_matrix
#import seaborn as sns
#from sklearn.metrics import roc_curve
###X_train
probas=InceptionV3_model.predict(X_train)
y_train_pred = np.argmax(probas,axis=1)
conf_mx = confusion_matrix(Y_train.astype(str), y_train_pred.astype(str))
conf_mx
#figure
def plot_confusion_matrix(matrix):
#If you prefer color and a colorbar
fig = plt.figure(figsize=(8,8))
ax = fig.add_subplot(111)
cax = ax.matshow(matrix)
fig.colorbar(cax)
plt.matshow(conf_mx, cmap=plt.cm.gray)
plt.title("InceptionV3_confusion_matrix",x=0.5,y=-0.12)
plt.savefig("./plot/InceptionV3_confusion_matrix.pdf", tight_layout=False)
plt.show()
###error analysis
#row： actual class， column：predicted classes
row_sums = conf_mx.sum(axis=1, keepdims=True)
norm_conf_mx = conf_mx / row_sums
np.fill_diagonal(norm_conf_mx, 0)
plt.matshow(norm_conf_mx, cmap=plt.cm.gray)
plt.title("InceptionV3_T_confusion_matrix_errors",x=0.5,y=-0.12)
plt.subplots_adjust(left=0.1, right=0.2, top=0.2, bottom=0.1)
plt.savefig("./plot/InceptionV3_T_confusion_matrix_errors.pdf", tight_layout=False)
plt.show()
###X_val
probas=InceptionV3_model.predict(X_val)
y_val_pred = np.argmax(probas,axis=1)
conf_mx = confusion_matrix(Y_val.astype(str), y_val_pred.astype(str))
conf_mx
#figure
def plot_confusion_matrix(matrix):
#If you prefer color and a colorbar
fig = plt.figure(figsize=(8,8))
ax = fig.add_subplot(111)
cax = ax.matshow(matrix)
fig.colorbar(cax)
plt.matshow(conf_mx, cmap=plt.cm.gray)
plt.title("InceptionV3_V_confusion_matrix",x=0.5,y=-0.12)
plt.savefig("./plot/InceptionV3_V_confusion_matrix.pdf", tight_layout=False)
plt.show()
###error analysis
#row： actual class， column：predicted classes
row_sums = conf_mx.sum(axis=1, keepdims=True)
norm_conf_mx = conf_mx / row_sums
np.fill_diagonal(norm_conf_mx, 0)
plt.matshow(norm_conf_mx, cmap=plt.cm.gray)
plt.title("InceptionV3_V_confusion_matrix_errors",x=0.5,y=-0.12)
plt.subplots_adjust(left=0.1, right=0.2, top=0.2, bottom=0.1)
plt.savefig("./plot/InceptionV3_V_confusion_matrix_errors.pdf", tight_layout=False)
plt.show()