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DECODA_binary_BOW_AE_MODELS.py
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# coding: utf-8
# In[2]:
# Import
import pandas
# Alignement
import nltk
import codecs
import gensim
from scipy import sparse
import itertools
from sklearn.feature_extraction.text import CountVectorizer
import scipy.sparse
import scipy.io
from sklearn import preprocessing
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation,AutoEncoder
from keras.optimizers import SGD,Adam
from keras.layers import containers
from mlp import *
import mlp
import sklearn.metrics
import shelve
import pickle
from utils import *
import sys
import json
# In[4]:
db=shelve.open("{}.shelve".format(sys.argv[2]),writeback=True)
sparse_model=shelve.open("{}.shelve".format(sys.argv[1]))
#['ASR', 'TRS', 'LABEL']
# In[6]:
ASR=sparse_model["ASR"]
TRS=sparse_model["TRS"]
LABEL=sparse_model["LABEL"]
db["ASR_SPARSE"]=ASR
db["TRS_SPARSE"]=TRS
db["LABEL"]=LABEL
print "todo label"
def select(elm):
return int(elm.split("_")[-1])
#z.apply(select)
label_bin={}
lb = preprocessing.LabelBinarizer(neg_label=0)
lb.fit(LABEL["TRAIN"].apply(select))
for i in ASR.keys():
label_bin=lb.transform(LABEL[i].apply(select))
hidden_size=795
hidden_size2=530
input_activation="tanh"
out_activation="tanh"
loss="mse"
epochs=15
batch=4
patience=10
w1_size=2048
w2_size=3096
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()
except AttributeError :
sgd_repr=sgd
json.dump({ "h1" : hidden_size,
"h2": hidden_size2,
"w1" : w1_size,
"w2" : w2_size,
"inside_activation" : input_activation,
"out_activation" : out_activation,
"loss" : loss,
"epochs" : epochs ,
"batch_size" : batch,
"patience" : patience,
"sgd" : sgd_repr},
open("{}.json".format(sys.argv[2]),"w"),
indent=4)
print "gogo autoencoder ASR"
autoencode=Sequential()
autoencode.add(Dense(hidden_size,input_dim=ASR["TRAIN"].shape[1],init='glorot_uniform',activation=input_activation))
autoencode.add(Dense(hidden_size2,input_dim=hidden_size,init='glorot_uniform',activation=input_activation))
autoencode.add(Dense(hidden_size,input_dim=hidden_size2,init="glorot_uniform",activation=out_activation))
autoencode.add(Dense(ASR["DEV"].todense().shape[1],input_dim=hidden_size,init="glorot_uniform",activation=out_activation))
#autoencode.compile(optimizer=sgd,loss=loss)
autoencode.compile(optimizer=sgd,loss=loss)
# In[ ]:
autoencode.fit(ASR["TRAIN"].todense(),ASR["TRAIN"].todense(),
nb_epoch=epochs,batch_size=batch,
callbacks=
[keras.callbacks.EarlyStopping(monitor='val_loss',patience=patience, verbose=0)], validation_data=(ASR["DEV"].todense(),ASR["DEV"].todense()),verbose=1)
# In[ ]:
auto_decoder=Sequential()
auto_decoder.add(Dense(hidden_size,input_dim=ASR["DEV"].todense().shape[1],init='uniform',activation=input_activation,weights=autoencode.get_weights()[:2]))
auto_decoder.add(Dense(hidden_size2,input_dim=hidden_size,init='glorot_uniform',activation=input_activation,weights=autoencode.get_weights()[2:4]))
auto_decoder.add(Dense(hidden_size,input_dim=hidden_size2,init='glorot_uniform',activation=input_activation,weights=autoencode.get_weights()[4:6]))
auto_decoder.compile(optimizer=sgd,loss=loss)
# In[77]:
#autoencode.predict(ASR["DEV"].todense())
# In[ ]:
print "auto encoder et auto decoder asr okay"
ASR_AE_H2={}
