-
Notifications
You must be signed in to change notification settings - Fork 4
/
pledge_trainer.py
107 lines (82 loc) · 4.57 KB
/
pledge_trainer.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
from tensorflow_generator import TensorflowGenerator
from model.keras_model import KerasFeatureVector, KerasFeatureModel
from products_tree import ProductSet, ProductSetError
import json, sys, getopt
import numpy as np
from pledge_evolution import PledgeEvolution
def train_model_from_product(pledge_output_file,index,export_file="", training_epochs=5, batch_size=64, dataset="mnist"):
initial_product_set = ProductSet(pledge_output_file, binary_products=True)
product, original_product = initial_product_set.format_product(prd_index=index)
tensorflow = TensorflowGenerator(product, training_epochs, dataset, product_features=original_product, features_label=initial_product_set.features, batch_size=batch_size)
vector_pdt = tensorflow.model.to_kerasvector()
#initial_product_set.products = [vector_pdt.features]
#product, original_product = initial_product_set.format_product(prd_index=index, sort_features=False)
parsed_product = initial_product_set.format_product(original_product=vector_pdt.features)
if not parsed_product:
return None
product, original_product = parsed_product
tensorflow = TensorflowGenerator(product, training_epochs, dataset, product_features=original_product, features_label=initial_product_set.features, batch_size=batch_size)
f1 = open(export_file, 'w')
f1.write(json.dumps([vector_pdt.to_vector()]))
f1.close()
def train_model_from_json(pledge_output_file,products_file,index=None,export_file="", training_epochs=5, batch_size=64, dataset="mnist"):
initial_product_set = ProductSet(pledge_output_file, binary_products=True)
f1 = open(products_file, 'r')
vects = json.loads(f1.read())
products = [KerasFeatureVector.from_vector(vect) for vect in vects]
vectors_export = []
if index is not None:
if len(index) ==0:
products = [products[int(index)]]
else:
products = products[max(0,int(index[0])):min(len(products),int(index[1]))]
for i,keras_product in enumerate(products):
initial_product_set.binary_products = True
product, original_product = initial_product_set.format_product(original_product=keras_product.features)
tensorflow = TensorflowGenerator(product, training_epochs, dataset, product_features=original_product, features_label=initial_product_set.features, batch_size=batch_size)
print("original accuracy {} new accuracy {}".format(keras_product.accuracy, tensorflow.model.accuracy))
vector_pdt = tensorflow.model.to_kerasvector()
vectors_export.append(vector_pdt.to_vector())
if export_file:
f1 = open(export_file, 'w')
f1.write(json.dumps(vectors_export))
f1.close()
#train_model_from_product('../products/run10/mnist/e0_m0_b0.pdt',0,"../products/run10/mnist/e0_m0_b0_2.json",1)
#train_model_from_json('../products/run10/mnist/e0_m0_b0.pdt',"../products/run10/mnist/e0_m0_b0_2.json",0,"../products/run10/mnist/e0_m0_b0_3.json",1)
#train_model_from_json('../products/run10/mnist/e0_m0_b0.pdt',"../products/run10/mnist/e0_m0_b0_3.json",0,1)
if __name__ == "__main__":
argv = sys.argv[1:]
pledge_product = json_product = export_file = ""
index_product = None
dataset = "cifar"
batch_size = 0
training_epochs = 300
try:
opts, args = getopt.getopt(argv, "hp:j:i:e:t:b:d:", [
"pledge=","json=", "index=","export=", "traing_epoch=", "batch_size=", "dataset="])
except getopt.GetoptError:
pass
print("arguments {}".format(opts))
for opt, arg in opts:
if opt == '-h':
print(
'pledge_trainer.py -p <pledge_product> -j <json_product> -i <index_product> -e <export_file> -t <training_epochs> -b <batch_size> -t <dataset>')
sys.exit()
elif opt in ("-p", "--pledge_product"):
pledge_product = arg
elif opt in ("-j", "--json_product"):
json_product = arg
elif opt in ("-e", "--export_file"):
export_file = arg
elif opt in ("-i", "--index"):
index_product = arg.split("-")
elif opt in ("-t", "--training_epochs"):
training_epochs = int(arg)
elif opt in ("-b", "--batch_size"):
batch_size = int(arg)
elif opt in ("-d", "--dataset"):
dataset = arg
if json_product:
train_model_from_json(pledge_product,json_product,index_product,export_file, training_epochs, batch_size, dataset)
else:
train_model_from_product(pledge_product,index_product,export_file, training_epochs, batch_size, dataset)