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LDA/00-mmf_make_features.py
Diff comments   View file @ d1012a7
1 import sys 1 import sys
2 import os 2 import os
3 3
4 import pandas 4 import pandas
5 import numpy 5 import numpy
6 import shelve 6 import shelve
7 7
8 from sklearn.preprocessing import LabelBinarizer 8 from sklearn.preprocessing import LabelBinarizer
9 9
10 from utils import select_mmf as select 10 from utils import select_mmf as select
11 11
12 input_dir = sys.argv[1] # Dossier de premire niveau contient ASR et TRS 12 input_dir = sys.argv[1] # Dossier de premire niveau contient ASR et TRS
13 level = sys.argv[2] # taille de LDA ( -5) voulu 13 level = sys.argv[2] # taille de LDA ( -5) voulu
14 output_dir = sys.argv[3] 14 output_dir = sys.argv[3]
15 15
16 lb=LabelBinarizer() 16 lb=LabelBinarizer()
17 #y_train=lb.fit_transform([utils.select(ligneid) for ligneid in origin_corps["LABEL"]["TRAIN"]]) 17 #y_train=lb.fit_transform([utils.select(ligneid) for ligneid in origin_corps["LABEL"]["TRAIN"]])
18 18
19 19
20 data = shelve.open("{}/mmf_{}.shelve".format(output_dir,level),writeback=True) 20 data = shelve.open("{}/mmf_{}.shelve".format(output_dir,level),writeback=True)
21 data["LABEL"]= {} 21 data["LABEL"]= {}
22 data["LDA"] = {"ASR":{},"TRS":{}} 22 data["LDA"] = {"ASR":{},"TRS":{}}
23 for mod in ["ASR", "TRS" ]: 23 for mod in ["ASR", "TRS" ]:
24 train = pandas.read_table("{}/{}/train_{}.ssv".format(input_dir, mod, level), sep=" ", header=None ) 24 train = pandas.read_table("{}/{}/train_{}.tab".format(input_dir, mod, level), sep=" ", header=None )
25 dev = pandas.read_table("{}/{}/dev_{}.ssv".format(input_dir, mod, level), sep=" ", header=None ) 25 dev = pandas.read_table("{}/{}/dev_{}.tab".format(input_dir, mod, level), sep=" ", header=None )
26 test = pandas.read_table("{}/{}/test_{}.ssv".format(input_dir, mod, level), sep=" ", header=None ) 26 test = pandas.read_table("{}/{}/test_{}.tab".format(input_dir, mod, level), sep=" ", header=None )
27 27
28 y_train = train.iloc[:,0].apply(select) 28 y_train = train.iloc[:,0].apply(select)
29 y_dev = dev.iloc[:,0].apply(select) 29 y_dev = dev.iloc[:,0].apply(select)
30 y_test = test.iloc[:,0].apply(select) 30 y_test = test.iloc[:,0].apply(select)
31 lb.fit(y_train) 31 lb.fit(y_train)
32 data["LABEL"][mod]={"TRAIN":lb.transform(y_train),"DEV":lb.transform(y_dev), "TEST": lb.transform(y_test)} 32 data["LABEL"][mod]={"TRAIN":lb.transform(y_train),"DEV":lb.transform(y_dev), "TEST": lb.transform(y_test)}
33 33
34 # data["LDA"][mod]={'ASR':[]} 34 # data["LDA"][mod]={'ASR':[]}
35 print data["LDA"][mod]
36 print train.values 35 print train.values
37 data["LDA"][mod]["TRAIN"]=train.iloc[:,1:-1].values 36 data["LDA"][mod]["TRAIN"]=train.iloc[:,1:-1].values
38 data["LDA"][mod]["DEV"]=dev.iloc[:,1:-1].values 37 data["LDA"][mod]["DEV"]=dev.iloc[:,1:-1].values
39 data["LDA"][mod]["TEST"]=test.iloc[:,1:-1].values 38 data["LDA"][mod]["TEST"]=test.iloc[:,1:-1].values
40 39
40 print data["LDA"][mod]["TRAIN"].shape
41 data.sync() 41 data.sync()
42 data.close() 42 data.close()
LDA/02-lda_split.py
View file @ d1012a7
1 import gensim File was deleted
2 import os
3 import sys
4 import pickle
5 from gensim.models.ldamodel import LdaModel
6 from gensim.models.ldamulticore import LdaMulticore
7 from collections import Counter
8 import numpy as np
9 import codecs
10 import shelve
11 import logging
12
13 def calc_perp(in_dir,train):
14 name = in_dir.split("/")[-1]
15 # s40_it1_sw50_a0.01_e0.1_p6_c1000
16 sw_size = int(name.split("_")[2][2:])
17
18 logging.warning(" go {} ".format(name))
19
20
21 logging.warning("Redo Vocab and stop")
22 asr_count=Counter([ x for y in train["ASR_wid"]["TRAIN"] for x in y])
23 trs_count=Counter([ x for y in train["TRS_wid"]["TRAIN"] for x in y])
24 asr_sw = [ x[0] for x in asr_count.most_common(sw_size) ]
25 trs_sw = [ x[0] for x in trs_count.most_common(sw_size) ]
26 stop_words=set(asr_sw) | set(trs_sw)
27
28 logging.warning("TRS to be done")
29 entry = Query()
30 value=db.search(entry.name == name)
31 if len(value) > 0 :
32 logging.warning("{} already done".format(name))
33 return
34
35 dev_trs=[ [ (x,y) for x,y in Counter(z).items() if x not in stop_words] for z in train["TRS_wid"]["DEV"]]
36 lda_trs = LdaModel.load("{}/lda_trs.model".format(in_dir))
37 perp_trs = lda_trs.log_perplexity(dev_trs)
38 logging.warning("ASR to be done")
39 dev_asr = [ [ (x,y) for x,y in Counter(z).items() if x not in stop_words] for z in train["ASR_wid"]["DEV"]]
40 lda_asr = LdaModel.load("{}/lda_asr.model".format(in_dir))
41 perp_asr = lda_asr.log_perplexity(dev_asr)
42 logging.warning("ASR saving")
43 res_dict = {"name" : name, "asr" : perp_asr, "trs" : perp_trs}
44 return res_dict
45
46
47
48
49 def train_lda(out_dir,train,name,size,it,sw_size,alpha,eta,passes,chunk):
50 output_dir = "{}/s{}_it{}_sw{}_a{}_e{}_p{}_c{}".format(out_dir,size,it,sw_size,alpha,eta,passes,chunk)
51 os.mkdir(output_dir)
52 logging.info(output_dir+" to be done")
53 asr_count=Counter([ x for y in train["ASR_wid"]["TRAIN"] for x in y])
54 trs_count=Counter([ x for y in train["TRS_wid"]["TRAIN"] for x in y])
55 asr_sw = [ x[0] for x in asr_count.most_common(sw_size) ]
56 trs_sw = [ x[0] for x in trs_count.most_common(sw_size) ]
57 stop_words=set(asr_sw) | set(trs_sw)
58
59 logging.info("TRS to be done")
60
61 lda_trs = LdaModel(corpus=[ [ (x,y) for x,y in Counter(z).items() if x not in stop_words] for z in train["TRS_wid"]["TRAIN"]], id2word=train["vocab"], num_topics=int(size), chunksize=1000,iterations=it)
62
63 logging.info("ASR to be done")
64 lda_asr = LdaModel(corpus=[ [ (x,y) for x,y in Counter(z).items() if x not in stop_words] for z in train["ASR_wid"]["TRAIN"]], id2word=train["vocab"], num_topics=int(size), chunksize=1000,iterations=it)
65
66 #logger.info("ASR saving")
67 #lda_asr.save("{}/lda_asr.model".format(output_dir,name,size,it))
68 #lda_trs.save("{}/lda_trs.model".format(output_dir,name,size,it))
69
70
71 out_file_asr=codecs.open("{}/asr_wordTopic.txt".format(output_dir),"w","utf-8")
72 out_file_trs=codecs.open("{}/trs_wordTopic.txt".format(output_dir),"w","utf-8")
73
74 dico = train["vocab"]
75 print >>out_file_asr, ",\t".join( [ dico[x] for x in range(len(train["vocab"]))])
76 for line in lda_asr.expElogbeta:
77 nline = line / np.sum(line)
78 print >>out_file_asr, ",\t".join( str(x) for x in nline)
79 out_file_asr.close()
80
81 print >>out_file_trs, ",\t".join( [ dico[x] for x in range(len(train["vocab"]))])
82 for line in lda_trs.expElogbeta:
83 nline = line / np.sum(line)
84 print >>out_file_trs, ",\t".join( str(x) for x in nline)
85 out_file_trs.close()
86
87 K = lda_asr.num_topics
88 topicWordProbMat = lda_asr.print_topics(K,10)
89 out_file_asr=codecs.open("{}/asr_best10.txt".format(output_dir),"w","utf-8")
90 for i in topicWordProbMat:
91 print >>out_file_asr,i
92 out_file_asr.close()
93
94 K = lda_trs.num_topics
95 topicWordProbMat = lda_trs.print_topics(K,10)
96 out_file_trs=codecs.open("{}/trs_best10.txt".format(output_dir),"w","utf-8")
97 for i in topicWordProbMat:
98 print >>out_file_trs,i
99 out_file_trs.close()
100
101 if __name__ == "__main__":
102 logging.basicConfig(format='%(levelname)s:%(message)s', level=logging.WARNING)
103
104 input_shelve = sys.argv[1]
105 output_dir = sys.argv[2]
106 size = [ int(x) for x in sys.argv[3].split("_")]
107 workers = int(sys.argv[4])
108 name = sys.argv[5]
109 it = [ int(x) for x in sys.argv[6].split("_")]
110 sw_size = [ int(x) for x in sys.argv[7].split("_")]
111 alpha = ["auto" , "symmetric"] + [ float(x) for x in sys.argv[8].split("_")]
112 eta = ["auto"] + [ float(x) for x in sys.argv[9].split("_")]
113 passes = [ int(x) for x in sys.argv[10].split("_")]
114 chunk = [ int(x) for x in sys.argv[11].split("_")]
115
116 #train=pickle.load(open("{}/newsgroup_bow_train.pk".format(input_dir)))
117 train = shelve.open(input_shelve)
118 out_dir = "{}/{}".format(output_dir,name)
119 os.mkdir(out_dir)
120
121 for s in size:
122 for i in it :
123 for sw in sw_size:
124 for a in alpha:
125 for e in eta:
126 for p in passes:
127 for c in chunk:
128 train_lda(out_dir,train,name,s,i,sw,a,e,p,c)
129
130 1 import gensim
LDA/02b-lda_order.py
View file @ d1012a7
1 import gensim File was deleted
2 import os
3 import sys
4 import pickle
5 from gensim.models.ldamodel import LdaModel
6 from gensim.models.ldamulticore import LdaMulticore
7 from collections import Counter
8 import numpy as np
9 import codecs
10 import shelve
11 import logging
12 import dill
13 from tinydb import TinyDB, where, Query
14 import time
15 from joblib import Parallel, delayed
16
17 def calc_perp(models,train):
18
19
20 stop_words=models[1]
21 name = models[0]
22
23 logging.warning(" go {} ".format(name))
24 logging.warning("TRS to be done")
25 entry = Query()
26 value=db.search(entry.name == name)
27 if len(value) > 0 :
28 logging.warning("{} already done".format(name))
29 return
30
31 dev_trs=[ [ (x,y) for x,y in Counter(z).items() if x not in stop_words] for z in train["TRS_wid"]["DEV"]]
32 lda_trs = models[2]
33 perp_trs = lda_trs.log_perplexity(dev_trs)
34
35 logging.warning("ASR to be done")
36 dev_asr = [ [ (x,y) for x,y in Counter(z).items() if x not in stop_words] for z in train["ASR_wid"]["DEV"]]
37 lda_asr = models[5]
38 perp_asr = lda_asr.log_perplexity(dev_asr)
39 logging.warning("ASR saving")
40 res_dict = {"name" : name, "asr" : perp_asr, "trs" : perp_trs }
41 return res_dict
42
43
44
45
46 def train_lda(out_dir,train,size,it,sw_size,alpha,eta,passes,chunk):
47 name = "s{}_it{}_sw{}_a{}_e{}_p{}_c{}".format(size,it,sw_size,alpha,eta,passes,chunk)
48 logging.warning(name)
49 deep_out_dir = out_dir+"/"+name
50 if os.path.isdir(deep_out_dir):
51 logging.error(name+" already done")
52 return
53 logging.warning(name+" to be done")
54 asr_count=Counter([ x for y in train["ASR_wid"]["TRAIN"] for x in y])
55 trs_count=Counter([ x for y in train["TRS_wid"]["TRAIN"] for x in y])
56 asr_sw = [ x[0] for x in asr_count.most_common(sw_size) ]
57 trs_sw = [ x[0] for x in trs_count.most_common(sw_size) ]
58 stop_words=set(asr_sw) | set(trs_sw)
59
60 logging.warning("TRS to be done")
61
62 lda_trs = LdaModel(corpus=[ [ (x,y) for x,y in Counter(z).items() if x not in stop_words] for z in train["TRS_wid"]["TRAIN"]], id2word=train["vocab"], num_topics=int(size), chunksize=chunk,iterations=it,alpha=alpha,eta=eta,passes=passes)
63
64 logging.warning("ASR to be done")
65 lda_asr = LdaModel(corpus=[ [ (x,y) for x,y in Counter(z).items() if x not in stop_words] for z in train["ASR_wid"]["TRAIN"]], id2word=train["vocab"], num_topics=int(size), chunksize=chunk,iterations=it,alpha=alpha,eta=eta,passes=passes)
66
67 dico = train["vocab"]
68 word_list = [ dico[x] for x in range(len(train["vocab"]))]
69 asr_probs = []
70 for line in lda_asr.expElogbeta:
71 nline = line / np.sum(line)
72 asr_probs.append([ str(x) for x in nline])
73 trs_probs = []
74 for line in lda_trs.expElogbeta:
75 nline = line / np.sum(line)
76 trs_probs.append([str(x) for x in nline])
77
78 K = lda_asr.num_topics
79 topicWordProbMat_asr = lda_asr.print_topics(K,10)
80
81 K = lda_trs.num_topics
82 topicWordProbMat_trs = lda_trs.print_topics(K,10)
83 os.mkdir(deep_out_dir)
84 dill.dump([x for x in stop_words],open(deep_out_dir+"/stopwords.dill","w"))
85 lda_asr.save(deep_out_dir+"/lda_asr.model")
86 lda_trs.save(deep_out_dir+"/lda_trs.model")
87 dill.dump([x for x in asr_probs],open(deep_out_dir+"/lda_asr_probs.dill","w"))
88 dill.dump([x for x in trs_probs],open(deep_out_dir+"/lda_trs_probs.dill","w"))
89
90 return [name, stop_words, lda_asr , asr_probs , topicWordProbMat_asr, lda_trs, trs_probs, topicWordProbMat_trs]
91
92 def train_one(name,train,s,i,sw,a,e,p,c):
93 st=time.time()
94 logging.warning(" ; ".join([str(x) for x in [s,i,sw,a,e,p,c]]))
95 models = train_lda(name,train,s,i,sw,a,e,p,c)
96 if models:
97 m = calc_perp(models,train)
98 #dill.dump(models,open("{}/{}.dill".format(name,models[0]),"wb"))
99 else :
100 m = None
101 e = time.time()
102 logging.warning("fin en : {}".format(e-st))
103 return m
104
105
106
107
108 if __name__ == "__main__":
109 logging.basicConfig(format='%(levelname)s:%(message)s', level=logging.WARNING)
110
111 input_shelve = sys.argv[1]
112 db_path = sys.argv[2]
113 size = [ int(x) for x in sys.argv[3].split("_")]
114 workers = int(sys.argv[4])
115 name = sys.argv[5]
116 it = [ int(x) for x in sys.argv[6].split("_")]
117 sw_size = [ int(x) for x in sys.argv[7].split("_")]
118 if sys.argv[8] != "None" :
119 alpha = [ "symmetric", "auto" ] + [ float(x) for x in sys.argv[8].split("_")]
120 eta = ["auto"] + [ float(x) for x in sys.argv[9].split("_")]
121 else :
122 alpha = ["symmetric"]
123 eta = ["auto"]
124 passes = [ int(x) for x in sys.argv[10].split("_")]
125 chunk = [ int(x) for x in sys.argv[11].split("_")]
126
127 #train=pickle.load(open("{}/newsgroup_bow_train.pk".format(input_dir)))
128 train = shelve.open(input_shelve)
129 try :
130 os.mkdir(name)
131 except :
132 logging.warning(" folder already existe " )
133 db = TinyDB(db_path)
134 nb_model = len(passes) * len(chunk) * len(it) * len(sw_size) * len(alpha) * len(eta) * len(size)
135 logging.warning(" hey will train {} models ".format(nb_model))
136
137 args_list=[]
138 for p in passes:
139 for c in chunk:
140 for i in it :
141 for sw in sw_size:
142 for a in alpha:
143 for e in eta:
144 for s in size:
145 args_list.append((name,train,s,i,sw,a,e,p,c))
146 res_list= Parallel(n_jobs=15)(delayed(train_one)(*args) for args in args_list)
147 for m in res_list :
148 db.insert(m)
149
150 1 import gensim
1 1
2 # coding: utf-8 2 # coding: utf-8
3 3
4 # In[2]: 4 # In[2]:
5 5
6 # Import 6 # Import
7 import gensim 7 import gensim
8 from scipy import sparse 8 from scipy import sparse
9 import itertools 9 import itertools
10 from sklearn import preprocessing 10 from sklearn import preprocessing
11 from keras.models import Sequential 11 from keras.models import Sequential
12 from keras.optimizers import SGD,Adam 12 from keras.optimizers import SGD,Adam
13 from mlp import * 13 from mlp import *
14 import mlp 14 import mlp
15 import sklearn.metrics 15 import sklearn.metrics
16 import shelve 16 import shelve
17 import pickle 17 import pickle
18 from utils import * 18 from utils import *
19 import sys 19 import sys
20 import os 20 import os
21 import json 21 import json
22 # In[4]: 22 # In[4]:
23 23
24 sparse_model=shelve.open("{}".format(sys.argv[2])) 24 sparse_model=shelve.open("{}".format(sys.argv[2]))
25 in_dir = sys.argv[1] 25 in_dir = sys.argv[1]
