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LDA/04c-mmf_sae.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 *
import mlp
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, 80, 50 , 20 ]
input_activation="relu"
output_activation="relu"
loss="mse"
epochs=3000
batch=1
patience=20
do_do=[ 0 ] * len(hidden_size)
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)
try :
sgd_repr=sgd.get_config()["name"]
except AttributeError :
sgd_repr=sgd
params={ "h1" : "_".join([str(x) for x in hidden_size]),
"inside_activation" : input_activation,
"out_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}
name = "_".join([ str(x) for x in params.values()])
try:
os.mkdir("{}/SAE_{}".format(in_dir,name))
except:
pass
db = shelve.open("{}/SAE_{}/ae_model.shelve".format(in_dir,name),writeback=True)
#
json.dump(params,
open("{}/SAE_{}/ae_model.json".format(in_dir,name),"w"),
indent=4)
keys = ["ASR","TRS"]
mlp_h = [ 150 , 300 ]
mlp_loss ="categorical_crossentropy"
mlp_dropouts = [0,0,0,0]
mlp_sgd = Adam(0.001)
mlp_epochs = 2000
mlp_batch_size = 8
db["SAE"] = {}
db["SAEFT"] = {}
for mod in keys :
print "MODE ", mod
res_tuple=train_sae(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="tanh",
output_activation="tanh",loss=loss,epochs=epochs,
batch_size=batch,verbose=0)
#print len(res), [len(x) for x in res[0]], [ len(x) for x in res[1]]
for name , levels in zip(["SAE","SAEFT"],res_tuple):
print "NAME", name
mlp_res_by_level = []
for res in levels:
mlp_res_list=[]
for nb,layer in enumerate(res) :
print "layer NB",nb
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,batch_size=mlp_batch_size,
fit_verbose=0))
mlp_res_by_level.append(mlp_res_list)
db[name][mod]=mlp_res_by_level
mod = "ASR"
mod2= "TRS"
print "mode SPE "
res_tuple = train_sae(infer_model["LDA"][mod]["TRAIN"],
infer_model["LDA"][mod]["DEV"],
infer_model["LDA"][mod]["TEST"],
hidden_size,dropouts=[0],patience=params["patience"],
sgd=sgd,input_activation=input_activation,output_activation=input_activation,
loss=loss,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 name , levels in zip(["SAE","SAEFT"],res_tuple):
mlp_res_by_level = []
for res in levels :
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,batch_size=mlp_batch_size,
fit_verbose=0))
mlp_res_by_level.append(mlp_res_list)
db[name]["SPE"] = mlp_res_by_level
db.close()
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