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LDA/04e-mm_vae.py
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7db73861f
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 *
from vae 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= [60]
input_activation="tanh"
output_activation="sigmoid"
epochs=300
batch=1
patience=60
sgd = Adam(lr=0.0001)#SGD(lr=0.00001,nesterov=False) #'rmsprop'# Adam(lr=0.00001)#SGD(lr=0.001, momentum=0.9, nesterov=True)
latent_dim = 30
mlp_h = [ 256 ]
mlp_loss = "categorical_crossentropy"
mlp_dropouts = []
mlp_sgd = Adam(lr=0.001)
mlp_epochs = 1000
mlp_batch_size = 16
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,
"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("{}/VAE_{}".format(in_dir,name))
except:
pass
db = shelve.open("{}/VAE_{}/ae_model.shelve".format(in_dir,name),writeback=True)
db["params"] = params
db["LABEL"]=infer_model["LABEL"]
#
json.dump(params,
open("{}/VAE_{}/ae_model.json".format(in_dir,name),"w"),
indent=4)
keys = ["ASR","TRS"]
db["VAE"] = {}
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_vae(infer_model["LDA"][mod]["TRAIN"],infer_model["LDA"][mod]["DEV"],infer_model["LDA"][mod]["TEST"],
hidden_size=hidden_size[0],
latent_dim=latent_dim,sgd=sgd,
input_activation=input_activation,output_activation=output_activation,
nb_epochs=epochs,batch_size=batch)
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["VAE"][mod]=mlp_res_list
mod = "ASR"
mod2= "TRS"
mlp_res_list=[]
res = train_vae(infer_model["LDA"][mod]["TRAIN"],
infer_model["LDA"][mod]["DEV"],
infer_model["LDA"][mod]["TEST"],
hidden_size=hidden_size[0],
sgd=sgd,input_activation=input_activation,output_activation=output_activation,
latent_dim=latent_dim,
nb_epochs=epochs,
batch_size=batch,
y_train=infer_model["LDA"][mod2]["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["VAE"]["SPE"] = mlp_res_list
db.sync()
db.close()
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