26 infer_model=shelve.open("{}/infer.shelve".format(in_dir)) 26 infer_model=shelve.open("{}/infer.shelve".format(in_dir))
27 #['ASR', 'TRS', 'LABEL'] 27 #['ASR', 'TRS', 'LABEL']
28 # In[6]: 28 # In[6]:
29 ASR=sparse_model["ASR_wid"] 29 ASR=sparse_model["ASR_wid"]
30 TRS=sparse_model["TRS_wid"] 30 TRS=sparse_model["TRS_wid"]
31 LABEL=sparse_model["LABEL"] 31 LABEL=sparse_model["LABEL"]
32 32
33 33
34 hidden_size=40 34 hidden_size=40
35 input_activation="tanh" 35 input_activation="tanh"
36 out_activation="tanh" 36 out_activation="tanh"
37 loss="mse" 37 loss="mse"
38 epochs=500 38 epochs=500
39 batch=1 39 batch=1
40 patience=60 40 patience=60
41 do_do=False 41 do_do=False
42 sgd = Adam(lr=0.00001)#SGD(lr=0.00001,nesterov=False) #'rmsprop'# Adam(lr=0.00001)#SGD(lr=0.001, momentum=0.9, nesterov=True) 42 sgd = Adam(lr=0.00001)#SGD(lr=0.00001,nesterov=False) #'rmsprop'# Adam(lr=0.00001)#SGD(lr=0.001, momentum=0.9, nesterov=True)
43 try : 43 try :
44 sgd_repr=sgd.get_config()["name"] 44 sgd_repr=sgd.get_config()["name"]
45 except AttributeError : 45 except AttributeError :
46 sgd_repr=sgd 46 sgd_repr=sgd
47 47
48 params={ "h1" : hidden_size, 48 params={ "h1" : hidden_size,
49 "inside_activation" : input_activation, 49 "inside_activation" : input_activation,
50 "out_activation" : out_activation, 50 "out_activation" : out_activation,
51 "do_dropout": do_do, 51 "do_dropout": do_do,
52 "loss" : loss, 52 "loss" : loss,
53 "epochs" : epochs , 53 "epochs" : epochs ,
54 "batch_size" : batch, 54 "batch_size" : batch,
55 "patience" : patience, 55 "patience" : patience,
56 "sgd" : sgd_repr} 56 "sgd" : sgd_repr}
57 name = "_".join([ str(x) for x in params.values()]) 57 name = "_".join([ str(x) for x in params.values()])
58 try: 58 try:
59 os.mkdir("{}/{}".format(in_dir,name)) 59 os.mkdir("{}/{}".format(in_dir,name))
60 except: 60 except:
61 pass 61 pass
62 db = shelve.open("{}/{}/ae_model.shelve".format(in_dir,name),writeback=True) 62 db = shelve.open("{}/{}/ae_model.shelve".format(in_dir,name),writeback=True)
63 db["params"] = params 63 db["params"] = params
64 db["LABEL"]=LABEL 64 db["LABEL"]=LABEL
65 # 65 #
66 json.dump(params, 66 json.dump(params,
67 open("{}/{}/ae_model.json".format(in_dir,name),"w"), 67 open("{}/{}/ae_model.json".format(in_dir,name),"w"),
68 indent=4) 68 indent=4)
69 69
70 keys = ["ASR","TRS"] 70 keys = ["ASR","TRS"]
71 71
72 mlp_h = [ 40 , 25 , 40] 72 mlp_h = [ 512 , 1024 , 2048]
73 mlp_loss ="categorical_crossentropy" 73 mlp_loss ="categorical_crossentropy"
74 mlp_dropouts = [0,0,0,0] 74 mlp_dropouts = [0,0,0,0]
75 mlp_sgd = Adam(0.0001) 75 mlp_sgd = Adam(0.0001)
76 mlp_epochs = 200 76 mlp_epochs = 200
77 mlp_batch_size = 8 77 mlp_batch_size = 8
78 78
79 db["AE"] = {} 79 db["AE"] = {}
80 for mod in keys : 80 for mod in keys :
81 res=train_ae(infer_model["LDA"][mod]["TRAIN"],infer_model["LDA"][mod]["DEV"],infer_model["LDA"][mod]["TEST"],[params["h1"]],patience = params["patience"],sgd=sgd,in_activation="tanh",out_activation="tanh",loss=loss,epochs=epochs,batch_size=batch,verbose=0) 81 res=train_ae(infer_model["LDA"][mod]["TRAIN"],infer_model["LDA"][mod]["DEV"],infer_model["LDA"][mod]["TEST"],[params["h1"]],patience = params["patience"],sgd=sgd,in_activation="tanh",out_activation="tanh",loss=loss,epochs=epochs,batch_size=batch,verbose=0)
82 mlp_res_list=[] 82 mlp_res_list=[]
83 for layer in res : 83 for layer in res :
84 mlp_res_list.append(train_mlp(layer[0],LABEL["TRAIN"],layer[1],LABEL["DEV"],layer[2],LABEL["TEST"],mlp_h,loss=mlp_loss,dropouts=mlp_dropouts,sgd=mlp_sgd,epochs=mlp_epochs,batch_size=mlp_batch_size,fit_verbose=0)) 84 mlp_res_list.append(train_mlp(layer[0],LABEL["TRAIN"],layer[1],LABEL["DEV"],layer[2],LABEL["TEST"],mlp_h,loss=mlp_loss,dropouts=mlp_dropouts,sgd=mlp_sgd,epochs=mlp_epochs,batch_size=mlp_batch_size,fit_verbose=0))
85 db["AE"][mod]=mlp_res_list 85 db["AE"][mod]=mlp_res_list
86 86
87 mod = "ASR" 87 mod = "ASR"
88 mod2= "TRS" 88 mod2= "TRS"
89 mlp_res_list=[] 89 mlp_res_list=[]
90 90
91 res = train_ae(infer_model["LDA"][mod]["TRAIN"],infer_model["LDA"][mod]["DEV"],infer_model["LDA"][mod]["TEST"],[params["h1"]],dropouts=[0],patience = params["patience"],sgd=sgd,in_activation="tanh",out_activation="tanh",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"]) 91 res = train_ae(infer_model["LDA"][mod]["TRAIN"],infer_model["LDA"][mod]["DEV"],infer_model["LDA"][mod]["TEST"],[params["h1"]],dropouts=[0],patience = params["patience"],sgd=sgd,in_activation="tanh",out_activation="tanh",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"])
92 for layer in res : 92 for layer in res :
93 mlp_res_list.append(train_mlp(layer[0],LABEL["TRAIN"],layer[1],LABEL["DEV"],layer[2],LABEL["TEST"],mlp_h,loss=mlp_loss,dropouts=mlp_dropouts,sgd=mlp_sgd,epochs=mlp_epochs,batch_size=mlp_batch_size,fit_verbose=0)) 93 mlp_res_list.append(train_mlp(layer[0],LABEL["TRAIN"],layer[1],LABEL["DEV"],layer[2],LABEL["TEST"],mlp_h,loss=mlp_loss,dropouts=mlp_dropouts,sgd=mlp_sgd,epochs=mlp_epochs,batch_size=mlp_batch_size,fit_verbose=0))
94 94
95 db["AE"]["SPE"] = mlp_res_list 95 db["AE"]["SPE"] = mlp_res_list
96 96
97 97
98 db.close() 98 db.close()
99 99
1 1
2 # coding: utf-8 2 # coding: utf-8
3 import gensim 3 import gensim
4 from scipy import sparse 4 from scipy import sparse
5 import itertools 5 import itertools
6 from sklearn import preprocessing 6 from sklearn import preprocessing
7 from keras.models import Sequential 7 from keras.models import Sequential
8 from keras.optimizers import SGD,Adam 8 from keras.optimizers import SGD,Adam
9 from mlp import * 9 from mlp import *
10 from vae import * 10 from vae import *
11 import sklearn.metrics 11 import sklearn.metrics
12 import shelve 12 import shelve
13 import pickle 13 import pickle
14 from utils import * 14 from utils import *
15 import sys 15 import sys
16 import os 16 import os
17 import json 17 import json
18 # In[4]: 18 # In[4]:
19 19
20 infer_model=shelve.open("{}".format(sys.argv[2])) 20 infer_model=shelve.open("{}".format(sys.argv[2]))
21 in_dir = sys.argv[1] 21 in_dir = sys.argv[1]
22 #['ASR', 'TRS', 'LABEL'] 22 #['ASR', 'TRS', 'LABEL']
23 # In[6]: 23 # In[6]:
24 if len(sys.argv) > 4 : 24 if len(sys.argv) > 4 :
25 features_key = sys.argv[4] 25 features_key = sys.argv[4]
26 else : 26 else :
27 features_key = "LDA" 27 features_key = "LDA"
28 28
29 save_projection = True 29 save_projection = True
30 json_conf =json.load(open(sys.argv[3])) 30 json_conf =json.load(open(sys.argv[3]))
31 vae_conf = json_conf["vae"] 31 vae_conf = json_conf["vae"]
32 32
33 hidden_size= vae_conf["hidden_size"] 33 hidden_size= vae_conf["hidden_size"]
34 input_activation=vae_conf["input_activation"] 34 input_activation=vae_conf["input_activation"]
35 output_activation=vae_conf["output_activation"] 35 output_activation=vae_conf["output_activation"]
36 epochs=vae_conf["epochs"] 36 epochs=vae_conf["epochs"]
37 batch=vae_conf["batch"] 37 batch=vae_conf["batch"]
38 patience=vae_conf["patience"] 38 patience=vae_conf["patience"]
39 latent_dim = vae_conf["latent"] 39 latent_dim = vae_conf["latent"]
40 try: 40 try:
41 k = vae_conf["sgd"] 41 k = vae_conf["sgd"]
42 if vae_conf["sgd"]["name"] == "adam": 42 if vae_conf["sgd"]["name"] == "adam":
43 sgd = Adam(lr=vae_conf["sgd"]["lr"])#SGD(lr=0.00001,nesterov=False) #'rmsprop'# Adam(lr=0.00001)#SGD(lr=0.001, momentum=0.9, nesterov=True) 43 sgd = Adam(lr=vae_conf["sgd"]["lr"])#SGD(lr=0.00001,nesterov=False) #'rmsprop'# Adam(lr=0.00001)#SGD(lr=0.001, momentum=0.9, nesterov=True)
44 elif vae_conf["sgd"]["name"] == "sgd": 44 elif vae_conf["sgd"]["name"] == "sgd":
45 sgd = SGD(lr=vae_conf["sgd"]["lr"]) 45 sgd = SGD(lr=vae_conf["sgd"]["lr"])
46 except: 46 except:
47 sgd = vae_conf["sgd"] 47 sgd = vae_conf["sgd"]
48 48
49 mlp_conf = json_conf["mlp"] 49 mlp_conf = json_conf["mlp"]
50 mlp_h = mlp_conf["hidden_size"] 50 mlp_h = mlp_conf["hidden_size"]
51 mlp_loss = mlp_conf["loss"] 51 mlp_loss = mlp_conf["loss"]
52 mlp_dropouts = mlp_conf["do"] 52 mlp_dropouts = mlp_conf["do"]
53 mlp_epochs = mlp_conf["epochs"] 53 mlp_epochs = mlp_conf["epochs"]
54 mlp_batch_size = mlp_conf["batch"] 54 mlp_batch_size = mlp_conf["batch"]
55 mlp_input_activation=mlp_conf["input_activation"] 55 mlp_input_activation=mlp_conf["input_activation"]
56 mlp_output_activation=mlp_conf["output_activation"] 56 mlp_output_activation=mlp_conf["output_activation"]
57 57
58 58
59 try: 59 try:
60 k = mlp_conf["sgd"] 60 k = mlp_conf["sgd"]
61 if mlp_conf["sgd"]["name"] == "adam": 61 if mlp_conf["sgd"]["name"] == "adam":
62 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) 62 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)
63 elif mlp_conf["sgd"]["name"] == "sgd": 63 elif mlp_conf["sgd"]["name"] == "sgd":
64 mlp_sgd = SGD(lr=mlp_conf["sgd"]["lr"]) 64 mlp_sgd = SGD(lr=mlp_conf["sgd"]["lr"])
65 except: 65 except:
66 mlp_sgd = mlp_conf["sgd"] 66 mlp_sgd = mlp_conf["sgd"]
67 67
68 68
69 name = json_conf["name"] 69 name = json_conf["name"]
70 70
71 try : 71 try :
72 print "make folder " 72 print "make folder "
73 os.mkdir("{}/{}".format(in_dir,name)) 73 os.mkdir("{}/{}".format(in_dir,name))
74 except: 74 except:
75 print "folder not maked" 75 print "folder not maked"
76 pass 76 pass
77 77
78 78
79 db = shelve.open("{}/{}/ae_model.shelve".format(in_dir,name),writeback=True) 79 db = shelve.open("{}/{}/ae_model.shelve".format(in_dir,name),writeback=True)
80 db["LABEL"]=infer_model["LABEL"] 80 db["LABEL"]=infer_model["LABEL"]
81 # 81 #
82 82
83 83
84 keys = infer_model[features_key].keys() 84 keys = infer_model[features_key].keys()
85 85
86 db["VAE"] = {} 86 db["VAE"] = {}
87 db[features_key] = {} 87 db[features_key] = {}
88 for mod in keys : 88 for mod in keys :
89 #print mod 89 #print mod
90 db[features_key][mod] = train_mlp(infer_model[features_key][mod]["TRAIN"],infer_model["LABEL"][mod]["TRAIN"], 90 db[features_key][mod] = train_mlp(infer_model[features_key][mod]["TRAIN"],infer_model["LABEL"][mod]["TRAIN"],
91 infer_model[features_key][mod]["DEV"],infer_model["LABEL"][mod]["DEV"], 91 infer_model[features_key][mod]["DEV"],infer_model["LABEL"][mod]["DEV"],
92 infer_model[features_key][mod]["TEST"],infer_model["LABEL"][mod]["TEST"], 92 infer_model[features_key][mod]["TEST"],infer_model["LABEL"][mod]["TEST"],
93 mlp_h ,sgd=mlp_sgd, 93 mlp_h ,sgd=mlp_sgd,
94 epochs=mlp_epochs, 94 epochs=mlp_epochs,
95 batch_size=mlp_batch_size, 95 batch_size=mlp_batch_size,
96 input_activation=input_activation, 96 input_activation=input_activation,
97 output_activation=mlp_output_activation, 97 output_activation=mlp_output_activation,
98 dropouts=mlp_dropouts, 98 dropouts=mlp_dropouts,
99 fit_verbose=0) 99 fit_verbose=0)
100 100
101 res=train_vae(infer_model[features_key][mod]["TRAIN"],infer_model[features_key][mod]["DEV"],infer_model[features_key][mod]["TEST"], 101 res=train_vae(infer_model[features_key][mod]["TRAIN"],infer_model[features_key][mod]["DEV"],infer_model[features_key][mod]["TEST"],
102 hidden_size=hidden_size[0], 102 hidden_size=hidden_size[0],
103 latent_dim=latent_dim,sgd=sgd, 103 latent_dim=latent_dim,sgd=sgd,
104 input_activation=input_activation,output_activation=output_activation, 104 input_activation=input_activation,output_activation=output_activation,
105 nb_epochs=epochs,batch_size=batch) 105 nb_epochs=epochs,batch_size=batch)
106 mlp_res_list=[] 106 mlp_res_list=[]
107 for nb,layer in enumerate(res) : 107 for nb,layer in enumerate(res) :
108 if save_projection: 108 if save_projection:
109 pd = pandas.DataFrame(layer[0]) 109 pd = pandas.DataFrame(layer[0])
110 col_count = (pd.sum(axis=0) != 0) 110 col_count = (pd.sum(axis=0) != 0)
111 pd = pd.loc[:,cyyol_count] 111 pd = pd.loc[:,col_count]
112 pd.to_hdf("{}/{}/VAE_{}_{}_df.hdf".format(in_dir,name,nb,mod),"TRAIN") 112 pd.to_hdf("{}/{}/VAE_{}_{}_df.hdf".format(in_dir,name,nb,mod),"TRAIN")
113 pd = pandas.DataFrame(layer[1]) 113 pd = pandas.DataFrame(layer[1])
114 pd = pd.loc[:,col_count] 114 pd = pd.loc[:,col_count]
115 pd.to_hdf("{}/{}/VAE_{}_{}_df.hdf".format(in_dir,name,nb,mod),"DEV") 115 pd.to_hdf("{}/{}/VAE_{}_{}_df.hdf".format(in_dir,name,nb,mod),"DEV")
116 pd = pandas.DataFrame(layer[2]) 116 pd = pandas.DataFrame(layer[2])
117 pd = pd.loc[:,col_count] 117 pd = pd.loc[:,col_count]
118 pd.to_hdf("{}/{}/VAE_{}_{}_df.hdf".format(in_dir,name,nb,mod),"TEST") 118 pd.to_hdf("{}/{}/VAE_{}_{}_df.hdf".format(in_dir,name,nb,mod),"TEST")
119 del pd 119 del pd
120 120
121 mlp_res_list.append(train_mlp(layer[0],infer_model['LABEL'][mod]["TRAIN"], 121 mlp_res_list.append(train_mlp(layer[0],infer_model['LABEL'][mod]["TRAIN"],
122 layer[1],infer_model["LABEL"][mod]["DEV"], 122 layer[1],infer_model["LABEL"][mod]["DEV"],
123 layer[2],infer_model["LABEL"][mod]["TEST"], 123 layer[2],infer_model["LABEL"][mod]["TEST"],
124 mlp_h,loss=mlp_loss,dropouts=mlp_dropouts,sgd=mlp_sgd,epochs=mlp_epochs, 124 mlp_h,loss=mlp_loss,dropouts=mlp_dropouts,sgd=mlp_sgd,epochs=mlp_epochs,
125 output_activation=mlp_output_activation, 125 output_activation=mlp_output_activation,
126 input_activation=input_activation, 126 input_activation=input_activation,
127 batch_size=mlp_batch_size,fit_verbose=0)) 127 batch_size=mlp_batch_size,fit_verbose=0))
128 db["VAE"][mod]=mlp_res_list 128 db["VAE"][mod]=mlp_res_list
129 129
130 if "ASR" in keys and "TRS" in keys : 130 if "ASR" in keys and "TRS" in keys :
131 mod = "ASR" 131 mod = "ASR"
132 mod2= "TRS" 132 mod2= "TRS"
133 mlp_res_list=[] 133 mlp_res_list=[]
134 134
135 res = train_vae(infer_model[features_key][mod]["TRAIN"], 135 res = train_vae(infer_model[features_key][mod]["TRAIN"],
136 infer_model[features_key][mod]["DEV"], 136 infer_model[features_key][mod]["DEV"],
137 infer_model[features_key][mod]["TEST"], 137 infer_model[features_key][mod]["TEST"],
138 hidden_size=hidden_size[0], 138 hidden_size=hidden_size[0],
139 sgd=sgd,input_activation=input_activation,output_activation=output_activation, 139 sgd=sgd,input_activation=input_activation,output_activation=output_activation,
140 latent_dim=latent_dim, 140 latent_dim=latent_dim,
141 nb_epochs=epochs, 141 nb_epochs=epochs,
142 batch_size=batch, 142 batch_size=batch,
143 y_train=infer_model[features_key][mod2]["TRAIN"], 143 y_train=infer_model[features_key][mod2]["TRAIN"],
144 y_dev=infer_model[features_key][mod2]["DEV"], 144 y_dev=infer_model[features_key][mod2]["DEV"],
145 y_test=infer_model[features_key][mod2]["TEST"]) 145 y_test=infer_model[features_key][mod2]["TEST"])
146 146
147 for nb,layer in enumerate(res) : 147 for nb,layer in enumerate(res) :
148 if save_projection: 148 if save_projection:
149 pd = pandas.DataFrame(layer[0]) 149 pd = pandas.DataFrame(layer[0])
