-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathmodel.py
201 lines (174 loc) · 8.76 KB
/
model.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
import NDN3.NDN as NDN
from NDN3 import NDNutils
import numpy as np
class Model:
def __init__(self,data_loader,args):
self.data_loader = data_loader
self.width = data_loader.width
self.height = data_loader.height
self.out_num = data_loader.num_neurons
self.args = args
self.net_name='generic'
self.opt_params = {}
def get_params(self):
return {}
def get_net(self, seed):
params = self.get_params()
print(self.opt_params)
bs = self.get_opt_params()['batch_size']
seed = self.get_opt_params()['seed'] if 'seed' in self.get_opt_params() else seed
net = NDN.NDN(params,
input_dim_list=[[1, self.data_loader.width, self.data_loader.height]],
batch_size=bs,
noise_dist='poisson',
tf_seed=seed)
return net
def get_opt_params(self):
return self.opt_params
def get_name(self):
name = self.net_name
for key,value in self.args.items():
name+=f'-{key}{value}'
return name
class SimpleConvModel(Model):
def __init__(self,data_loader,args):
super().__init__(data_loader,args)
epochs = 5000
self.opt_params = {'display': 1,'batch_size': 16, 'use_gpu': False, 'epochs_summary': epochs//50, 'epochs_training': epochs, 'learning_rate': 0.001}
self.opt_params.update(self.args)
self.net_name = 'conv'
def get_params(self):
params = NDNutils.ffnetwork_params(
input_dims=[1, self.width, self.height],
layer_sizes=[self.args['channels'],self.args['channels'], int(0.2*self.out_num), self.out_num], # paper: 9, 0.2*output_shape
ei_layers=[None,None, None, None],
normalization=[0,0, 0, 0],
layer_types=['var','conv', self.args['hidden_lt'], 'normal'],
act_funcs=['lin','softplus', 'softplus','softplus'],
shift_spacing=[1,(self.args['c_size']+1)//2, 1, 1],
conv_filter_widths=[0,self.args['c_size'], 0, 0],
reg_list={
'd2x': [None,self.args['cd2x'], None , None],
self.args['hidden_t']:[None,None, self.args['hidden_s'], None],
'l2':[0.1,None, None, 0.1],
})
params['weights_initializers']=['normal','normal','normal','normal']
params['biases_initializers']=['normal','trunc_normal','trunc_normal','trunc_normal']
return params
class SimpleConvGANModel(Model):
def __init__(self,data_loader,args):
super().__init__(data_loader,args)
epochs = 5000
self.opt_params = {'display': 1,'batch_size': 16, 'use_gpu': False, 'epochs_summary': epochs//50, 'epochs_training': epochs, 'learning_rate': 0.001}
self.opt_params.update(self.args)
self.net_name = 'conv'
def get_params(self):
params = NDNutils.ffnetwork_params(
input_dims=[10],
layer_sizes=[[31,31],30, int(0.2*self.out_num), self.out_num], # paper: 9, 0.2*output_shape
ei_layers=[None,None, None, None],
normalization=[0,0, 0, 0],
layer_types=['normal','conv', 'conv', 'normal'],
act_funcs=['lin','softplus', 'softplus','softplus'],
shift_spacing=[1,(7+1)//2, 1, 1],
conv_filter_widths=[0,7, 0, 0],
reg_list={
'd2x': [None,0.2, None , None],
'l2':[0.1,None, None, 0.1],
})
params['weights_initializers']=['normal','normal','normal','normal']
params['biases_initializers']=['normal','trunc_normal','trunc_normal','trunc_normal']
return params
class FCModel(Model):
def __init__(self,data_loader,args):
super().__init__(data_loader,args)
epochs = 5000
self.opt_params = {'batch_size': 16, 'use_gpu': False, 'epochs_summary': epochs//50, 'epochs_training': epochs, 'learning_rate': 0.001}
self.opt_params.update(self.args)
self.net_name = 'basicFC'
def get_params(self):
hsm_params = NDNutils.ffnetwork_params(
input_dims=[1, self.width, self.height],
