04b-mmf_mini_ae.py
6.74 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
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
# 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 keras.layers.advanced_activations import ELU,PReLU
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]:
if len(sys.argv) > 4 :
features_key = sys.argv[4]
else :
features_key = "LDA"
save_projection = True
json_conf =json.load(open(sys.argv[3]))
ae_conf = json_conf["ae"]
hidden_size= ae_conf["hidden_size"]
input_activation = None
print ae_conf["input_activation"]
if ae_conf["input_activation"] == "elu":
print " ELU"
input_activation = PReLU()
else:
print " ELSE"
input_activation = ae_conf["input_activation"]
#input_activation=ae_conf["input_activation"]
output_activation=ae_conf["output_activation"]
loss=ae_conf["loss"]
epochs=ae_conf["epochs"]
batch=ae_conf["batch"]
patience=ae_conf["patience"]
do_do=ae_conf["do"]
try:
k = ae_conf["sgd"]
if ae_conf["sgd"]["name"] == "adam":
sgd = Adam(lr=ae_conf["sgd"]["lr"])#SGD(lr=0.00001,nesterov=False) #'rmsprop'# Adam(lr=0.00001)#SGD(lr=0.001, momentum=0.9, nesterov=True)
elif ae_conf["sgd"]["name"] == "sgd":
sgd = SGD(lr=ae_conf["sgd"]["lr"])
except:
sgd = ae_conf["sgd"]
mlp_conf = json_conf["mlp"]
mlp_h = mlp_conf["hidden_size"]
mlp_loss = mlp_conf["loss"]
mlp_dropouts = mlp_conf["do"]
mlp_epochs = mlp_conf["epochs"]
mlp_batch_size = mlp_conf["batch"]
mlp_input_activation=mlp_conf["input_activation"]
mlp_output_activation=mlp_conf["output_activation"]
try:
k = mlp_conf["sgd"]
if mlp_conf["sgd"]["name"] == "adam":
mlp_sgd = Adam(lr=mlp_conf["sgd"]["lr"])#SGD(lr=0.00001,nesterov=False) #'rmsprop'# Adam(lr=0.00001)#SGD(lr=0.001, momentum=0.9, nesterov=True)
elif mlp_conf["sgd"]["name"] == "sgd":
mlp_sgd = SGD(lr=mlp_conf["sgd"]["lr"])
except:
mlp_sgd = mlp_conf["sgd"]
name = json_conf["name"]
try:
os.mkdir("{}/{}".format(in_dir,name))
except:
pass
db = shelve.open("{}/{}/ae_model.shelve".format(in_dir,name),writeback=True)
db["LABEL"]=infer_model["LABEL"]
#
keys = infer_model[features_key].keys()
db["AE"] = {}
db[features_key] = {}
for mod in keys :
print infer_model[features_key][mod]["TRAIN"].shape
print infer_model[features_key][mod]["DEV"].shape
print infer_model[features_key][mod]["TEST"].shape
db[features_key][mod] = train_mlp(infer_model[features_key][mod]["TRAIN"],infer_model["LABEL"][mod]["TRAIN"],
infer_model[features_key][mod]["DEV"],infer_model["LABEL"][mod]["DEV"],
infer_model[features_key][mod]["TEST"],infer_model["LABEL"][mod]["TEST"],
mlp_h ,sgd=mlp_sgd,
epochs=mlp_epochs,
batch_size=mlp_batch_size,
input_activation=mlp_input_activation,
output_activation=mlp_output_activation,
dropouts=mlp_dropouts,
fit_verbose=0)
print input_activation
res=train_ae(infer_model[features_key][mod]["TRAIN"],infer_model[features_key][mod]["DEV"],infer_model[features_key][mod]["TEST"],
hidden_size,patience = 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 nb,layer in enumerate(res) :
if save_projection:
pd = pandas.DataFrame(layer[0])
col_count = (pd.sum(axis=0) != 0)
pd = pd.loc[:,col_count]
pd.to_hdf("{}/{}/AE_{}_{}_df.hdf".format(in_dir,name,nb,mod),"TRAIN")
pd = pandas.DataFrame(layer[1])
pd = pd.loc[:,col_count]
pd.to_hdf("{}/{}/AE_{}_{}_df.hdf".format(in_dir,name,nb,mod),"DEV")
pd = pandas.DataFrame(layer[2])
pd = pd.loc[:,col_count]
pd.to_hdf("{}/{}/AE_{}_{}_df.hdf".format(in_dir,name,nb,mod),"TEST")
del pd
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=mlp_input_activation,
batch_size=mlp_batch_size,fit_verbose=0))
db["AE"][mod]=mlp_res_list
if "ASR" in keys and "TRS" in keys:
mod = "ASR"
mod2= "TRS"
mlp_res_list=[]
res = train_ae(infer_model[features_key][mod]["TRAIN"],
infer_model[features_key][mod]["DEV"],
infer_model[features_key][mod]["TEST"],
hidden_size,dropouts=do_do,patience = patience,
sgd=sgd,input_activation=input_activation,output_activation=output_activation,loss=loss,epochs=epochs,
batch_size=batch,
y_train=infer_model[features_key][mod]["TRAIN"],
y_dev=infer_model[features_key][mod2]["DEV"],
y_test=infer_model[features_key][mod2]["TEST"])
for nb,layer in enumerate(res) :
if save_projection:
pd = pandas.DataFrame(layer[0])
col_count= (pd.sum(axis=0) != 0)
pd = pd.loc[:,col_count]
pd.to_hdf("{}/{}/AE_{}_{}_df.hdf".format(in_dir,name,nb,"SPE"),"TRAIN")
pd = pandas.DataFrame(layer[1])
pd = pd.loc[:,col_count]
pd.to_hdf("{}/{}/AE_{}_{}_df.hdf".format(in_dir,name,nb,"SPE"),"DEV")
pd = pandas.DataFrame(layer[2])
pd = pd.loc[:,col_count]
pd.to_hdf("{}/{}/AE_{}_{}_df.hdf".format(in_dir,name,nb,"SPE"),"TEST")
del pd
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()