-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathloop_cycle3.py
188 lines (143 loc) · 5.83 KB
/
loop_cycle3.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
from keras.datasets import mnist
from keras.utils import to_categorical
from keras.models import Sequential, load_model
from keras.layers import Dense, Conv2D, Flatten,Dropout, MaxPooling2D, AveragePooling2D, Activation
import keras
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from keras import backend as K
import sys
rep = sys.argv[1]
path = "Cycle3/rep"+rep
train = 1000
val = 500
test = 1000
#test = 70000 - (train+val)
rand_seed = int(sys.argv[2])
(Xtrain, ytrain), (Xtest, ytest) = mnist.load_data()
x = np.concatenate((Xtrain, Xtest))
y = np.concatenate((ytrain, ytest))
Xtrain, Xtemp, ytrain, ytemp = train_test_split(x, y, train_size=train, random_state=rand_seed)
Xval, Xtest, yval, ytest = train_test_split(Xtemp, ytemp, train_size=val, random_state=rand_seed)
#Cycle 1
Xtest, X_remaining, ytest, y_remaining = train_test_split(Xtest, ytest, train_size=test, random_state=rand_seed)
#Cycle 2
Xtest, X_remaining, ytest, y_remaining = train_test_split(X_remaining, y_remaining, train_size=test, random_state=rand_seed)
#Cycle 3
Xtest, X_remaining, ytest, y_remaining = train_test_split(X_remaining, y_remaining, train_size=test, random_state=rand_seed)
# Xtest, X_remaining, ytest, y_remaining = train_test_split(X_remaining, y_remaining, train_size=test, random_state=rand_seed)
# Xtest, X_remaining, ytest, y_remaining = train_test_split(X_remaining, y_remaining, train_size=test, random_state=rand_seed)
# Xtest, X_remaining, ytest, y_remaining = train_test_split(X_remaining, y_remaining, train_size=test, random_state=rand_seed)
tot_train_examples = train
tot_test_examples = val
width=28
height=28
channels = 1
f_size1 = 32
f_size2= 16
Xtrain_reshaped = Xtrain.reshape(tot_train_examples,width,height,channels)
Xval_reshaped = Xval.reshape(tot_test_examples,width,height,channels)
# # #### Inversione labels ####
# index1 = np.where(ytest == 2)
# index2 = np.where(ytest == 7)
# # index1 = np.where(ytrain == 5)
# # index2 = np.where(ytrain == 6)
# index1 = index1[0][0: int(index1[0].shape[0])]
# ytest[index1] = 7
# # ytrain[index1] = 6
# index2 = index2[0][0: int(index2[0].shape[0])]
# ytest[index2] = 2
# # ytrain[index2] = 5
# # ################################
print('New shape ',Xtrain_reshaped[0].shape)
y_train_cat = to_categorical(ytrain)
y_val_cat = to_categorical(yval)
print(y_train_cat[0])
model = load_model("Cycle1/rep"+rep+"/modelC_loop.h5")
Xtest_reshaped = Xtest.reshape(test,width,height,channels)
y_test_cat = to_categorical(ytest)
#print(Xtest_reshaped.shape)
classification_v = model.predict_classes(Xval_reshaped)
#print(classification_v)
score_final_v = model.predict(Xval_reshaped)
classification = model.predict_classes(Xtest_reshaped)
#print(classification)
print(model.evaluate(Xval_reshaped,y_val_cat))
print(model.evaluate(Xtest_reshaped,y_test_cat))
score_final = model.predict(Xtest_reshaped)
# print(score_final)
get_3rd_layer_output = K.function([model.layers[0].input],[model.layers[3].output])
layer_output = get_3rd_layer_output(np.expand_dims(Xtest_reshaped[0], axis=0))[0]
#print(layer_output.shape)
#training set csv printing
trainingset=pd.DataFrame()
for i in range(0, train):
ma = np.matrix(Xtrain_reshaped[i])
ar = ma.flatten()
res = str(ar)[1:-1]
res2 = str(res)[1:-1]
array=pd.DataFrame(ar)
array.insert(784,'label',ytrain[i])
trainingset=trainingset.append(array)
trainingset.to_csv(path+'/training.csv', index = False, header = True)
print("training.csv completed")
#validation set csv printing
validationset=pd.DataFrame()
for i in range(0, val):
ma = np.matrix(Xval_reshaped[i])
ar = ma.flatten()
res = str(ar)[1:-1]
res2 = str(res)[1:-1]
array=pd.DataFrame(ar)
array.insert(784,'label',yval[i])
array.insert(785,'SUT',classification_v[i])
array.insert(786,'PredictedLabel0',score_final_v[i][0])
array.insert(787,'PredictedLabel1',score_final_v[i][1])
array.insert(788,'PredictedLabel2',score_final_v[i][2])
array.insert(789,'PredictedLabel3',score_final_v[i][3])
array.insert(790,'PredictedLabel4',score_final_v[i][4])
array.insert(791,'PredictedLabel5',score_final_v[i][5])
array.insert(792,'PredictedLabel6',score_final_v[i][6])
array.insert(793,'PredictedLabel7',score_final_v[i][7])
array.insert(794,'PredictedLabel8',score_final_v[i][8])
array.insert(795,'PredictedLabel9',score_final_v[i][9])
validationset=validationset.append(array)
validationset.to_csv(path+'/validation.csv', index = False, header = True)
print("validation.csv completed")
#test set csv printing
testset=pd.DataFrame()
for i in range(0, test):
#print(Xtest_reshaped[i].shape)
ma = np.matrix(Xtest_reshaped[i])
ar = ma.flatten()
res = str(ar)[1:-1]
res2 = str(res)[1:-1]
# print(res2)
# print(ytest[0])
array=pd.DataFrame(ar)
array.insert(784,'label',ytest[i])
array.insert(785,'SUT',classification[i])
array.insert(786,'PredictedLabel0',score_final[i][0])
array.insert(787,'PredictedLabel1',score_final[i][1])
array.insert(788,'PredictedLabel2',score_final[i][2])
array.insert(789,'PredictedLabel3',score_final[i][3])
array.insert(790,'PredictedLabel4',score_final[i][4])
array.insert(791,'PredictedLabel5',score_final[i][5])
array.insert(792,'PredictedLabel6',score_final[i][6])
array.insert(793,'PredictedLabel7',score_final[i][7])
array.insert(794,'PredictedLabel8',score_final[i][8])
array.insert(795,'PredictedLabel9',score_final[i][9])
if(ytest[i] < 5):
array.insert(796,'EP', 0)
else:
array.insert(796,'EP', 1)
if(ytest[i] in (0, 3, 6, 8, 9)):
array.insert(797,'FP', 0)
elif(ytest[i] in (1, 4, 7)):
array.insert(797,'FP', 1)
else:
array.insert(797,'FP', 2)
testset=testset.append(array)
testset.to_csv(path+'/test.csv', index = False, header = True)
print("test.csv completed")