04b-mmf_mini_ae.py
4.9 KB
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
# coding: utf-8
# In[2]:
# Import
import gensim
from scipy import sparse
import itertools
from sklearn import preprocessing
from keras.models import Sequential
from keras.optimizers import SGD,Adam
from mlp import *
import sklearn.metrics
import shelve
import pickle
from utils import *
import sys
import os
import json
# In[4]:
infer_model=shelve.open("{}".format(sys.argv[2]))
in_dir = sys.argv[1]
#['ASR', 'TRS', 'LABEL']
# In[6]:
hidden_size=[ 100 , 50, 100 ]
input_activation="tanh"
output_activation="tanh"
loss="mse"
epochs=1000
batch=1
patience=60
do_do=[False]
sgd = Adam(lr=0.000001)#SGD(lr=0.00001,nesterov=False) #'rmsprop'# Adam(lr=0.00001)#SGD(lr=0.001, momentum=0.9, nesterov=True)
mlp_h = [ 150 ,150 ,150 ]
mlp_loss = "categorical_crossentropy"
mlp_dropouts = []
mlp_sgd = Adam(lr=0.0001)
mlp_epochs = 2000
mlp_batch_size = 8
mlp_output_activation="softmax"
try :
sgd_repr=sgd.get_config()["name"]
except AttributeError :
sgd_repr=sgd
try :
mlp_sgd_repr=mlp_sgd.get_config()["name"]
except AttributeError :
mlp_sgd_repr=mlp_sgd
params={ "h1" : "_".join([ str(x) for x in hidden_size ]),
"inside_activation" : input_activation,
"output_activation" : output_activation,
"do_dropout": "_".join([str(x) for x in do_do]),
"loss" : loss,
"epochs" : epochs ,
"batch_size" : batch,
"patience" : patience,
"sgd" : sgd_repr,
"mlp_h ": "_".join([str(x) for x in mlp_h]),
"mlp_loss ": mlp_loss,
"mlp_dropouts ": "_".join([str(x) for x in mlp_dropouts]),
"mlp_sgd ": mlp_sgd_repr,
"mlp_epochs ": mlp_epochs,
"mlp_batch_size ": mlp_batch_size,
"mlp_output" : mlp_output_activation
}
name = "_".join([ str(x) for x in params.values()])
try:
os.mkdir("{}/{}".format(in_dir,name))
except:
pass
db = shelve.open("{}/{}/ae_model.shelve".format(in_dir,name),writeback=True)
db["params"] = params
db["LABEL"]=infer_model["LABEL"]
#
json.dump(params,
open("{}/{}/ae_model.json".format(in_dir,name),"w"),
indent=4)
keys = ["ASR","TRS"]
db["AE"] = {}
db["LDA"] = {}
for mod in keys :
print mod
db["LDA"][mod] = train_mlp(infer_model["LDA"][mod]["TRAIN"],infer_model["LABEL"][mod]["TRAIN"],
infer_model["LDA"][mod]["DEV"],infer_model["LABEL"][mod]["DEV"],
infer_model["LDA"][mod]["TEST"],infer_model["LABEL"][mod]["TEST"],
mlp_h ,sgd=mlp_sgd,
epochs=mlp_epochs,
batch_size=mlp_batch_size,
input_activation=input_activation,
output_activation=mlp_output_activation,
dropouts=mlp_dropouts,
fit_verbose=0)
res=train_ae(infer_model["LDA"][mod]["TRAIN"],infer_model["LDA"][mod]["DEV"],infer_model["LDA"][mod]["TEST"],
hidden_size,patience = params["patience"],sgd=sgd,
dropouts=do_do,input_activation=input_activation,output_activation=output_activation,
loss=loss,epochs=epochs,batch_size=batch,verbose=0)
mlp_res_list=[]
for layer in res :
mlp_res_list.append(train_mlp(layer[0],infer_model['LABEL'][mod]["TRAIN"],
layer[1],infer_model["LABEL"][mod]["DEV"],
layer[2],infer_model["LABEL"][mod]["TEST"],
mlp_h,loss=mlp_loss,dropouts=mlp_dropouts,sgd=mlp_sgd,epochs=mlp_epochs,
output_activation=mlp_output_activation,
input_activation=input_activation,
batch_size=mlp_batch_size,fit_verbose=0))
db["AE"][mod]=mlp_res_list
mod = "ASR"
mod2= "TRS"
mlp_res_list=[]
res = train_ae(infer_model["LDA"][mod]["TRAIN"],
infer_model["LDA"][mod]["DEV"],
infer_model["LDA"][mod]["TEST"],
hidden_size,dropouts=do_do,patience = params["patience"],
sgd=sgd,input_activation=input_activation,output_activation=output_activation,loss=loss,epochs=epochs,
batch_size=batch,
y_train=infer_model["LDA"][mod]["TRAIN"],
y_dev=infer_model["LDA"][mod2]["DEV"],
y_test=infer_model["LDA"][mod2]["TEST"])
for layer in res :
mlp_res_list.append(train_mlp(layer[0],infer_model["LABEL"][mod]["TRAIN"],
layer[1],infer_model["LABEL"][mod]["DEV"],
layer[2],infer_model["LABEL"][mod]["TEST"],
mlp_h,loss=mlp_loss,dropouts=mlp_dropouts,sgd=mlp_sgd,epochs=mlp_epochs,
output_activation=mlp_output_activation,
input_activation=input_activation,
batch_size=mlp_batch_size,fit_verbose=0))
db["AE"]["SPE"] = mlp_res_list
db.sync()
db.close()