150 col_count = (pd.sum(axis=0) != 0) 150 col_count = (pd.sum(axis=0) != 0)
151 pd = pd.loc[:,col_count] 151 pd = pd.loc[:,col_count]
152 pd.to_hdf("{}/{}/VAE_{}_{}_df.hdf".format(in_dir,name,nb,"SPE"),"TRAIN") 152 pd.to_hdf("{}/{}/VAE_{}_{}_df.hdf".format(in_dir,name,nb,"SPE"),"TRAIN")
153 pd = pandas.DataFrame(layer[1]) 153 pd = pandas.DataFrame(layer[1])
154 pd = pd.loc[:,col_count] 154 pd = pd.loc[:,col_count]
155 pd.to_hdf("{}/{}/VAE_{}_{}_df.hdf".format(in_dir,name,nb,"SPE"),"DEV") 155 pd.to_hdf("{}/{}/VAE_{}_{}_df.hdf".format(in_dir,name,nb,"SPE"),"DEV")
156 pd = pandas.DataFrame(layer[2]) 156 pd = pandas.DataFrame(layer[2])
157 pd = pd.loc[:,col_count] 157 pd = pd.loc[:,col_count]
158 pd.to_hdf("{}/{}/VAE_{}_{}_df.hdf".format(in_dir,name,nb,"SPE"),"TEST") 158 pd.to_hdf("{}/{}/VAE_{}_{}_df.hdf".format(in_dir,name,nb,"SPE"),"TEST")
159 del pd 159 del pd
160 160
161 mlp_res_list.append(train_mlp(layer[0],infer_model["LABEL"][mod]["TRAIN"], 161 mlp_res_list.append(train_mlp(layer[0],infer_model["LABEL"][mod]["TRAIN"],
162 layer[1],infer_model["LABEL"][mod]["DEV"], 162 layer[1],infer_model["LABEL"][mod]["DEV"],
163 layer[2],infer_model["LABEL"][mod]["TEST"], 163 layer[2],infer_model["LABEL"][mod]["TEST"],
164 mlp_h,loss=mlp_loss,dropouts=mlp_dropouts,sgd=mlp_sgd,epochs=mlp_epochs, 164 mlp_h,loss=mlp_loss,dropouts=mlp_dropouts,sgd=mlp_sgd,epochs=mlp_epochs,
165 output_activation=mlp_output_activation, 165 output_activation=mlp_output_activation,
166 input_activation=input_activation, 166 input_activation=input_activation,
167 batch_size=mlp_batch_size,fit_verbose=0)) 167 batch_size=mlp_batch_size,fit_verbose=0))
168 168
169 db["VAE"]["SPE"] = mlp_res_list 169 db["VAE"]["SPE"] = mlp_res_list
170 170
171 db.sync() 171 db.sync()
172 db.close() 172 db.close()
173 173
File was created 1
2 # coding: utf-8
3
4 # In[29]:
5
6 # Import
7 import itertools
8 import shelve
9 import pickle
10 import numpy
11 import scipy
12 from scipy import sparse
13 import scipy.sparse
14 import scipy.io
15 from mlp import *
16 import mlp
17 import sys
18 import utils
19 import dill
20 from collections import Counter
21 from gensim.models import LdaModel
22 from sklearn.decomposition import PCA
23
24
25
26 # In[3]:
27
28 #30_50_50_150_0.0001
29
30 # In[4]:
31
32 #db=shelve.open("SPELIKE_MLP_DB.shelve",writeback=True)
33 origin_corps=shelve.open("{}".format(sys.argv[2]))
34 in_dir = sys.argv[1]
35 if len(sys.argv) > 3 :
36 features_key = sys.argv[3]
37 else :
38 features_key = "LDA"
39
40 out_db=shelve.open("{}/pca_scores.shelve".format(in_dir),writeback=True)
41 mlp_h = [ 250, 250 ]
42 mlp_loss = "categorical_crossentropy"
43 mlp_dropouts = [0.25]* len(mlp_h)
44 mlp_sgd = Adam(lr=0.0001)
45 mlp_epochs = 3000
46 mlp_batch_size = 5
47 mlp_input_activation = "relu"
48 mlp_output_activation="softmax"
49
50 ress = []
51 print
52
53 for key in origin_corps[features_key].keys() :
54 print "#########" + key + "########"
55 dev_best =[]
56 test_best = []
57 test_max = []
58 pca = PCA(n_components=200, copy=True, whiten=True)
59 x_train_big = pca.fit_transform(origin_corps[features_key][key]["TRAIN"])
60 y_train =origin_corps["LABEL"][key]["TRAIN"]
61
62
63
64 x_dev_big = pca.transform(origin_corps[features_key][key]["DEV"])
65 y_dev = origin_corps["LABEL"][key]["DEV"]
66
67 x_test_big = pca.transform(origin_corps[features_key][key]["TEST"])
68 y_test = origin_corps["LABEL"][key]["TEST"]
69 for i in range(1,200):
70 x_train = x_train_big[:,:i]
71 x_dev = x_dev_big[:,:i]
72 x_test = x_test_big[:,:i]
73 print "xshape",x_train.shape
74 print "xdev", x_dev.shape
75 print "xtest",x_test.shape
76 res=mlp.train_mlp(x_train,y_train,
77 x_dev,y_dev,
78 x_test ,y_test,
79 mlp_h,dropouts=mlp_dropouts,sgd=mlp_sgd,
80 epochs=mlp_epochs,
81 batch_size=mlp_batch_size,
82 save_pred=False,keep_histo=False,
83 loss="categorical_crossentropy",fit_verbose=0)
84 arg_best = numpy.argmax(res[1])
85 dev_best.append(res[1][arg_best])
86 test_best.append(res[2][arg_best])
87 test_max.append(numpy.max(res[2]))
88 print dev_best[-1],test_best[-1]
89 out_db[key]=(res,(dev_best,test_best,test_max))
90 ress.append((key,dev_best,test_best,test_max))
91 out_db.sync()
92
93 for el in ress :
94 print el
95 out_db.close()
96 origin_corps.close()
1 97
File was created 1 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/193/ MM_features/data_w99/mmf_193.shelve >> output_v8/recap.txt
2 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/194/ MM_features/data_w99/mmf_194.shelve >> output_v8/recap.txt
3 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/195/ MM_features/data_w99/mmf_195.shelve >> output_v8/recap.txt
4 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/196/ MM_features/data_w99/mmf_196.shelve >> output_v8/recap.txt
5 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/197/ MM_features/data_w99/mmf_197.shelve >> output_v8/recap.txt
6 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/198/ MM_features/data_w99/mmf_198.shelve >> output_v8/recap.txt
7 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/393/ MM_features/data_w99/mmf_393.shelve >> output_v8/recap.txt
8 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/394/ MM_features/data_w99/mmf_394.shelve >> output_v8/recap.txt
9 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/395/ MM_features/data_w99/mmf_395.shelve >> output_v8/recap.txt
10 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/396/ MM_features/data_w99/mmf_396.shelve >> output_v8/recap.txt
11 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/397/ MM_features/data_w99/mmf_397.shelve >> output_v8/recap.txt
12 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/43/ MM_features/data_w99/mmf_43.shelve >> output_v8/recap.txt
13 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/44/ MM_features/data_w99/mmf_44.shelve >> output_v8/recap.txt
14 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/45/ MM_features/data_w99/mmf_45.shelve >> output_v8/recap.txt
15 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/46/ MM_features/data_w99/mmf_46.shelve >> output_v8/recap.txt
16 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/47/ MM_features/data_w99/mmf_47.shelve >> output_v8/recap.txt
17 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/48/ MM_features/data_w99/mmf_48.shelve >> output_v8/recap.txt
18 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/493/ MM_features/data_w99/mmf_493.shelve >> output_v8/recap.txt
19 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/494/ MM_features/data_w99/mmf_494.shelve >> output_v8/recap.txt
20 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/495/ MM_features/data_w99/mmf_495.shelve >> output_v8/recap.txt
21 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/496/ MM_features/data_w99/mmf_496.shelve >> output_v8/recap.txt
22 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/497/ MM_features/data_w99/mmf_497.shelve >> output_v8/recap.txt
23 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/50/ MM_features/data_w99/mmf_50.shelve >> output_v8/recap.txt
24 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/93/ MM_features/data_w99/mmf_93.shelve >> output_v8/recap.txt
25 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/94/ MM_features/data_w99/mmf_94.shelve >> output_v8/recap.txt
26 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/95/ MM_features/data_w99/mmf_95.shelve >> output_v8/recap.txt
27 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/96/ MM_features/data_w99/mmf_96.shelve >> output_v8/recap.txt
28 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/97/ MM_features/data_w99/mmf_97.shelve >> output_v8/recap.txt
29 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/98/ MM_features/data_w99/mmf_98.shelve >> output_v8/recap.txt
1 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/193/ MM_features/data_w99/mmf_193.shelve >> output_v8/recap.txt 30
File was created 1 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T1/ MM_features/gen3/shelves/mmf_1.shelve output_v8/Conf1.json
2 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T2/ MM_features/gen3/shelves/mmf_2.shelve output_v8/Conf1.json
3 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T3/ MM_features/gen3/shelves/mmf_3.shelve output_v8/Conf1.json
4 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T4/ MM_features/gen3/shelves/mmf_4.shelve output_v8/Conf1.json
5 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T5/ MM_features/gen3/shelves/mmf_5.shelve output_v8/Conf1.json
6 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T6/ MM_features/gen3/shelves/mmf_6.shelve output_v8/Conf1.json
7 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T7/ MM_features/gen3/shelves/mmf_7.shelve output_v8/Conf1.json
8 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T8/ MM_features/gen3/shelves/mmf_8.shelve output_v8/Conf1.json
9 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T9/ MM_features/gen3/shelves/mmf_9.shelve output_v8/Conf1.json
10 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T10/ MM_features/gen3/shelves/mmf_10.shelve output_v8/Conf1.json
11 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T11/ MM_features/gen3/shelves/mmf_11.shelve output_v8/Conf1.json
12 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T12/ MM_features/gen3/shelves/mmf_12.shelve output_v8/Conf1.json
13 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T13/ MM_features/gen3/shelves/mmf_13.shelve output_v8/Conf1.json
14 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T14/ MM_features/gen3/shelves/mmf_14.shelve output_v8/Conf1.json
15 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T15/ MM_features/gen3/shelves/mmf_15.shelve output_v8/Conf1.json
16 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T16/ MM_features/gen3/shelves/mmf_16.shelve output_v8/Conf1.json
17 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T17/ MM_features/gen3/shelves/mmf_17.shelve output_v8/Conf1.json
18 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T18/ MM_features/gen3/shelves/mmf_18.shelve output_v8/Conf1.json
19 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T19/ MM_features/gen3/shelves/mmf_19.shelve output_v8/Conf1.json
20 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T20/ MM_features/gen3/shelves/mmf_20.shelve output_v8/Conf1.json
21 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T21/ MM_features/gen3/shelves/mmf_21.shelve output_v8/Conf1.json
22 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T22/ MM_features/gen3/shelves/mmf_22.shelve output_v8/Conf1.json
23 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T23/ MM_features/gen3/shelves/mmf_23.shelve output_v8/Conf1.json
24 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T24/ MM_features/gen3/shelves/mmf_24.shelve output_v8/Conf1.json
25 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T25/ MM_features/gen3/shelves/mmf_25.shelve output_v8/Conf1.json
26 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T26/ MM_features/gen3/shelves/mmf_26.shelve output_v8/Conf1.json
27 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T27/ MM_features/gen3/shelves/mmf_27.shelve output_v8/Conf1.json
28 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T28/ MM_features/gen3/shelves/mmf_28.shelve output_v8/Conf1.json
29 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T29/ MM_features/gen3/shelves/mmf_29.shelve output_v8/Conf1.json
30 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T30/ MM_features/gen3/shelves/mmf_30.shelve output_v8/Conf1.json
31 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T31/ MM_features/gen3/shelves/mmf_31.shelve output_v8/Conf1.json
32 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T32/ MM_features/gen3/shelves/mmf_32.shelve output_v8/Conf1.json
33 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T33/ MM_features/gen3/shelves/mmf_33.shelve output_v8/Conf1.json
34 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T34/ MM_features/gen3/shelves/mmf_34.shelve output_v8/Conf1.json
35 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T35/ MM_features/gen3/shelves/mmf_35.shelve output_v8/Conf1.json
36 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T36/ MM_features/gen3/shelves/mmf_36.shelve output_v8/Conf1.json
37 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T37/ MM_features/gen3/shelves/mmf_37.shelve output_v8/Conf1.json
38 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T38/ MM_features/gen3/shelves/mmf_38.shelve output_v8/Conf1.json
39 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T39/ MM_features/gen3/shelves/mmf_39.shelve output_v8/Conf1.json
40 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T40/ MM_features/gen3/shelves/mmf_40.shelve output_v8/Conf1.json
41 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T41/ MM_features/gen3/shelves/mmf_41.shelve output_v8/Conf1.json
42 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T42/ MM_features/gen3/shelves/mmf_42.shelve output_v8/Conf1.json
43 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T43/ MM_features/gen3/shelves/mmf_43.shelve output_v8/Conf1.json
44 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T44/ MM_features/gen3/shelves/mmf_44.shelve output_v8/Conf1.json
45 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T45/ MM_features/gen3/shelves/mmf_45.shelve output_v8/Conf1.json
46 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T46/ MM_features/gen3/shelves/mmf_46.shelve output_v8/Conf1.json
47 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T47/ MM_features/gen3/shelves/mmf_47.shelve output_v8/Conf1.json
48 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T48/ MM_features/gen3/shelves/mmf_48.shelve output_v8/Conf1.json
49 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T49/ MM_features/gen3/shelves/mmf_49.shelve output_v8/Conf1.json
50 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T50/ MM_features/gen3/shelves/mmf_50.shelve output_v8/Conf1.json
51 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T51/ MM_features/gen3/shelves/mmf_51.shelve output_v8/Conf1.json
52 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T52/ MM_features/gen3/shelves/mmf_52.shelve output_v8/Conf1.json
53 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T53/ MM_features/gen3/shelves/mmf_53.shelve output_v8/Conf1.json
54 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T54/ MM_features/gen3/shelves/mmf_54.shelve output_v8/Conf1.json
55 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T55/ MM_features/gen3/shelves/mmf_55.shelve output_v8/Conf1.json
