-
Notifications
You must be signed in to change notification settings - Fork 0
/
vgg16_test.py
402 lines (230 loc) · 10.6 KB
/
vgg16_test.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
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
# -*- coding: utf-8 -*-
from __future__ import print_function
"""
Created on Mon Sep 4 09:25:28 2017
@author: xingshuli
"""
import keras
import numpy as np
import warnings
import os
from keras.models import Model
from keras.layers import Flatten
from keras.layers import Dense
from keras.layers import Input
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import GlobalMaxPooling2D
from keras.layers import GlobalAveragePooling2D
#from keras.layers import Dropout
from keras.preprocessing import image
from keras.utils import layer_utils
from keras.utils.data_utils import get_file
from keras import backend as K
from keras.preprocessing.image import ImageDataGenerator
#from keras.applications.imagenet_utils import decode_predictions
#from keras.applications.imagenet_utils import preprocess_input
from keras.applications.imagenet_utils import _obtain_input_shape
from keras.engine.topology import get_source_inputs
#from keras.callbacks import EarlyStopping
WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels.h5'
WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5'
def VGG16(include_top=True, weights='imagenet', input_tensor=None,
input_shape=None, pooling=None, classes=1000):
if weights not in {'imagenet', None}:
raise ValueError('The `weights` argument should be either '
'`None` (random initialization) or `imagenet` '
'(pre-training on ImageNet).')
if weights == 'imagenet' and include_top and classes != 1000:
raise ValueError('If using `weights` as imagenet with `include_top`'
' as true, `classes` should be 1000')
#determine proper input shape
input_shape = _obtain_input_shape(input_shape, default_size=224,
min_size=48,
data_format=K.image_data_format(),
include_top=include_top)
if input_tensor is None:
img_input = Input(shape=input_shape)
else:
if not K.is_keras_tensor(input_tensor):
img_input = Input(tensor=input_tensor, shape=input_shape)
else:
img_input = input_tensor
#Block_1
x = Conv2D(64, (3,3), activation='relu', padding='same', name='block1_conv1')(img_input)
x = Conv2D(64, (3,3), activation='relu', padding='same', name='block1_conv2')(x)
x = MaxPooling2D((2,2), strides=(2,2), name='block1_pool')(x)
#Block_2
x = Conv2D(128, (3,3), activation='relu', padding='same', name='block2_conv1')(x)
x = Conv2D(128, (3,3), activation='relu', padding='same', name='block2_conv2')(x)
x = MaxPooling2D((2,2), strides=(2,2), name='block2_pool')(x)
#Block_3
x = Conv2D(256, (3,3), activation='relu', padding='same', name='block3_conv1')(x)
x = Conv2D(256, (3,3), activation='relu', padding='same', name='block3_conv2')(x)
x = Conv2D(256, (3,3), activation='relu', padding='same', name='block3_conv3')(x)
x = MaxPooling2D((2,2), strides=(2,2), name='block3_pool')(x)
#Block_4
x = Conv2D(512, (3,3), activation='relu', padding='same', name='block4_conv1')(x)
x = Conv2D(512, (3,3), activation='relu', padding='same', name='block4_conv2')(x)
x = Conv2D(512, (3,3), activation='relu', padding='same', name='block4_conv3')(x)
x = MaxPooling2D((2,2), strides=(2,2), name='block4_pool')(x)
#Block_5
x = Conv2D(512, (3,3), activation='relu', padding='same', name='block5_conv1')(x)
x = Conv2D(512, (3,3), activation='relu', padding='same', name='block5_conv2')(x)
x = Conv2D(512, (3,3), activation='relu', padding='same', name='block5_conv3')(x)
x = MaxPooling2D((2,2), strides=(2,2), name='block5_pool')(x)
if include_top:
x = Flatten(name='flatten')(x)
x = Dense(4096, activation='relu', name='fc1')(x)
x = Dense(4096, activation='relu', name='fc2')(x)
x = Dense(classes, activation='softmax', name='predictions')(x)
else:
if pooling == 'avg':
x = GlobalAveragePooling2D()(x)
elif pooling == 'max':
x = GlobalMaxPooling2D()(x)