layer_sizes=[int(self.args['hidden']*self.out_num),int(self.args['hidden']*self.out_num), self.out_num], # paper: 9, 0.2*output_shape
ei_layers=[None, None, None],
normalization=[0, 0, 0],
layer_types=['var','normal','normal'],
act_funcs=['lin','softplus','softplus'],
reg_list={
'l2':[0.1,None, self.args['reg_l']],
'd2x':[None,self.args['reg_h'], None],
})
hsm_params['weights_initializers']=['normal','normal','normal']
hsm_params['biases_initializers']=['trunc_normal','trunc_normal','trunc_normal']
return hsm_params
class DoGModel(Model):
def __init__(self,data_loader,args):
super().__init__(data_loader,args)
epochs = 5000
self.opt_params = {'batch_size': 16, 'use_gpu': False, 'epochs_summary': epochs//50, 'epochs_training': epochs, 'learning_rate': 0.001}
self.opt_params.update(self.args)
self.net_name = 'DoG'
def get_params(self):
hsm_params = NDNutils.ffnetwork_params(
input_dims=[1, self.width, self.height],
layer_sizes=[self.args['filt_size'],self.args['filt_size'],int(self.args['perc_output']*self.out_num), self.out_num], # paper: 9, 0.2*output_shape
ei_layers=[None,None, None, None],
normalization=[0,0,0, 0],
layer_types=['var','diff_of_gaussians','normal','normal'],
act_funcs=['lin','lin','softplus','softplus'],
reg_list={
'l2':[0.1,None,None, 0.1],
})
hsm_params['weights_initializers']=['normal','random','normal','normal']
hsm_params['biases_initializers']=['trunc_normal','trunc_normal','trunc_normal','trunc_normal']
return hsm_params
class ConvDoGModel(Model):
def __init__(self,data_loader,args):
super().__init__(data_loader,args)
epochs = 5000
self.opt_params = {'batch_size': 16, 'use_gpu': False, 'epochs_summary': epochs//50, 'epochs_training': epochs, 'learning_rate': 0.001}
self.opt_params.update(self.args)
self.net_name = 'convDoG'
def get_params(self):
hsm_params = NDNutils.ffnetwork_params(
input_dims=[1, self.width, self.height],
layer_sizes=[int(self.args['hidden']),int(self.args['hidden']),int(0.2*self.out_num), self.out_num], # paper: 9, 0.2*output_shape
ei_layers=[None,None, None, None],
normalization=[0,0,0, 0],
layer_types=['var','conv_diff_of_gaussians', self.args['layer'], 'normal'],
act_funcs=['lin','lin','softplus','softplus'],
shift_spacing=[1,(self.args['c_size']+1)//2, 1,1],
conv_filter_widths=[self.args['c_size'],self.args['c_size'], 0, 0],
reg_list={
'l2':[0.1,None,None, 0.1],
'l1':[None,None,self.args['reg_h'], None],
})
hsm_params['weights_initializers']=['normal','random','normal','normal']
hsm_params['biases_initializers']=['trunc_normal','trunc_normal','trunc_normal','trunc_normal']
return hsm_params
class ICLRModel(Model):
def get_params(self):
params = NDNutils.ffnetwork_params(
input_dims=[1, self.width, self.height],
layer_sizes=[self.args['channels'],self.args['channels'],self.args['channels'], self.out_num],
layer_types=['conv', 'conv', 'conv', 'sep'],
act_funcs=['softplus', 'softplus', 'lin', 'softplus'],
shift_spacing=[1, 1, 1, None],
reg_list={
#'d2x': [0.03, 0.015, 0.015, None],
'l1': [None, None, None, 0.02]
})
params['conv_filter_widths'] = [13, 5, 5, None]
params['weights_initializers'] = ['trunc_normal',
'trunc_normal', 'trunc_normal', 'trunc_normal']
params['bias_initializers'] = ['zeros', 'zeros', 'zeros', 'trunc_normal']
params['pos_constraint'] = [False, False, False, True]
return params
def get_net(self, params):
net = super().get_net(params)
net.log_correlation = 'filter-low-std-gold'
net.networks[-1].layers[-1].biases = 0.5 * np.log(np.exp(self.data_loader.means) - 1)
return net
def get_opt_params(self):
epochs = 2000
return {'batch_size': 256, 'use_gpu': False, 'epochs_summary': 25, 'epochs_training': epochs, 'learning_rate': 0.002}