56 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T56/ MM_features/gen3/shelves/mmf_56.shelve output_v8/Conf1.json
57 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T57/ MM_features/gen3/shelves/mmf_57.shelve output_v8/Conf1.json
58 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T58/ MM_features/gen3/shelves/mmf_58.shelve output_v8/Conf1.json
59 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T59/ MM_features/gen3/shelves/mmf_59.shelve output_v8/Conf1.json
60 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T60/ MM_features/gen3/shelves/mmf_60.shelve output_v8/Conf1.json
61 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T61/ MM_features/gen3/shelves/mmf_61.shelve output_v8/Conf1.json
62 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T62/ MM_features/gen3/shelves/mmf_62.shelve output_v8/Conf1.json
63 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T63/ MM_features/gen3/shelves/mmf_63.shelve output_v8/Conf1.json
64 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T64/ MM_features/gen3/shelves/mmf_64.shelve output_v8/Conf1.json
65 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T65/ MM_features/gen3/shelves/mmf_65.shelve output_v8/Conf1.json
66 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T66/ MM_features/gen3/shelves/mmf_66.shelve output_v8/Conf1.json
67 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T67/ MM_features/gen3/shelves/mmf_67.shelve output_v8/Conf1.json
68 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T68/ MM_features/gen3/shelves/mmf_68.shelve output_v8/Conf1.json
69 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T69/ MM_features/gen3/shelves/mmf_69.shelve output_v8/Conf1.json
70 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T70/ MM_features/gen3/shelves/mmf_70.shelve output_v8/Conf1.json
71 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T71/ MM_features/gen3/shelves/mmf_71.shelve output_v8/Conf1.json
72 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T72/ MM_features/gen3/shelves/mmf_72.shelve output_v8/Conf1.json
73 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T73/ MM_features/gen3/shelves/mmf_73.shelve output_v8/Conf1.json
74 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T74/ MM_features/gen3/shelves/mmf_74.shelve output_v8/Conf1.json
75 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T75/ MM_features/gen3/shelves/mmf_75.shelve output_v8/Conf1.json
76 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T76/ MM_features/gen3/shelves/mmf_76.shelve output_v8/Conf1.json
77 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T77/ MM_features/gen3/shelves/mmf_77.shelve output_v8/Conf1.json
78 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T78/ MM_features/gen3/shelves/mmf_78.shelve output_v8/Conf1.json
79 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T79/ MM_features/gen3/shelves/mmf_79.shelve output_v8/Conf1.json
80 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T80/ MM_features/gen3/shelves/mmf_80.shelve output_v8/Conf1.json
81 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T81/ MM_features/gen3/shelves/mmf_81.shelve output_v8/Conf1.json
82 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T82/ MM_features/gen3/shelves/mmf_82.shelve output_v8/Conf1.json
83 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T83/ MM_features/gen3/shelves/mmf_83.shelve output_v8/Conf1.json
84 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T84/ MM_features/gen3/shelves/mmf_84.shelve output_v8/Conf1.json
85 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T85/ MM_features/gen3/shelves/mmf_85.shelve output_v8/Conf1.json
86 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T86/ MM_features/gen3/shelves/mmf_86.shelve output_v8/Conf1.json
87 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T87/ MM_features/gen3/shelves/mmf_87.shelve output_v8/Conf1.json
88 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T88/ MM_features/gen3/shelves/mmf_88.shelve output_v8/Conf1.json
89 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T89/ MM_features/gen3/shelves/mmf_89.shelve output_v8/Conf1.json
90 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T90/ MM_features/gen3/shelves/mmf_90.shelve output_v8/Conf1.json
91 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T91/ MM_features/gen3/shelves/mmf_91.shelve output_v8/Conf1.json
92 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T92/ MM_features/gen3/shelves/mmf_92.shelve output_v8/Conf1.json
93 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T93/ MM_features/gen3/shelves/mmf_93.shelve output_v8/Conf1.json
94 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T94/ MM_features/gen3/shelves/mmf_94.shelve output_v8/Conf1.json
95 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T95/ MM_features/gen3/shelves/mmf_95.shelve output_v8/Conf1.json
96 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T96/ MM_features/gen3/shelves/mmf_96.shelve output_v8/Conf1.json
97 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T97/ MM_features/gen3/shelves/mmf_97.shelve output_v8/Conf1.json
98 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T98/ MM_features/gen3/shelves/mmf_98.shelve output_v8/Conf1.json
99 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T99/ MM_features/gen3/shelves/mmf_99.shelve output_v8/Conf1.json
100 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T100/ MM_features/gen3/shelves/mmf_100.shelve output_v8/Conf1.json
101 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T101/ MM_features/gen3/shelves/mmf_101.shelve output_v8/Conf1.json
102 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T102/ MM_features/gen3/shelves/mmf_102.shelve output_v8/Conf1.json
103 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T103/ MM_features/gen3/shelves/mmf_103.shelve output_v8/Conf1.json
104 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T104/ MM_features/gen3/shelves/mmf_104.shelve output_v8/Conf1.json
105 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T105/ MM_features/gen3/shelves/mmf_105.shelve output_v8/Conf1.json
106 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T106/ MM_features/gen3/shelves/mmf_106.shelve output_v8/Conf1.json
107 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T107/ MM_features/gen3/shelves/mmf_107.shelve output_v8/Conf1.json
108 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T108/ MM_features/gen3/shelves/mmf_108.shelve output_v8/Conf1.json
109 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T109/ MM_features/gen3/shelves/mmf_109.shelve output_v8/Conf1.json
110 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T110/ MM_features/gen3/shelves/mmf_110.shelve output_v8/Conf1.json
111 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T111/ MM_features/gen3/shelves/mmf_111.shelve output_v8/Conf1.json
112 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T112/ MM_features/gen3/shelves/mmf_112.shelve output_v8/Conf1.json
113 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T113/ MM_features/gen3/shelves/mmf_113.shelve output_v8/Conf1.json
114 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T114/ MM_features/gen3/shelves/mmf_114.shelve output_v8/Conf1.json
115 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T115/ MM_features/gen3/shelves/mmf_115.shelve output_v8/Conf1.json
116 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T116/ MM_features/gen3/shelves/mmf_116.shelve output_v8/Conf1.json
117 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T117/ MM_features/gen3/shelves/mmf_117.shelve output_v8/Conf1.json
118 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T118/ MM_features/gen3/shelves/mmf_118.shelve output_v8/Conf1.json
119 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T119/ MM_features/gen3/shelves/mmf_119.shelve output_v8/Conf1.json
120 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T120/ MM_features/gen3/shelves/mmf_120.shelve output_v8/Conf1.json
121 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T121/ MM_features/gen3/shelves/mmf_121.shelve output_v8/Conf1.json
122 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T122/ MM_features/gen3/shelves/mmf_122.shelve output_v8/Conf1.json
123 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T123/ MM_features/gen3/shelves/mmf_123.shelve output_v8/Conf1.json
124 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T124/ MM_features/gen3/shelves/mmf_124.shelve output_v8/Conf1.json
125 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T125/ MM_features/gen3/shelves/mmf_125.shelve output_v8/Conf1.json
126 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T126/ MM_features/gen3/shelves/mmf_126.shelve output_v8/Conf1.json
127 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T127/ MM_features/gen3/shelves/mmf_127.shelve output_v8/Conf1.json
128 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T128/ MM_features/gen3/shelves/mmf_128.shelve output_v8/Conf1.json
129 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T129/ MM_features/gen3/shelves/mmf_129.shelve output_v8/Conf1.json
130 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T130/ MM_features/gen3/shelves/mmf_130.shelve output_v8/Conf1.json
131 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T131/ MM_features/gen3/shelves/mmf_131.shelve output_v8/Conf1.json
132 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T132/ MM_features/gen3/shelves/mmf_132.shelve output_v8/Conf1.json
133 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T133/ MM_features/gen3/shelves/mmf_133.shelve output_v8/Conf1.json
134 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T134/ MM_features/gen3/shelves/mmf_134.shelve output_v8/Conf1.json
135 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T135/ MM_features/gen3/shelves/mmf_135.shelve output_v8/Conf1.json
136 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T136/ MM_features/gen3/shelves/mmf_136.shelve output_v8/Conf1.json
137 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T137/ MM_features/gen3/shelves/mmf_137.shelve output_v8/Conf1.json
138 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T138/ MM_features/gen3/shelves/mmf_138.shelve output_v8/Conf1.json
139 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T139/ MM_features/gen3/shelves/mmf_139.shelve output_v8/Conf1.json
140 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T140/ MM_features/gen3/shelves/mmf_140.shelve output_v8/Conf1.json
141 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T141/ MM_features/gen3/shelves/mmf_141.shelve output_v8/Conf1.json
142 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T142/ MM_features/gen3/shelves/mmf_142.shelve output_v8/Conf1.json
143 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T143/ MM_features/gen3/shelves/mmf_143.shelve output_v8/Conf1.json
144 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T144/ MM_features/gen3/shelves/mmf_144.shelve output_v8/Conf1.json
145 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T145/ MM_features/gen3/shelves/mmf_145.shelve output_v8/Conf1.json
146 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T146/ MM_features/gen3/shelves/mmf_146.shelve output_v8/Conf1.json
147 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T147/ MM_features/gen3/shelves/mmf_147.shelve output_v8/Conf1.json
148 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T148/ MM_features/gen3/shelves/mmf_148.shelve output_v8/Conf1.json
149 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T149/ MM_features/gen3/shelves/mmf_149.shelve output_v8/Conf1.json
150 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T150/ MM_features/gen3/shelves/mmf_150.shelve output_v8/Conf1.json
151 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T151/ MM_features/gen3/shelves/mmf_151.shelve output_v8/Conf1.json
152 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T152/ MM_features/gen3/shelves/mmf_152.shelve output_v8/Conf1.json
153 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T153/ MM_features/gen3/shelves/mmf_153.shelve output_v8/Conf1.json
154 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T154/ MM_features/gen3/shelves/mmf_154.shelve output_v8/Conf1.json
155 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T155/ MM_features/gen3/shelves/mmf_155.shelve output_v8/Conf1.json
156 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T156/ MM_features/gen3/shelves/mmf_156.shelve output_v8/Conf1.json
157 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T157/ MM_features/gen3/shelves/mmf_157.shelve output_v8/Conf1.json
158 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T158/ MM_features/gen3/shelves/mmf_158.shelve output_v8/Conf1.json
159 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T159/ MM_features/gen3/shelves/mmf_159.shelve output_v8/Conf1.json
160 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T160/ MM_features/gen3/shelves/mmf_160.shelve output_v8/Conf1.json
161 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T161/ MM_features/gen3/shelves/mmf_161.shelve output_v8/Conf1.json
162 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T162/ MM_features/gen3/shelves/mmf_162.shelve output_v8/Conf1.json
163 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T163/ MM_features/gen3/shelves/mmf_163.shelve output_v8/Conf1.json
164 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T164/ MM_features/gen3/shelves/mmf_164.shelve output_v8/Conf1.json
165 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T165/ MM_features/gen3/shelves/mmf_165.shelve output_v8/Conf1.json
166 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T166/ MM_features/gen3/shelves/mmf_166.shelve output_v8/Conf1.json
167 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T167/ MM_features/gen3/shelves/mmf_167.shelve output_v8/Conf1.json
168 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T168/ MM_features/gen3/shelves/mmf_168.shelve output_v8/Conf1.json
169 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T169/ MM_features/gen3/shelves/mmf_169.shelve output_v8/Conf1.json
170 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T170/ MM_features/gen3/shelves/mmf_170.shelve output_v8/Conf1.json
171 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T171/ MM_features/gen3/shelves/mmf_171.shelve output_v8/Conf1.json
172 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T172/ MM_features/gen3/shelves/mmf_172.shelve output_v8/Conf1.json
173 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T173/ MM_features/gen3/shelves/mmf_173.shelve output_v8/Conf1.json
