/
run_svm_params.py
34 lines (25 loc) · 1.39 KB
/
run_svm_params.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
# -*- coding: utf-8 -*-
import pickle
import numpy as np
from sklearn import svm
#Don't forget to change this value AND also The filename on the second to last line!
experiment_number = 10
print "Loading datasets..."
#train_samples = pickle.load(open("Models/SC/trainset%d.pkl"%experiment_number,'rb'))
#train_samples = pickle.load(open("Models/AutoE/trainset%d.pkl"%experiment_number,'rb'))
train_samples = pickle.load(open("Models/RBM/trainset%d.pkl"%experiment_number,'rb'))
train_outputs = pickle.load(open("Models/train_outputs.pkl",'rb'))[:1000]
#valid_samples = pickle.load(open("Models/SC/validset%d.pkl"%experiment_number,'rb'))
#valid_samples = pickle.load(open("Models/AutoE/validset%d.pkl"%experiment_number,'rb'))
valid_samples = pickle.load(open("Models/RBM/validset%d.pkl"%experiment_number,'rb'))
valid_outputs = pickle.load(open("Models/valid_outputs.pkl",'rb'))[:1000]
print "Training the svm"
svm = svm.SVC() # Default uses RBF
svm.fit(train_samples, train_outputs)
print "Predicting..."
train_score = svm.score(train_samples,train_outputs)
valid_score = svm.score(valid_samples,valid_outputs)
print "Training accuracy: %.3f, validation accuracy¸: %.3f"%(train_score, valid_score)
#Save the output to file.
with open("Outputs/RBM_param_tests.txt", "a") as myfile:
myfile.write("Experiment %d, training accuracy: %.3f, validation accuracy: %.3f \n"%(experiment_number,train_score, valid_score))