04e-mm_vae.py
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# coding: utf-8
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]:
if len(sys.argv) > 4 :
features_key = sys.argv[4]
else :
features_key = "LDA"
save_projection = True
json_conf =json.load(open(sys.argv[3]))
vae_conf = json_conf["vae"]
hidden_size= vae_conf["hidden_size"]
input_activation=vae_conf["input_activation"]
output_activation=vae_conf["output_activation"]
epochs=vae_conf["epochs"]
batch=vae_conf["batch"]
patience=vae_conf["patience"]
latent_dim = vae_conf["latent"]
try:
k = vae_conf["sgd"]
if vae_conf["sgd"]["name"] == "adam":
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)
elif vae_conf["sgd"]["name"] == "sgd":
sgd = SGD(lr=vae_conf["sgd"]["lr"])
except:
sgd = vae_conf["sgd"]
mlp_conf = json_conf["mlp"]
mlp_h = mlp_conf["hidden_size"]
mlp_loss = mlp_conf["loss"]
mlp_dropouts = mlp_conf["do"]
mlp_epochs = mlp_conf["epochs"]
mlp_batch_size = mlp_conf["batch"]
mlp_input_activation=mlp_conf["input_activation"]
mlp_output_activation=mlp_conf["output_activation"]
try:
k = mlp_conf["sgd"]
if mlp_conf["sgd"]["name"] == "adam":
mlp_sgd = Adam(lr=mlp_conf["sgd"]["lr"])#SGD(lr=0.00001,nesterov=False) #'rmsprop'# Adam(lr=0.00001)#SGD(lr=0.001, momentum=0.9, nesterov=True)
elif mlp_conf["sgd"]["name"] == "sgd":
mlp_sgd = SGD(lr=mlp_conf["sgd"]["lr"])
except:
mlp_sgd = mlp_conf["sgd"]
name = json_conf["name"]
try :
print "make folder "
os.mkdir("{}/{}".format(in_dir,name))
except:
print "folder not maked"
pass
db = shelve.open("{}/{}/ae_model.shelve".format(in_dir,name),writeback=True)
db["LABEL"]=infer_model["LABEL"]
#
keys = infer_model[features_key].keys()
db["VAE"] = {}
db[features_key] = {}
for mod in keys :
#print mod
db[features_key][mod] = train_mlp(infer_model[features_key][mod]["TRAIN"],infer_model["LABEL"][mod]["TRAIN"],
infer_model[features_key][mod]["DEV"],infer_model["LABEL"][mod]["DEV"],
infer_model[features_key][mod]["TEST"],infer_model["LABEL"][mod]["TEST"],
mlp_h ,sgd=mlp_sgd,
epochs=mlp_epochs,
batch_size=mlp_batch_size,
input_activation=input_activation,
output_activation=mlp_output_activation,
dropouts=mlp_dropouts,
fit_verbose=0)
res=train_vae(infer_model[features_key][mod]["TRAIN"],infer_model[features_key][mod]["DEV"],infer_model[features_key][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 nb,layer in enumerate(res) :
if save_projection:
pd = pandas.DataFrame(layer[0])
col_count = (pd.sum(axis=0) != 0)
pd = pd.loc[:,cyyol_count]
pd.to_hdf("{}/{}/VAE_{}_{}_df.hdf".format(in_dir,name,nb,mod),"TRAIN")
pd = pandas.DataFrame(layer[1])
pd = pd.loc[:,col_count]
pd.to_hdf("{}/{}/VAE_{}_{}_df.hdf".format(in_dir,name,nb,mod),"DEV")
pd = pandas.DataFrame(layer[2])
pd = pd.loc[:,col_count]
pd.to_hdf("{}/{}/VAE_{}_{}_df.hdf".format(in_dir,name,nb,mod),"TEST")
del pd
mlp_res_list.append(train_mlp(layer[0],infer_model['LABEL'][mod]["TRAIN"],
layer[1],infer_model["LABEL"][mod]["DEV"],
layer[2],infer_model["LABEL"][mod]["TEST"],
mlp_h,loss=mlp_loss,dropouts=mlp_dropouts,sgd=mlp_sgd,epochs=mlp_epochs,
output_activation=mlp_output_activation,
input_activation=input_activation,
batch_size=mlp_batch_size,fit_verbose=0))
db["VAE"][mod]=mlp_res_list
if "ASR" in keys and "TRS" in keys :
mod = "ASR"
mod2= "TRS"
mlp_res_list=[]
res = train_vae(infer_model[features_key][mod]["TRAIN"],
infer_model[features_key][mod]["DEV"],
infer_model[features_key][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[features_key][mod2]["TRAIN"],
y_dev=infer_model[features_key][mod2]["DEV"],
y_test=infer_model[features_key][mod2]["TEST"])
for nb,layer in enumerate(res) :
if save_projection:
pd = pandas.DataFrame(layer[0])
col_count = (pd.sum(axis=0) != 0)
pd = pd.loc[:,col_count]
pd.to_hdf("{}/{}/VAE_{}_{}_df.hdf".format(in_dir,name,nb,"SPE"),"TRAIN")
pd = pandas.DataFrame(layer[1])
pd = pd.loc[:,col_count]
pd.to_hdf("{}/{}/VAE_{}_{}_df.hdf".format(in_dir,name,nb,"SPE"),"DEV")
pd = pandas.DataFrame(layer[2])
pd = pd.loc[:,col_count]
pd.to_hdf("{}/{}/VAE_{}_{}_df.hdf".format(in_dir,name,nb,"SPE"),"TEST")
del pd
mlp_res_list.append(train_mlp(layer[0],infer_model["LABEL"][mod]["TRAIN"],
layer[1],infer_model["LABEL"][mod]["DEV"],
layer[2],infer_model["LABEL"][mod]["TEST"],
mlp_h,loss=mlp_loss,dropouts=mlp_dropouts,sgd=mlp_sgd,epochs=mlp_epochs,
output_activation=mlp_output_activation,
input_activation=input_activation,
batch_size=mlp_batch_size,fit_verbose=0))
db["VAE"]["SPE"] = mlp_res_list
db.sync()
db.close()