174 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T174/ MM_features/gen3/shelves/mmf_174.shelve output_v8/Conf1.json
175 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T175/ MM_features/gen3/shelves/mmf_175.shelve output_v8/Conf1.json
176 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T176/ MM_features/gen3/shelves/mmf_176.shelve output_v8/Conf1.json
177 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T177/ MM_features/gen3/shelves/mmf_177.shelve output_v8/Conf1.json
178 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T178/ MM_features/gen3/shelves/mmf_178.shelve output_v8/Conf1.json
179 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T179/ MM_features/gen3/shelves/mmf_179.shelve output_v8/Conf1.json
180 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T180/ MM_features/gen3/shelves/mmf_180.shelve output_v8/Conf1.json
181 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T181/ MM_features/gen3/shelves/mmf_181.shelve output_v8/Conf1.json
182 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T182/ MM_features/gen3/shelves/mmf_182.shelve output_v8/Conf1.json
183 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T183/ MM_features/gen3/shelves/mmf_183.shelve output_v8/Conf1.json
184 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T184/ MM_features/gen3/shelves/mmf_184.shelve output_v8/Conf1.json
185 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T185/ MM_features/gen3/shelves/mmf_185.shelve output_v8/Conf1.json
186 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T186/ MM_features/gen3/shelves/mmf_186.shelve output_v8/Conf1.json
187 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T187/ MM_features/gen3/shelves/mmf_187.shelve output_v8/Conf1.json
188 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T188/ MM_features/gen3/shelves/mmf_188.shelve output_v8/Conf1.json
189 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T189/ MM_features/gen3/shelves/mmf_189.shelve output_v8/Conf1.json
190 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T190/ MM_features/gen3/shelves/mmf_190.shelve output_v8/Conf1.json
191 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T191/ MM_features/gen3/shelves/mmf_191.shelve output_v8/Conf1.json
192 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T192/ MM_features/gen3/shelves/mmf_192.shelve output_v8/Conf1.json
193 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T193/ MM_features/gen3/shelves/mmf_193.shelve output_v8/Conf1.json
194 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T194/ MM_features/gen3/shelves/mmf_194.shelve output_v8/Conf1.json
195 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T195/ MM_features/gen3/shelves/mmf_195.shelve output_v8/Conf1.json
196 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T196/ MM_features/gen3/shelves/mmf_196.shelve output_v8/Conf1.json
197 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T197/ MM_features/gen3/shelves/mmf_197.shelve output_v8/Conf1.json
198 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T198/ MM_features/gen3/shelves/mmf_198.shelve output_v8/Conf1.json
199 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T199/ MM_features/gen3/shelves/mmf_199.shelve output_v8/Conf1.json
200 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T200/ MM_features/gen3/shelves/mmf_200.shelve output_v8/Conf1.json
201 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T201/ MM_features/gen3/shelves/mmf_201.shelve output_v8/Conf1.json
202 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T202/ MM_features/gen3/shelves/mmf_202.shelve output_v8/Conf1.json
203 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T203/ MM_features/gen3/shelves/mmf_203.shelve output_v8/Conf1.json
204 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T204/ MM_features/gen3/shelves/mmf_204.shelve output_v8/Conf1.json
205 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T205/ MM_features/gen3/shelves/mmf_205.shelve output_v8/Conf1.json
206 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T206/ MM_features/gen3/shelves/mmf_206.shelve output_v8/Conf1.json
207 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T207/ MM_features/gen3/shelves/mmf_207.shelve output_v8/Conf1.json
208 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T208/ MM_features/gen3/shelves/mmf_208.shelve output_v8/Conf1.json
209 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T209/ MM_features/gen3/shelves/mmf_209.shelve output_v8/Conf1.json
210 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T210/ MM_features/gen3/shelves/mmf_210.shelve output_v8/Conf1.json
211 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T211/ MM_features/gen3/shelves/mmf_211.shelve output_v8/Conf1.json
212 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T212/ MM_features/gen3/shelves/mmf_212.shelve output_v8/Conf1.json
213 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T213/ MM_features/gen3/shelves/mmf_213.shelve output_v8/Conf1.json
214 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T214/ MM_features/gen3/shelves/mmf_214.shelve output_v8/Conf1.json
215 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T215/ MM_features/gen3/shelves/mmf_215.shelve output_v8/Conf1.json
216 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T216/ MM_features/gen3/shelves/mmf_216.shelve output_v8/Conf1.json
217 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T217/ MM_features/gen3/shelves/mmf_217.shelve output_v8/Conf1.json
218 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T218/ MM_features/gen3/shelves/mmf_218.shelve output_v8/Conf1.json
219 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T219/ MM_features/gen3/shelves/mmf_219.shelve output_v8/Conf1.json
220 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T220/ MM_features/gen3/shelves/mmf_220.shelve output_v8/Conf1.json
221 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T221/ MM_features/gen3/shelves/mmf_221.shelve output_v8/Conf1.json
222 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T222/ MM_features/gen3/shelves/mmf_222.shelve output_v8/Conf1.json
223 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T223/ MM_features/gen3/shelves/mmf_223.shelve output_v8/Conf1.json
224 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T224/ MM_features/gen3/shelves/mmf_224.shelve output_v8/Conf1.json
225 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T225/ MM_features/gen3/shelves/mmf_225.shelve output_v8/Conf1.json
226 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T226/ MM_features/gen3/shelves/mmf_226.shelve output_v8/Conf1.json
227 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T227/ MM_features/gen3/shelves/mmf_227.shelve output_v8/Conf1.json
228 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T228/ MM_features/gen3/shelves/mmf_228.shelve output_v8/Conf1.json
229 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T229/ MM_features/gen3/shelves/mmf_229.shelve output_v8/Conf1.json
230 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T230/ MM_features/gen3/shelves/mmf_230.shelve output_v8/Conf1.json
231 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T231/ MM_features/gen3/shelves/mmf_231.shelve output_v8/Conf1.json
232 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T232/ MM_features/gen3/shelves/mmf_232.shelve output_v8/Conf1.json
233 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T233/ MM_features/gen3/shelves/mmf_233.shelve output_v8/Conf1.json
234 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T234/ MM_features/gen3/shelves/mmf_234.shelve output_v8/Conf1.json
235 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T235/ MM_features/gen3/shelves/mmf_235.shelve output_v8/Conf1.json
236 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T236/ MM_features/gen3/shelves/mmf_236.shelve output_v8/Conf1.json
237 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T237/ MM_features/gen3/shelves/mmf_237.shelve output_v8/Conf1.json
238 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T238/ MM_features/gen3/shelves/mmf_238.shelve output_v8/Conf1.json
239 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T239/ MM_features/gen3/shelves/mmf_239.shelve output_v8/Conf1.json
240 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T240/ MM_features/gen3/shelves/mmf_240.shelve output_v8/Conf1.json
241 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T241/ MM_features/gen3/shelves/mmf_241.shelve output_v8/Conf1.json
242 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T242/ MM_features/gen3/shelves/mmf_242.shelve output_v8/Conf1.json
243 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T243/ MM_features/gen3/shelves/mmf_243.shelve output_v8/Conf1.json
244 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T244/ MM_features/gen3/shelves/mmf_244.shelve output_v8/Conf1.json
245 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T245/ MM_features/gen3/shelves/mmf_245.shelve output_v8/Conf1.json
246 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T246/ MM_features/gen3/shelves/mmf_246.shelve output_v8/Conf1.json
247 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T247/ MM_features/gen3/shelves/mmf_247.shelve output_v8/Conf1.json
248 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T248/ MM_features/gen3/shelves/mmf_248.shelve output_v8/Conf1.json
249 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T249/ MM_features/gen3/shelves/mmf_249.shelve output_v8/Conf1.json
250 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T250/ MM_features/gen3/shelves/mmf_250.shelve output_v8/Conf1.json
251 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T251/ MM_features/gen3/shelves/mmf_251.shelve output_v8/Conf1.json
252 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T252/ MM_features/gen3/shelves/mmf_252.shelve output_v8/Conf1.json
253 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T253/ MM_features/gen3/shelves/mmf_253.shelve output_v8/Conf1.json
254 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T254/ MM_features/gen3/shelves/mmf_254.shelve output_v8/Conf1.json
255 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T255/ MM_features/gen3/shelves/mmf_255.shelve output_v8/Conf1.json
256 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T256/ MM_features/gen3/shelves/mmf_256.shelve output_v8/Conf1.json
257 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T257/ MM_features/gen3/shelves/mmf_257.shelve output_v8/Conf1.json
258 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T258/ MM_features/gen3/shelves/mmf_258.shelve output_v8/Conf1.json
259 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T259/ MM_features/gen3/shelves/mmf_259.shelve output_v8/Conf1.json
260 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T260/ MM_features/gen3/shelves/mmf_260.shelve output_v8/Conf1.json
261 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T261/ MM_features/gen3/shelves/mmf_261.shelve output_v8/Conf1.json
262 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T262/ MM_features/gen3/shelves/mmf_262.shelve output_v8/Conf1.json
263 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T263/ MM_features/gen3/shelves/mmf_263.shelve output_v8/Conf1.json
264 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T264/ MM_features/gen3/shelves/mmf_264.shelve output_v8/Conf1.json
265 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T265/ MM_features/gen3/shelves/mmf_265.shelve output_v8/Conf1.json
266 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T266/ MM_features/gen3/shelves/mmf_266.shelve output_v8/Conf1.json
267 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T267/ MM_features/gen3/shelves/mmf_267.shelve output_v8/Conf1.json
268 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T268/ MM_features/gen3/shelves/mmf_268.shelve output_v8/Conf1.json
269 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T269/ MM_features/gen3/shelves/mmf_269.shelve output_v8/Conf1.json
270 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T270/ MM_features/gen3/shelves/mmf_270.shelve output_v8/Conf1.json
271 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T271/ MM_features/gen3/shelves/mmf_271.shelve output_v8/Conf1.json
272 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T272/ MM_features/gen3/shelves/mmf_272.shelve output_v8/Conf1.json
273 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T273/ MM_features/gen3/shelves/mmf_273.shelve output_v8/Conf1.json
274 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T274/ MM_features/gen3/shelves/mmf_274.shelve output_v8/Conf1.json
275 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T275/ MM_features/gen3/shelves/mmf_275.shelve output_v8/Conf1.json
276 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T276/ MM_features/gen3/shelves/mmf_276.shelve output_v8/Conf1.json
277 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T277/ MM_features/gen3/shelves/mmf_277.shelve output_v8/Conf1.json
278 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T278/ MM_features/gen3/shelves/mmf_278.shelve output_v8/Conf1.json
279 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T279/ MM_features/gen3/shelves/mmf_279.shelve output_v8/Conf1.json
280 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T280/ MM_features/gen3/shelves/mmf_280.shelve output_v8/Conf1.json
281 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T281/ MM_features/gen3/shelves/mmf_281.shelve output_v8/Conf1.json
282 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T282/ MM_features/gen3/shelves/mmf_282.shelve output_v8/Conf1.json
283 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T283/ MM_features/gen3/shelves/mmf_283.shelve output_v8/Conf1.json
284 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T284/ MM_features/gen3/shelves/mmf_284.shelve output_v8/Conf1.json
285 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T285/ MM_features/gen3/shelves/mmf_285.shelve output_v8/Conf1.json
286 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T286/ MM_features/gen3/shelves/mmf_286.shelve output_v8/Conf1.json
287 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T287/ MM_features/gen3/shelves/mmf_287.shelve output_v8/Conf1.json
288 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T288/ MM_features/gen3/shelves/mmf_288.shelve output_v8/Conf1.json
289 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T289/ MM_features/gen3/shelves/mmf_289.shelve output_v8/Conf1.json
290 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T290/ MM_features/gen3/shelves/mmf_290.shelve output_v8/Conf1.json
