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