-
Notifications
You must be signed in to change notification settings - Fork 0
/
FilterPlan2_WithComp2.py
181 lines (126 loc) · 6.09 KB
/
FilterPlan2_WithComp2.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
#import os
#os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
#os.environ["CUDA_VISIBLE_DEVICES"] = "1" # use id from $ nvidia-smi
#To force using CPU
#import os
#os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152
#os.environ["CUDA_VISIBLE_DEVICES"] = ""
from keras.layers import Activation as activation
from keras.layers.core import Dense, Flatten , Dropout
from keras.layers.convolutional import Convolution2D
from keras.layers.normalization import BatchNormalization
from keras.layers.pooling import MaxPooling2D
from keras.models import Model
from keras import layers
from keras import Input
import numpy as np
from keras.utils.np_utils import to_categorical
from keras.optimizers import *
from keras import callbacks
from keras.callbacks import TensorBoard, CSVLogger
import os
import scipy.io
import h5py
import mat73
from keras import optimizers
path='D:\Shapar\ShaghayeghUni\AfterPropozal\Step1-EventLandmark\Programs\MyPrograms\EventExtraction\Keras'
#import data-------------------------------------------------------------------
#val:
CB_total_val= scipy.io.loadmat(path+'\CB_total_val.mat')
x_val=CB_total_val['CB_total_val']
LBL_total_val = scipy.io.loadmat(path+'\LBL_total_val.mat')
y_val=LBL_total_val['LBL_total_val']
#------------------------------------------------------------------------------
#-----------------------------------------------------------------------------
# after running (LandmarkNumber_Train7Test.m):
# load matlab output:
data_dict = mat73.loadmat(path+'\CBtrain_In_Comp2.mat')
x_train=np.transpose(data_dict['CBtrain_In_Comp2'])
data_dict = mat73.loadmat(path+'\CBtrain_Out_Comp2.mat')
x_train_out=np.transpose(data_dict['CBtrain_Out_Comp2'])
LBL_out_Comp2= scipy.io.loadmat(path+'\LBL_out_Comp2.mat')
train_lbl=LBL_out_Comp2['LBL_out_Comp2']
#-----------------------------------------------------------------------------
#-----------------------------------------------------------------------------
# DAE model
L=len(x_train[0])
input_tensor = Input(shape=(L,))
x=input_tensor
x = Dense(700, activation="relu")(x)
x = Dense(700, activation="relu")(x)
x = Dense(700, activation="relu")(x)
x = Dense(270, activation="linear")(x)
output = x
model1=Model(input_tensor, output)
model1.summary()
# train parameters:
opt1 = optimizers.Adam(lr=0.00002, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)
model1.compile(optimizer=opt1, loss='mean_squared_error', metrics= ['mean_squared_error'])
reduce_LR = callbacks.ReduceLROnPlateau(monitor='mean_squared_error', factor=0.1, patience=5, verbose=1, mode='max', min_delta=0.0001, cooldown=0, min_lr=0)
tensorboard1 = TensorBoard(path+'/log_dir')
logger = CSVLogger(path+'/training.log')
folder_model="./model"
path_model = os.path.join(folder_model, 'model_epoch_{epoch:02d}.hdf5')
model_checkpoint = callbacks.ModelCheckpoint(filepath= path_model,monitor="mean_squared_error", mode="max", verbose=0, save_best_only=False, save_weights_only=False)
Early_stop=callbacks.EarlyStopping(monitor='mean_squared_error', min_delta=0.0001, patience=10, verbose=0, mode='auto', baseline=None)
#Train DAE
history = model1.fit(x_train, x_train_out, batch_size=128, epochs=40, shuffle=True,verbose=1,
callbacks= [model_checkpoint, tensorboard1 , logger , reduce_LR, Early_stop]
)
model1.save('modelDae.h5')
#----------------------------
#-----------------------------------------------------------------------------
# add Dense model
L=len(x_train[0])
input_tensor = Input(shape=(L,))
x=input_tensor
x = Dense(700, activation="relu")(x)
x = Dense(700, activation="relu")(x)
#x = Dense(700, activation="relu")(x)
#x = Dense(270, activation="linear")(x)
x = Dense(1000, activation="relu")(x)
x = Dense(1000, activation="relu")(x)
x = Dense(1000, activation="relu")(x)
x = Dense(1000, activation="relu")(x)
x = Dense(1000, activation="relu")(x)
x = Dense(103, activation="linear")(x)
output = x
model=Model(input_tensor, output)
model.summary()
model.compile(optimizer=opt1, loss='mean_squared_error', metrics= ['mean_squared_error'])
# copy weights from model1 to model
WeightL1 = model1.layers[1].get_weights()
model.layers[1].set_weights(WeightL1)
WeightL2 = model1.layers[2].get_weights()
model.layers[2].set_weights(WeightL2)
# feeze DAE weight to prevent them from training
model.layers[1].trainable=False
model.layers[2].trainable=False
opt1 = optimizers.Adam(lr=0.0002, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)
reduce_LR = callbacks.ReduceLROnPlateau(monitor='mean_squared_error', factor=0.1, patience=5, verbose=1, mode='max', min_delta=0.0001, cooldown=0, min_lr=0)
tensorboard1 = TensorBoard(path+'/log_dir')
logger = CSVLogger(path+'/training.log')
folder_model="./model"
path_model = os.path.join(folder_model, 'model_epoch_{epoch:02d}.hdf5')
model_checkpoint = callbacks.ModelCheckpoint(filepath= path_model,monitor="mean_squared_error", mode="max", verbose=0, save_best_only=False, save_weights_only=False)
Early_stop=callbacks.EarlyStopping(monitor='mean_squared_error', min_delta=0.0001, patience=10, verbose=0, mode='auto', baseline=None)
model.compile(optimizer=opt1, loss='mean_squared_error', metrics= ['mean_squared_error'])
# train model
history = model.fit(x_train, train_lbl, batch_size=128, epochs=40, validation_data=(x_val, y_val),shuffle=True, verbose=1,
callbacks= [model_checkpoint, tensorboard1, logger, reduce_LR, Early_stop]
)
model.save('ModelDAE3.h5')
#------------------------------------------------------------------------------
PathTest=path+'\TestMat'
PathOut=path+'\TestOut'
import glob
D=glob.glob(PathTest+"/*.mat")
NumTest=len(D)
for i in range(NumTest):
CB= scipy.io.loadmat(D[i])
CB_value=CB['CB_context']
y_predict1 = model1.predict(CB_value)
y_predict = model.predict(y_predict1)
D2=D[i].replace(PathTest, PathOut)
scipy.io.savemat(D2, {'lbl': y_predict})
#------------------------------------------------------------------------------