291 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T291/ MM_features/gen3/shelves/mmf_291.shelve output_v8/Conf1.json
292 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T292/ MM_features/gen3/shelves/mmf_292.shelve output_v8/Conf1.json
293 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T293/ MM_features/gen3/shelves/mmf_293.shelve output_v8/Conf1.json
294 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T294/ MM_features/gen3/shelves/mmf_294.shelve output_v8/Conf1.json
295 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T295/ MM_features/gen3/shelves/mmf_295.shelve output_v8/Conf1.json
296 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T296/ MM_features/gen3/shelves/mmf_296.shelve output_v8/Conf1.json
297 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T297/ MM_features/gen3/shelves/mmf_297.shelve output_v8/Conf1.json
298 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T298/ MM_features/gen3/shelves/mmf_298.shelve output_v8/Conf1.json
299 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T299/ MM_features/gen3/shelves/mmf_299.shelve output_v8/Conf1.json
300 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T300/ MM_features/gen3/shelves/mmf_300.shelve output_v8/Conf1.json
301 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T301/ MM_features/gen3/shelves/mmf_301.shelve output_v8/Conf1.json
302 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T302/ MM_features/gen3/shelves/mmf_302.shelve output_v8/Conf1.json
303 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T303/ MM_features/gen3/shelves/mmf_303.shelve output_v8/Conf1.json
304 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T304/ MM_features/gen3/shelves/mmf_304.shelve output_v8/Conf1.json
305 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T305/ MM_features/gen3/shelves/mmf_305.shelve output_v8/Conf1.json
306 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T306/ MM_features/gen3/shelves/mmf_306.shelve output_v8/Conf1.json
307 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T307/ MM_features/gen3/shelves/mmf_307.shelve output_v8/Conf1.json
308 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T308/ MM_features/gen3/shelves/mmf_308.shelve output_v8/Conf1.json
309 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T309/ MM_features/gen3/shelves/mmf_309.shelve output_v8/Conf1.json
310 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T310/ MM_features/gen3/shelves/mmf_310.shelve output_v8/Conf1.json
311 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T311/ MM_features/gen3/shelves/mmf_311.shelve output_v8/Conf1.json
312 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T312/ MM_features/gen3/shelves/mmf_312.shelve output_v8/Conf1.json
313 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T313/ MM_features/gen3/shelves/mmf_313.shelve output_v8/Conf1.json
314 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T314/ MM_features/gen3/shelves/mmf_314.shelve output_v8/Conf1.json
315 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T315/ MM_features/gen3/shelves/mmf_315.shelve output_v8/Conf1.json
316 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T316/ MM_features/gen3/shelves/mmf_316.shelve output_v8/Conf1.json
317 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T317/ MM_features/gen3/shelves/mmf_317.shelve output_v8/Conf1.json
318 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T318/ MM_features/gen3/shelves/mmf_318.shelve output_v8/Conf1.json
319 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T319/ MM_features/gen3/shelves/mmf_319.shelve output_v8/Conf1.json
320 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T320/ MM_features/gen3/shelves/mmf_320.shelve output_v8/Conf1.json
321 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T321/ MM_features/gen3/shelves/mmf_321.shelve output_v8/Conf1.json
322 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T322/ MM_features/gen3/shelves/mmf_322.shelve output_v8/Conf1.json
323 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T323/ MM_features/gen3/shelves/mmf_323.shelve output_v8/Conf1.json
324 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T324/ MM_features/gen3/shelves/mmf_324.shelve output_v8/Conf1.json
325 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T325/ MM_features/gen3/shelves/mmf_325.shelve output_v8/Conf1.json
326 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T326/ MM_features/gen3/shelves/mmf_326.shelve output_v8/Conf1.json
327 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T327/ MM_features/gen3/shelves/mmf_327.shelve output_v8/Conf1.json
328 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T328/ MM_features/gen3/shelves/mmf_328.shelve output_v8/Conf1.json
329 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T329/ MM_features/gen3/shelves/mmf_329.shelve output_v8/Conf1.json
330 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T330/ MM_features/gen3/shelves/mmf_330.shelve output_v8/Conf1.json
331 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T331/ MM_features/gen3/shelves/mmf_331.shelve output_v8/Conf1.json
332 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T332/ MM_features/gen3/shelves/mmf_332.shelve output_v8/Conf1.json
333 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T333/ MM_features/gen3/shelves/mmf_333.shelve output_v8/Conf1.json
334 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T334/ MM_features/gen3/shelves/mmf_334.shelve output_v8/Conf1.json
335 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T335/ MM_features/gen3/shelves/mmf_335.shelve output_v8/Conf1.json
336 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T336/ MM_features/gen3/shelves/mmf_336.shelve output_v8/Conf1.json
337 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T337/ MM_features/gen3/shelves/mmf_337.shelve output_v8/Conf1.json
338 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T338/ MM_features/gen3/shelves/mmf_338.shelve output_v8/Conf1.json
339 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T339/ MM_features/gen3/shelves/mmf_339.shelve output_v8/Conf1.json
340 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T340/ MM_features/gen3/shelves/mmf_340.shelve output_v8/Conf1.json
341 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T341/ MM_features/gen3/shelves/mmf_341.shelve output_v8/Conf1.json
342 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T342/ MM_features/gen3/shelves/mmf_342.shelve output_v8/Conf1.json
343 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T343/ MM_features/gen3/shelves/mmf_343.shelve output_v8/Conf1.json
344 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T344/ MM_features/gen3/shelves/mmf_344.shelve output_v8/Conf1.json
345 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T345/ MM_features/gen3/shelves/mmf_345.shelve output_v8/Conf1.json
346 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T346/ MM_features/gen3/shelves/mmf_346.shelve output_v8/Conf1.json
347 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T347/ MM_features/gen3/shelves/mmf_347.shelve output_v8/Conf1.json
348 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T348/ MM_features/gen3/shelves/mmf_348.shelve output_v8/Conf1.json
349 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T349/ MM_features/gen3/shelves/mmf_349.shelve output_v8/Conf1.json
350 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T350/ MM_features/gen3/shelves/mmf_350.shelve output_v8/Conf1.json
351 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T351/ MM_features/gen3/shelves/mmf_351.shelve output_v8/Conf1.json
352 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T352/ MM_features/gen3/shelves/mmf_352.shelve output_v8/Conf1.json
353 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T353/ MM_features/gen3/shelves/mmf_353.shelve output_v8/Conf1.json
354 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T354/ MM_features/gen3/shelves/mmf_354.shelve output_v8/Conf1.json
355 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T355/ MM_features/gen3/shelves/mmf_355.shelve output_v8/Conf1.json
356 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T356/ MM_features/gen3/shelves/mmf_356.shelve output_v8/Conf1.json
357 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T357/ MM_features/gen3/shelves/mmf_357.shelve output_v8/Conf1.json
358 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T358/ MM_features/gen3/shelves/mmf_358.shelve output_v8/Conf1.json
359 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T359/ MM_features/gen3/shelves/mmf_359.shelve output_v8/Conf1.json
360 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T360/ MM_features/gen3/shelves/mmf_360.shelve output_v8/Conf1.json
361 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T361/ MM_features/gen3/shelves/mmf_361.shelve output_v8/Conf1.json
362 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T362/ MM_features/gen3/shelves/mmf_362.shelve output_v8/Conf1.json
363 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T363/ MM_features/gen3/shelves/mmf_363.shelve output_v8/Conf1.json
364 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T364/ MM_features/gen3/shelves/mmf_364.shelve output_v8/Conf1.json
365 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T365/ MM_features/gen3/shelves/mmf_365.shelve output_v8/Conf1.json
366 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T366/ MM_features/gen3/shelves/mmf_366.shelve output_v8/Conf1.json
367 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T367/ MM_features/gen3/shelves/mmf_367.shelve output_v8/Conf1.json
368 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T368/ MM_features/gen3/shelves/mmf_368.shelve output_v8/Conf1.json
369 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T369/ MM_features/gen3/shelves/mmf_369.shelve output_v8/Conf1.json
370 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T370/ MM_features/gen3/shelves/mmf_370.shelve output_v8/Conf1.json
371 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T371/ MM_features/gen3/shelves/mmf_371.shelve output_v8/Conf1.json
372 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T372/ MM_features/gen3/shelves/mmf_372.shelve output_v8/Conf1.json
373 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T373/ MM_features/gen3/shelves/mmf_373.shelve output_v8/Conf1.json
374 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T374/ MM_features/gen3/shelves/mmf_374.shelve output_v8/Conf1.json
375 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T375/ MM_features/gen3/shelves/mmf_375.shelve output_v8/Conf1.json
376 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T376/ MM_features/gen3/shelves/mmf_376.shelve output_v8/Conf1.json
377 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T377/ MM_features/gen3/shelves/mmf_377.shelve output_v8/Conf1.json
378 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T378/ MM_features/gen3/shelves/mmf_378.shelve output_v8/Conf1.json
379 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T379/ MM_features/gen3/shelves/mmf_379.shelve output_v8/Conf1.json
380 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T380/ MM_features/gen3/shelves/mmf_380.shelve output_v8/Conf1.json
381 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T381/ MM_features/gen3/shelves/mmf_381.shelve output_v8/Conf1.json
382 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T382/ MM_features/gen3/shelves/mmf_382.shelve output_v8/Conf1.json
383 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T383/ MM_features/gen3/shelves/mmf_383.shelve output_v8/Conf1.json
384 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T384/ MM_features/gen3/shelves/mmf_384.shelve output_v8/Conf1.json
385 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T385/ MM_features/gen3/shelves/mmf_385.shelve output_v8/Conf1.json
386 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T386/ MM_features/gen3/shelves/mmf_386.shelve output_v8/Conf1.json
387 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T387/ MM_features/gen3/shelves/mmf_387.shelve output_v8/Conf1.json
388 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T388/ MM_features/gen3/shelves/mmf_388.shelve output_v8/Conf1.json
389 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T389/ MM_features/gen3/shelves/mmf_389.shelve output_v8/Conf1.json
390 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T390/ MM_features/gen3/shelves/mmf_390.shelve output_v8/Conf1.json
391 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T391/ MM_features/gen3/shelves/mmf_391.shelve output_v8/Conf1.json
392 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T392/ MM_features/gen3/shelves/mmf_392.shelve output_v8/Conf1.json
393 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T393/ MM_features/gen3/shelves/mmf_393.shelve output_v8/Conf1.json
394 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T394/ MM_features/gen3/shelves/mmf_394.shelve output_v8/Conf1.json
395 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T395/ MM_features/gen3/shelves/mmf_395.shelve output_v8/Conf1.json
396 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T396/ MM_features/gen3/shelves/mmf_396.shelve output_v8/Conf1.json
397 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T397/ MM_features/gen3/shelves/mmf_397.shelve output_v8/Conf1.json
398 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T398/ MM_features/gen3/shelves/mmf_398.shelve output_v8/Conf1.json
399 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T399/ MM_features/gen3/shelves/mmf_399.shelve output_v8/Conf1.json
400 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T400/ MM_features/gen3/shelves/mmf_400.shelve output_v8/Conf1.json
401 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T401/ MM_features/gen3/shelves/mmf_401.shelve output_v8/Conf1.json
402 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T402/ MM_features/gen3/shelves/mmf_402.shelve output_v8/Conf1.json
403 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T403/ MM_features/gen3/shelves/mmf_403.shelve output_v8/Conf1.json
404 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T404/ MM_features/gen3/shelves/mmf_404.shelve output_v8/Conf1.json
405 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T405/ MM_features/gen3/shelves/mmf_405.shelve output_v8/Conf1.json
406 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T406/ MM_features/gen3/shelves/mmf_406.shelve output_v8/Conf1.json
407 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T407/ MM_features/gen3/shelves/mmf_407.shelve output_v8/Conf1.json
