-
Notifications
You must be signed in to change notification settings - Fork 102
/
transfer_learning_resnet50_custom_data.py
165 lines (134 loc) · 5.35 KB
/
transfer_learning_resnet50_custom_data.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
import numpy as np
import os
import time
from resnet50 import ResNet50
from keras.preprocessing import image
from keras.layers import GlobalAveragePooling2D, Dense, Dropout,Activation,Flatten
from imagenet_utils import preprocess_input
from keras.layers import Input
from keras.models import Model
from keras.utils import np_utils
from sklearn.utils import shuffle
from sklearn.cross_validation import train_test_split
img_path = 'elephant.jpg'
img = image.load_img(img_path, target_size=(224, 224))
x = image.img_to_array(img)
print (x.shape)
x = np.expand_dims(x, axis=0)
print (x.shape)
x = preprocess_input(x)
print('Input image shape:', x.shape)
# Loading the training data
PATH = os.getcwd()
# Define data path
data_path = PATH + '/data'
data_dir_list = os.listdir(data_path)
img_data_list=[]
for dataset in data_dir_list:
img_list=os.listdir(data_path+'/'+ dataset)
print ('Loaded the images of dataset-'+'{}\n'.format(dataset))
for img in img_list:
img_path = data_path + '/'+ dataset + '/'+ img
img = image.load_img(img_path, target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
print('Input image shape:', x.shape)
img_data_list.append(x)
img_data = np.array(img_data_list)
#img_data = img_data.astype('float32')
print (img_data.shape)
img_data=np.rollaxis(img_data,1,0)
print (img_data.shape)
img_data=img_data[0]
print (img_data.shape)
# Define the number of classes
num_classes = 4
num_of_samples = img_data.shape[0]
labels = np.ones((num_of_samples,),dtype='int64')
labels[0:202]=0
labels[202:404]=1
labels[404:606]=2
labels[606:]=3
names = ['cats','dogs','horses','humans']
# convert class labels to on-hot encoding
Y = np_utils.to_categorical(labels, num_classes)
#Shuffle the dataset
x,y = shuffle(img_data,Y, random_state=2)
# Split the dataset
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=2)
###########################################################################################################################
# Custom_resnet_model_1
#Training the classifier alone
image_input = Input(shape=(224, 224, 3))
model = ResNet50(input_tensor=image_input, include_top=True,weights='imagenet')
model.summary()
last_layer = model.get_layer('avg_pool').output
x= Flatten(name='flatten')(last_layer)
out = Dense(num_classes, activation='softmax', name='output_layer')(x)
custom_resnet_model = Model(inputs=image_input,outputs= out)
custom_resnet_model.summary()
for layer in custom_resnet_model.layers[:-1]:
layer.trainable = False
custom_resnet_model.layers[-1].trainable
custom_resnet_model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])
t=time.time()
hist = custom_resnet_model.fit(X_train, y_train, batch_size=32, epochs=12, verbose=1, validation_data=(X_test, y_test))
print('Training time: %s' % (t - time.time()))
(loss, accuracy) = custom_resnet_model.evaluate(X_test, y_test, batch_size=10, verbose=1)
print("[INFO] loss={:.4f}, accuracy: {:.4f}%".format(loss,accuracy * 100))
###########################################################################################################################
# Fine tune the resnet 50
#image_input = Input(shape=(224, 224, 3))
model = ResNet50(weights='imagenet',include_top=False)
model.summary()
last_layer = model.output
# add a global spatial average pooling layer
x = GlobalAveragePooling2D()(last_layer)
# add fully-connected & dropout layers
x = Dense(512, activation='relu',name='fc-1')(x)
x = Dropout(0.5)(x)
x = Dense(256, activation='relu',name='fc-2')(x)
x = Dropout(0.5)(x)
# a softmax layer for 4 classes
out = Dense(num_classes, activation='softmax',name='output_layer')(x)
# this is the model we will train
custom_resnet_model2 = Model(inputs=model.input, outputs=out)
custom_resnet_model2.summary()
for layer in custom_resnet_model2.layers[:-6]:
layer.trainable = False
custom_resnet_model2.layers[-1].trainable
custom_resnet_model2.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])
t=time.time()
hist = custom_resnet_model2.fit(X_train, y_train, batch_size=32, epochs=12, verbose=1, validation_data=(X_test, y_test))
print('Training time: %s' % (t - time.time()))
(loss, accuracy) = custom_resnet_model2.evaluate(X_test, y_test, batch_size=10, verbose=1)
print("[INFO] loss={:.4f}, accuracy: {:.4f}%".format(loss,accuracy * 100))
############################################################################################
import matplotlib.pyplot as plt
# visualizing losses and accuracy
train_loss=hist.history['loss']
val_loss=hist.history['val_loss']
train_acc=hist.history['acc']
val_acc=hist.history['val_acc']
xc=range(12)
plt.figure(1,figsize=(7,5))
plt.plot(xc,train_loss)
plt.plot(xc,val_loss)
plt.xlabel('num of Epochs')
plt.ylabel('loss')
plt.title('train_loss vs val_loss')
plt.grid(True)
plt.legend(['train','val'])
#print plt.style.available # use bmh, classic,ggplot for big pictures
plt.style.use(['classic'])
plt.figure(2,figsize=(7,5))
plt.plot(xc,train_acc)
plt.plot(xc,val_acc)
plt.xlabel('num of Epochs')
plt.ylabel('accuracy')
plt.title('train_acc vs val_acc')
plt.grid(True)
plt.legend(['train','val'],loc=4)
#print plt.style.available # use bmh, classic,ggplot for big pictures
plt.style.use(['classic'])