#Ensure that the model takes into account
# any potential predecessors of `input_tensor`.
if input_tensor is not None:
inputs = get_source_inputs(input_tensor)
else:
inputs = img_input
#create model
model = Model(inputs, x, name='vgg16')
#load weights
if weights == 'imagenet':
if include_top:
weights_path = get_file('vgg16_weights_tf_dim_ordering_tf_kernels.h5',
WEIGHTS_PATH,
cache_subdir='models')
else:
weights_path = get_file('vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5',
WEIGHTS_PATH_NO_TOP,
cache_subdir='models')
model.load_weights(weights_path)
if K.backend() == 'theano':
layer_utils.convert_all_kernels_in_model(model)
if K.image_data_format() == 'channels_first':
if include_top:
maxpool = model.get_layer(name='block5_pool')
shape = maxpool.output_shape[1:]
dense = model.get_layer(name='fc1')
layer_utils.convert_dense_weights_data_format(dense, shape, 'channels_first')
if K.backend() == 'tensorflow':
warnings.warn('You are using the TensorFlow backend, yet you '
'are using the Theano '
'image data format convention '
'(`image_data_format="channels_first"`). '
'For best performance, set '
'`image_data_format="channels_last"` in '
'your Keras config '
'at ~/.keras/keras.json.')
return model
#Recreate my own vgg16 model
img_width, img_height = 200, 200
input_tensor = Input(shape=(img_width, img_height, 3))
train_data_dir = os.path.join(os.getcwd(), 'data/train')
validation_data_dir = os.path.join(os.getcwd(), 'data/validation')
nb_train_samples = 5000
nb_validation_samples = 1000
num_class = 10
epochs = 100
batch_size = 20
base_model = VGG16(include_top=False, weights='imagenet',
input_tensor=input_tensor, pooling='avg')
for i, layer in enumerate(base_model.layers):
print(i, layer.name)
x = base_model.output
#Rebuild the fully connected layers
#x = Flatten()(x)
#x = Dense(4096, activation='relu')(x)
#x = Dropout(0.5)(x)
#x = Dense(4096, activation='relu')(x)
#x = Dropout(0.5)(x)
#pre_out = Dense(num_class, activation='softmax')(x)
x = Dense(512, activation='relu')(x)
pre_out = Dense(num_class, activation='softmax')(x)
#we will train the model again
train_model = Model(base_model.input, outputs= pre_out, name='train_model')
## we assume that all layers can be trainable
#for layer in base_model.layers:
# layer.trainable = True
for layer in base_model.layers[:4]:
layer.trainable = False
for layer in base_model.layers[4:]:
layer.trainable = True
sgd = keras.optimizers.SGD(lr=0.0001, decay=1e-6, momentum=0.9, nesterov=True)
train_model.compile(loss='categorical_crossentropy',
optimizer=sgd, metrics=['accuracy'])
train_model.summary()
##preprocessing
train_datagen = ImageDataGenerator(samplewise_center=False,
rotation_range=30,
width_shift_range=0.1,
height_shift_range=0.1,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
rescale=1. / 255,
fill_mode='nearest')
test_datagen = ImageDataGenerator(rescale=1. / 255)
train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='categorical')
validation_generator = test_datagen.flow_from_directory(
validation_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='categorical')
#early-stopping
#early_stopping = EarlyStopping(monitor='val_loss', patience=3)
hist = train_model.fit_generator(
train_generator,
steps_per_epoch=nb_train_samples //batch_size,
epochs=epochs,
validation_data=validation_generator,
validation_steps=nb_validation_samples //batch_size
)
#print(hist.history['acc'])
f = open('/home/xingshuli/Desktop/acc.txt','w')
f.write(str(hist.history['acc']))
f.close()
#print(hist.history['val_acc'])
f = open('/home/xingshuli/Desktop/val_acc.txt','w')
f.write(str(hist.history['val_acc']))
f.close()
#print val_loss and stored into val_loss.txt
f = open('/home/xingshuli/Desktop/val_loss.txt', 'w')
f.write(str(hist.history['val_loss']))
f.close()
evaluation = train_model.evaluate_generator(validation_generator,
steps=nb_validation_samples //batch_size)
print('Model Accuracy = %.4f' % (evaluation[1]))
#predict a category of input image
img_path = '/home/xingshuli/Desktop/test_pictures/citrus_swallowtail.jpeg'
img = image.load_img(img_path, target_size=(200, 200))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x /=255
print('Input image shape:', x.shape)
preds = train_model.predict(x)
print('citrus_swallowtail:%f, forktailed_bush_katydid:%f, ground_beetle:%f, \
green_stink_bug:%f, green_leafhopper:%f, syrhip_fly:%f, dragon_fly:%f, \
mantis:%f, fruit_moth:%f, citrus_longicorn_beetle:%f' \
%(preds[0][0], preds[0][1], preds[0][2], preds[0][3], preds[0][4],
preds[0][5], preds[0][6], preds[0][7], preds[0][8], preds[0][9]))
##if __name__ == '__main__':
## model = VGG16(include_top=True, weights='imagenet')
##
## img_path = '/home/xingshuli/Desktop/elephant.jpeg'
## 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)
##
## preds = model.predict(x)
## print('Predicted:', decode_predictions(preds))
##