408 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T408/ MM_features/gen3/shelves/mmf_408.shelve output_v8/Conf1.json
409 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T409/ MM_features/gen3/shelves/mmf_409.shelve output_v8/Conf1.json
410 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T410/ MM_features/gen3/shelves/mmf_410.shelve output_v8/Conf1.json
411 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T411/ MM_features/gen3/shelves/mmf_411.shelve output_v8/Conf1.json
412 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T412/ MM_features/gen3/shelves/mmf_412.shelve output_v8/Conf1.json
413 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T413/ MM_features/gen3/shelves/mmf_413.shelve output_v8/Conf1.json
414 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T414/ MM_features/gen3/shelves/mmf_414.shelve output_v8/Conf1.json
415 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T415/ MM_features/gen3/shelves/mmf_415.shelve output_v8/Conf1.json
416 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T416/ MM_features/gen3/shelves/mmf_416.shelve output_v8/Conf1.json
417 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T417/ MM_features/gen3/shelves/mmf_417.shelve output_v8/Conf1.json
418 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T418/ MM_features/gen3/shelves/mmf_418.shelve output_v8/Conf1.json
419 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T419/ MM_features/gen3/shelves/mmf_419.shelve output_v8/Conf1.json
420 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T420/ MM_features/gen3/shelves/mmf_420.shelve output_v8/Conf1.json
421 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T421/ MM_features/gen3/shelves/mmf_421.shelve output_v8/Conf1.json
422 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T422/ MM_features/gen3/shelves/mmf_422.shelve output_v8/Conf1.json
423 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T423/ MM_features/gen3/shelves/mmf_423.shelve output_v8/Conf1.json
424 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T424/ MM_features/gen3/shelves/mmf_424.shelve output_v8/Conf1.json
425 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T425/ MM_features/gen3/shelves/mmf_425.shelve output_v8/Conf1.json
426 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T426/ MM_features/gen3/shelves/mmf_426.shelve output_v8/Conf1.json
427 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T427/ MM_features/gen3/shelves/mmf_427.shelve output_v8/Conf1.json
428 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T428/ MM_features/gen3/shelves/mmf_428.shelve output_v8/Conf1.json
429 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T429/ MM_features/gen3/shelves/mmf_429.shelve output_v8/Conf1.json
430 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T430/ MM_features/gen3/shelves/mmf_430.shelve output_v8/Conf1.json
431 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T431/ MM_features/gen3/shelves/mmf_431.shelve output_v8/Conf1.json
432 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T432/ MM_features/gen3/shelves/mmf_432.shelve output_v8/Conf1.json
433 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T433/ MM_features/gen3/shelves/mmf_433.shelve output_v8/Conf1.json
434 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T434/ MM_features/gen3/shelves/mmf_434.shelve output_v8/Conf1.json
435 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T435/ MM_features/gen3/shelves/mmf_435.shelve output_v8/Conf1.json
436 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T436/ MM_features/gen3/shelves/mmf_436.shelve output_v8/Conf1.json
437 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T437/ MM_features/gen3/shelves/mmf_437.shelve output_v8/Conf1.json
438 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T438/ MM_features/gen3/shelves/mmf_438.shelve output_v8/Conf1.json
439 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T439/ MM_features/gen3/shelves/mmf_439.shelve output_v8/Conf1.json
440 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T440/ MM_features/gen3/shelves/mmf_440.shelve output_v8/Conf1.json
441 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T441/ MM_features/gen3/shelves/mmf_441.shelve output_v8/Conf1.json
442 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T442/ MM_features/gen3/shelves/mmf_442.shelve output_v8/Conf1.json
443 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T443/ MM_features/gen3/shelves/mmf_443.shelve output_v8/Conf1.json
444 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T444/ MM_features/gen3/shelves/mmf_444.shelve output_v8/Conf1.json
445 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T445/ MM_features/gen3/shelves/mmf_445.shelve output_v8/Conf1.json
446 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T446/ MM_features/gen3/shelves/mmf_446.shelve output_v8/Conf1.json
447 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T447/ MM_features/gen3/shelves/mmf_447.shelve output_v8/Conf1.json
448 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T448/ MM_features/gen3/shelves/mmf_448.shelve output_v8/Conf1.json
449 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T449/ MM_features/gen3/shelves/mmf_449.shelve output_v8/Conf1.json
450 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T450/ MM_features/gen3/shelves/mmf_450.shelve output_v8/Conf1.json
451 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T451/ MM_features/gen3/shelves/mmf_451.shelve output_v8/Conf1.json
452 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T452/ MM_features/gen3/shelves/mmf_452.shelve output_v8/Conf1.json
453 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T453/ MM_features/gen3/shelves/mmf_453.shelve output_v8/Conf1.json
454 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T454/ MM_features/gen3/shelves/mmf_454.shelve output_v8/Conf1.json
455 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T455/ MM_features/gen3/shelves/mmf_455.shelve output_v8/Conf1.json
456 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T456/ MM_features/gen3/shelves/mmf_456.shelve output_v8/Conf1.json
457 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T457/ MM_features/gen3/shelves/mmf_457.shelve output_v8/Conf1.json
458 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T458/ MM_features/gen3/shelves/mmf_458.shelve output_v8/Conf1.json
459 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T459/ MM_features/gen3/shelves/mmf_459.shelve output_v8/Conf1.json
460 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T460/ MM_features/gen3/shelves/mmf_460.shelve output_v8/Conf1.json
461 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T461/ MM_features/gen3/shelves/mmf_461.shelve output_v8/Conf1.json
462 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T462/ MM_features/gen3/shelves/mmf_462.shelve output_v8/Conf1.json
463 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T463/ MM_features/gen3/shelves/mmf_463.shelve output_v8/Conf1.json
464 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T464/ MM_features/gen3/shelves/mmf_464.shelve output_v8/Conf1.json
465 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T465/ MM_features/gen3/shelves/mmf_465.shelve output_v8/Conf1.json
466 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T466/ MM_features/gen3/shelves/mmf_466.shelve output_v8/Conf1.json
467 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T467/ MM_features/gen3/shelves/mmf_467.shelve output_v8/Conf1.json
468 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T468/ MM_features/gen3/shelves/mmf_468.shelve output_v8/Conf1.json
469 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T469/ MM_features/gen3/shelves/mmf_469.shelve output_v8/Conf1.json
470 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T470/ MM_features/gen3/shelves/mmf_470.shelve output_v8/Conf1.json
471 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T471/ MM_features/gen3/shelves/mmf_471.shelve output_v8/Conf1.json
472 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T472/ MM_features/gen3/shelves/mmf_472.shelve output_v8/Conf1.json
473 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T473/ MM_features/gen3/shelves/mmf_473.shelve output_v8/Conf1.json
474 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T474/ MM_features/gen3/shelves/mmf_474.shelve output_v8/Conf1.json
475 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T475/ MM_features/gen3/shelves/mmf_475.shelve output_v8/Conf1.json
476 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T476/ MM_features/gen3/shelves/mmf_476.shelve output_v8/Conf1.json
477 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T477/ MM_features/gen3/shelves/mmf_477.shelve output_v8/Conf1.json
478 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T478/ MM_features/gen3/shelves/mmf_478.shelve output_v8/Conf1.json
479 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T479/ MM_features/gen3/shelves/mmf_479.shelve output_v8/Conf1.json
480 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T480/ MM_features/gen3/shelves/mmf_480.shelve output_v8/Conf1.json
481 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T481/ MM_features/gen3/shelves/mmf_481.shelve output_v8/Conf1.json
482 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T482/ MM_features/gen3/shelves/mmf_482.shelve output_v8/Conf1.json
483 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T483/ MM_features/gen3/shelves/mmf_483.shelve output_v8/Conf1.json
484 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T484/ MM_features/gen3/shelves/mmf_484.shelve output_v8/Conf1.json
485 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T485/ MM_features/gen3/shelves/mmf_485.shelve output_v8/Conf1.json
486 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T486/ MM_features/gen3/shelves/mmf_486.shelve output_v8/Conf1.json
487 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T487/ MM_features/gen3/shelves/mmf_487.shelve output_v8/Conf1.json
488 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T488/ MM_features/gen3/shelves/mmf_488.shelve output_v8/Conf1.json
489 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T489/ MM_features/gen3/shelves/mmf_489.shelve output_v8/Conf1.json
490 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T490/ MM_features/gen3/shelves/mmf_490.shelve output_v8/Conf1.json
491 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T491/ MM_features/gen3/shelves/mmf_491.shelve output_v8/Conf1.json
492 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T492/ MM_features/gen3/shelves/mmf_492.shelve output_v8/Conf1.json
493 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T493/ MM_features/gen3/shelves/mmf_493.shelve output_v8/Conf1.json
494 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T494/ MM_features/gen3/shelves/mmf_494.shelve output_v8/Conf1.json
495 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T495/ MM_features/gen3/shelves/mmf_495.shelve output_v8/Conf1.json
496 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T496/ MM_features/gen3/shelves/mmf_496.shelve output_v8/Conf1.json
497 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T497/ MM_features/gen3/shelves/mmf_497.shelve output_v8/Conf1.json
498 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T498/ MM_features/gen3/shelves/mmf_498.shelve output_v8/Conf1.json
499 THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T499/ MM_features/gen3/shelves/mmf_499.shelve output_v8/Conf1.json
500 THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32 python 04b-mmf_mini_ae.py output_v8/T500/ MM_features/gen3/shelves/mmf_500.shelve output_v8/Conf1.json
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1 python 00-prepross.py File was deleted
2 python 02-lda_split.py DECODA_list_wid.shelve output_v1/ 100 12 test2 1 400
3 python 03-mono_perplex.py DECODA_list_wid.shelve output_v1/test2 output_v1/t2db.json
4 1 python 00-prepross.py
File was created 1 output_dir=$1
2 features=$2
3 json_conf=$3
4 nb=$4
5
6 THEANO_FLAGS=mode=FAST_RUN,device=$nb,floatX=float32 python 04b-mmf_mini_ae.py $output_dir $features $json_conf LTS
7 #THEANO_FLAGS=mode=FAST_RUN,device=$nb,floatX=float32 python 04c-mmf_sae.py $output_dir $features $json_conf LTS
8 #THEANO_FLAGS=mode=FAST_RUN,device=$nb,floatX=float32 python 04d-mmf_dsae.py $output_dir $features $json_conf LTS
9 THEANO_FLAGS=mode=FAST_RUN,device=$nb,floatX=float32 python 04e-mm_vae.py $output_dir $features $json_conf LTS
10 python 05-lts_scoring.py $output_dir $json_conf
1 output_dir=$1 11
LDA/run_lts_alter.sh
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2 features=$2
3 json_conf=$3
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5
6 THEANO_FLAGS=mode=FAST_RUN,device=$nb,floatX=float32 python 04b-mmf_mini_ae.py $output_dir $features $json_conf LTS
7 #THEANO_FLAGS=mode=FAST_RUN,device=$nb,floatX=float32 python 04c-mmf_sae.py $output_dir $features $json_conf LTS
8 #THEANO_FLAGS=mode=FAST_RUN,device=$nb,floatX=float32 python 04d-mmf_dsae.py $output_dir $features $json_conf LTS
9 THEANO_FLAGS=mode=FAST_RUN,device=$nb,floatX=float32 python 04e-mm_vae.py $output_dir $features $json_conf LTS
10 python 05-lts_scoring.py $output_dir $json_conf
1 output_dir=$1 11
1 output_dir=$1 1 output_dir=$1
2 features=$2 2 features=$2
3 json_conf=$3 3 json_conf=$3
4 nb=$(echo "gpu$4") 4 nb=$(echo "gpu$4")
5 5
6 THEANO_FLAGS=mode=FAST_RUN,device=$nb,floatX=float32 python 04b-mmf_mini_ae.py $output_dir $features $json_conf >> ${output_dir}/miniae.log 6 THEANO_FLAGS=mode=FAST_RUN,device=$nb,floatX=float32 python 04b-mmf_mini_ae.py $output_dir $features $json_conf >> ${output_dir}/miniae.log
7 THEANO_FLAGS=mode=FAST_RUN,device=$nb,floatX=float32 python 04c-mmf_sae.py $output_dir $features $json_conf >> ${output_dir}/sae.log 7 THEANO_FLAGS=mode=FAST_RUN,device=$nb,floatX=float32 python 04c-mmf_sae.py $output_dir $features $json_conf >> ${output_dir}/sae.log
8 THEANO_FLAGS=mode=FAST_RUN,device=$nb,floatX=float32 python 04d-mmf_dsae.py $output_dir $features $json_conf >> ${output_dir}/dsae.log 8 THEANO_FLAGS=mode=FAST_RUN,device=$nb,floatX=float32 python 04d-mmf_dsae.py $output_dir $features $json_conf >> ${output_dir}/dsae.log
9 THEANO_FLAGS=mode=FAST_RUN,device=$nb,floatX=float32 python 04e-mm_vae.py $output_dir $features $json_conf >> ${output_dir}/vae.log
9 THEANO_FLAGS=mode=FAST_RUN,device=$nb,floatX=float32 python 04e-mm_vae.py $output_dir $features $json_conf >> ${output_dir}/vae.log 10 wait
10 wait
1 '''This script demonstrates how to build a variational autoencoder with Keras. 1 '''This script demonstrates how to build a variational autoencoder with Keras.
2 Reference: "Auto-Encoding Variational Bayes" https://arxiv.org/abs/1312.6114 2 Reference: "Auto-Encoding Variational Bayes" https://arxiv.org/abs/1312.6114
3 ''' 3 '''
4 4
5 import itertools 5 import itertools
6 import sys 6 import sys
7 import json 7 import json
8 8
9 import numpy as np 9 import numpy as np
10 import matplotlib.pyplot as plt 10 import matplotlib.pyplot as plt
11 from scipy import sparse 11 from scipy import sparse
12 import scipy.io 12 import scipy.io
13 13
14 from keras.layers import Input, Dense, Lambda 14 from keras.layers import Input, Dense, Lambda
15 from keras.models import Model 15 from keras.models import Model
16 from keras import backend as K 16 from keras import backend as K
17 from keras import objectives 17 from keras import objectives
18 from keras.datasets import mnist 18 from keras.datasets import mnist
19 from keras.callbacks import EarlyStopping,Callback 19 from keras.callbacks import EarlyStopping,Callback
20 20
21 import pandas 21 import pandas
22 import shelve 22 import shelve
23 import pickle 23 import pickle
24 24
25 25
26 class ZeroStopping(Callback): 26 class ZeroStopping(Callback):
27 '''Stop training when a monitored quantity has stopped improving. 27 '''Stop training when a monitored quantity has stopped improving.
28 # Arguments 28 # Arguments
29 monitor: quantity to be monitored. 29 monitor: quantity to be monitored.
30 patience: number of epochs with no improvement 30 patience: number of epochs with no improvement
31 after which training will be stopped. 31 after which training will be stopped.
32 verbose: verbosity mode. 32 verbose: verbosity mode.
33 mode: one of {auto, min, max}. In 'min' mode, 33 mode: one of {auto, min, max}. In 'min' mode,
34 training will stop when the quantity 34 training will stop when the quantity
35 monitored has stopped decreasing; in 'max' 35 monitored has stopped decreasing; in 'max'
36 mode it will stop when the quantity 36 mode it will stop when the quantity
37 monitored has stopped increasing. 37 monitored has stopped increasing.
38 ''' 38 '''
39 def __init__(self, monitor='val_loss', verbose=0, mode='auto', thresh = 0): 39 def __init__(self, monitor='val_loss', verbose=0, mode='auto', thresh = 0):
40 super(ZeroStopping, self).__init__() 40 super(ZeroStopping, self).__init__()
41 41
42 self.monitor = monitor 42 self.monitor = monitor
43 self.verbose = verbose 43 self.verbose = verbose
44 self.thresh = thresh # is a rythme 44 self.thresh = thresh # is a rythme
45 45
46 if mode not in ['auto', 'min', 'max']: 46 if mode not in ['auto', 'min', 'max']:
47 warnings.warn('EarlyStopping mode %s is unknown, ' 47 warnings.warn('EarlyStopping mode %s is unknown, '
48 'fallback to auto mode.' % (self.mode), 48 'fallback to auto mode.' % (self.mode),
49 RuntimeWarning) 49 RuntimeWarning)
50 mode = 'auto' 50 mode = 'auto'
51 51
52 if mode == 'min': 52 if mode == 'min':
53 self.monitor_op = np.less 53 self.monitor_op = np.less
54 elif mode == 'max': 54 elif mode == 'max':
55 self.monitor_op = np.greater 55 self.monitor_op = np.greater
56 else: 56 else:
57 if 'acc' in self.monitor: 57 if 'acc' in self.monitor:
58 self.monitor_op = np.greater 58 self.monitor_op = np.greater
59 else: 59 else:
60 self.monitor_op = np.less 60 self.monitor_op = np.less
61 61
62 def on_epoch_end(self, epoch, logs={}): 62 def on_epoch_end(self, epoch, logs={}):
63 current = logs.get(self.monitor) 63 current = logs.get(self.monitor)
64 if current is None: 64 if current is None:
65 warnings.warn('Zero stopping requires %s available!' % 65 warnings.warn('Zero stopping requires %s available!' %
66 (self.monitor), RuntimeWarning) 66 (self.monitor), RuntimeWarning)
67 67
68 if self.monitor_op(current, self.thresh): 68 if self.monitor_op(current, self.thresh):
69 self.best = current 69 self.best = current
70 self.model.stop_training = True 70 self.model.stop_training = True
71 71
72 #batch_size = 16 72 #batch_size = 16
73 #original_dim = 784 73 #original_dim = 784
74 #latent_dim = 2 74 #latent_dim = 2
75 #intermediate_dim = 128 75 #intermediate_dim = 128
76 #epsilon_std = 0.01 76 #epsilon_std = 0.01
77 #nb_epoch = 40 77 #nb_epoch = 40
78 78
79 79
80 80
81 81
82 def train_vae(x_train,x_dev,x_test,y_train=None,y_dev=None,y_test=None,hidden_size=80,latent_dim=12,batch_size=8,nb_epochs=10,sgd="rmsprop",input_activation = "relu",output_activation = "sigmoid",epsilon_std=0.01): 82 def train_vae(x_train,x_dev,x_test,y_train=None,y_dev=None,y_test=None,hidden_size=80,latent_dim=12,batch_size=8,nb_epochs=10,sgd="rmsprop",input_activation = "relu",output_activation = "sigmoid",epsilon_std=0.01):
83 83
84 84
85 85
86 def sampling(args): 86 def sampling(args):
87 z_mean, z_log_std = args 87 z_mean, z_log_std = args
88 epsilon = K.random_normal(shape=(batch_size, latent_dim), 88 epsilon = K.random_normal(shape=(batch_size, latent_dim),
89 mean=0., std=epsilon_std) 89 mean=0., std=epsilon_std)
90 return z_mean + K.exp(z_log_std) * epsilon 90 return z_mean + K.exp(z_log_std) * epsilon
91 91
92 def vae_loss(x, x_decoded_mean): 92 def vae_loss(x, x_decoded_mean):
93 xent_loss = objectives.binary_crossentropy(x, x_decoded_mean) 93 xent_loss = objectives.binary_crossentropy(x, x_decoded_mean)
94 kl_loss = - 0.5 * K.mean(1 + z_log_std - K.square(z_mean) - K.exp(z_log_std), axis=-1) 94 kl_loss = - 0.5 * K.mean(1 + z_log_std - K.square(z_mean) - K.exp(z_log_std), axis=-1)
95 return xent_loss + kl_loss 95 return xent_loss + kl_loss
96 96
97 original_dim = x_train.shape[1] 97 original_dim = x_train.shape[1]
98 98
99 99
100 x = Input(batch_shape=(batch_size, original_dim)) 100 x = Input(batch_shape=(batch_size, original_dim))
101 h = Dense(hidden_size, activation=input_activation)(x) 101 h = Dense(hidden_size, activation=input_activation)(x)
102 z_mean = Dense(latent_dim)(h) 102 z_mean = Dense(latent_dim)(h)
103 z_log_std = Dense(latent_dim)(h) 103 z_log_std = Dense(latent_dim)(h)
104 104
105 105
106 # note that "output_shape" isn't necessary with the TensorFlow backend 106 # note that "output_shape" isn't necessary with the TensorFlow backend
107 # so you could write `Lambda(sampling)([z_mean, z_log_std])` 107 # so you could write `Lambda(sampling)([z_mean, z_log_std])`
108 z = Lambda(sampling, output_shape=(latent_dim,))([z_mean, z_log_std]) 108 z = Lambda(sampling, output_shape=(latent_dim,))([z_mean, z_log_std])
109 109
110 # we instantiate these layers separately so as to reuse them later 110 # we instantiate these layers separately so as to reuse them later
111 decoder_h = Dense(hidden_size, activation=input_activation) 111 decoder_h = Dense(hidden_size, activation=input_activation)
112 decoder_mean = Dense(original_dim, activation=output_activation) 112 decoder_mean = Dense(original_dim, activation=output_activation)
113 h_decoded = decoder_h(z) 113 h_decoded = decoder_h(z)
114 x_decoded_mean = decoder_mean(h_decoded) 114 x_decoded_mean = decoder_mean(h_decoded)
115 115
116 116
117 vae = Model(x, x_decoded_mean) 117 vae = Model(x, x_decoded_mean)
118 vae.compile(optimizer=sgd, loss=vae_loss) 118 vae.compile(optimizer=sgd, loss=vae_loss)
119 119
120 # train the VAE on MNIST digits 120 # train the VAE on MNIST digits
121 if y_train is None or y_dev is None or y_test is None : 121 if y_train is None or y_dev is None or y_test is None :
122 y_train = x_train 122 y_train = x_train
123 y_dev = x_dev 123 y_dev = x_dev
124 y_test = x_test 124 y_test = x_test
125 125
126 vae.fit(x_train, y_train, 126 vae.fit(x_train, y_train,
127 shuffle=True, 127 shuffle=True,
128 nb_epoch=nb_epochs, 128 nb_epoch=nb_epochs,
129 verbose = 1, 129 verbose = 1,
130 batch_size=batch_size, 130 batch_size=batch_size,
131 validation_data=(x_dev, y_dev), 131 validation_data=(x_dev, y_dev)
132 callbacks = [ZeroStopping(monitor='val_loss', thresh=0, verbose=0, mode='min')] 132 #callbacks = [ZeroStopping(monitor='val_loss', thresh=0, verbose=0, mode='min')]
133 ) 133 )
134 134
135 # build a model to project inputs on the latent space 135 # build a model to project inputs on the latent space
136 encoder = Model(x, z_mean) 136 encoder = Model(x, z_mean)
137 pred_train = encoder.predict(x_train, batch_size=batch_size) 137 pred_train = encoder.predict(x_train, batch_size=batch_size)
138 pred_dev = encoder.predict(x_dev, batch_size=batch_size) 138 pred_dev = encoder.predict(x_dev, batch_size=batch_size)
139 pred_test = encoder.predict(x_test,batch_size=batch_size) 139 pred_test = encoder.predict(x_test,batch_size=batch_size)
140 return [ [ pred_train, pred_dev, pred_test ] ] 140 return [ [ pred_train, pred_dev, pred_test ] ]
141 # display a 2D plot of the digit classes in the latent space 141 # display a 2D plot of the digit classes in the latent space
142 #x_test_encoded = encoder.predict(x_test, batch_size=batch_size) 142 #x_test_encoded = encoder.predict(x_test, batch_size=batch_size)
143 # build a digit generator that can sample from the learned distribution 143 # build a digit generator that can sample from the learned distribution
144 #decoder_input = Input(shape=(latent_dim,)) 144 #decoder_input = Input(shape=(latent_dim,))
145 #_h_decoded = decoder_h(decoder_input) 145 #_h_decoded = decoder_h(decoder_input)
146 #_x_decoded_mean = decoder_mean(_h_decoded) 146 #_x_decoded_mean = decoder_mean(_h_decoded)
147 #generator = Model(decoder_input, _x_decoded_mean) 147 #generator = Model(decoder_input, _x_decoded_mean)
148 #x_decoded = generator.predict(z_sample) 148 #x_decoded = generator.predict(z_sample)
149 149
150 150