for i in ASR.keys():
ASR_AE_H2[i]=auto_decoder.predict(ASR[i].todense())
#TRS[i]=dico.transform(TRS[i][2])
db["ASR_AE_H2"]=ASR_AE_H2
auto_decoder=Sequential()
auto_decoder.add(Dense(hidden_size,input_dim=ASR["DEV"].todense().shape[1],init='uniform',activation=input_activation,weights=autoencode.get_weights()[:2]))
auto_decoder.add(Dense(hidden_size2,input_dim=hidden_size,init='glorot_uniform',activation=input_activation,weights=autoencode.get_weights()[2:4]))
auto_decoder.compile(optimizer=sgd,loss=loss)
ASR_AE_H1={}
for i in ASR.keys():
ASR_AE_H1[i]=auto_decoder.predict(ASR[i].todense())
#TRS[i]=dico.transform(TRS[i][2])
db["ASR_AE_H1"]=ASR_AE_H1
print "auto encoder trs learning"
# In[68]:/
sgd_trs = SGD(lr=0.1,momentum=0.9)
autoencode_trs=Sequential()
autoencode_trs.add(Dense(hidden_size,input_dim=TRS["DEV"].todense().shape[1],init='glorot_uniform',activation=input_activation))
autoencode_trs.add(Dense(hidden_size2,input_dim=hidden_size,init='glorot_uniform',activation=input_activation))
autoencode_trs.add(Dense(hidden_size,input_dim=hidden_size2,init="glorot_uniform",activation=out_activation))
autoencode_trs.add(Dense(TRS["DEV"].todense().shape[1],input_dim=hidden_size,init="glorot_uniform",activation=out_activation))
#autoencode_trs.compile(optimizer=sgd_trs,loss=loss)
autoencode_trs.compile(optimizer=sgd,loss=loss)
# In[69]:
autoencode_trs.fit(TRS["TRAIN"].todense(),TRS["TRAIN"].todense(),nb_epoch=epochs,batch_size=batch,
callbacks=[keras.callbacks.EarlyStopping(monitor='val_loss',
patience=patience, verbose=0)],
validation_data=(TRS["DEV"].todense(),TRS["DEV"].todense()),verbose=1)
# In[87]:
auto_decoder_trs=Sequential()
auto_decoder_trs.add(Dense(hidden_size,input_dim=ASR["DEV"].todense().shape[1],activation=input_activation,weights=autoencode_trs.get_weights()[:2]))
auto_decoder_trs.add(Dense(hidden_size2,input_dim=hidden_size,activation=input_activation,weights=autoencode_trs.get_weights()[2:4]))
auto_decoder_trs.add(Dense(hidden_size,input_dim=hidden_size2,activation=input_activation,weights=autoencode_trs.get_weights()[4:6]))
auto_decoder_trs.compile(optimizer=sgd,loss=loss)
# In[88]:
print "auto encoder trs okay"
TRS_AE_H2={}
for i in TRS.keys():
TRS_AE_H2[i]=auto_decoder_trs.predict(TRS[i].todense())
#TRS[i]=dico.transform(TRS[i][2])
db["TRS_AE_H2"]=TRS_AE_H2
auto_decoder_trs=Sequential()
auto_decoder_trs.add(Dense(hidden_size,input_dim=ASR["DEV"].todense().shape[1],activation=input_activation,weights=autoencode_trs.get_weights()[:2]))
auto_decoder_trs.add(Dense(hidden_size2,input_dim=hidden_size,activation=input_activation,weights=autoencode_trs.get_weights()[2:4]))
auto_decoder_trs.compile(optimizer=sgd,loss=loss)
# In[88]:
print "auto encoder trs okay"
TRS_AE_H1={}
for i in TRS.keys():
TRS_AE_H1[i]=auto_decoder_trs.predict(TRS[i].todense())
#TRS[i]=dico.transform(TRS[i][2])
db["TRS_AE_H1"]=TRS_AE_H1
db.sync()
# In[261]:
#pred_dev= model_TRS_AE.predict(TRS_AE["DEV"],batch_size=1)
TRS_AE={}
ASR_AE={}
for i in TRS.keys():
TRS_AE[i]=autoencode_trs.predict(TRS[i].todense())
ASR_AE[i]=autoencode.predict(ASR[i].todense())
db["TRS_AE_OUT"]=TRS_AE
db["ASR_AE_OUT"]=ASR_AE
db.sync()
# # Transfert de couche
# ICI
# In[138]:
print "learn transform ae H2({})".format(hidden_size)
model_TRANS = Sequential()
model_TRANS.add(Dense( w1_size,input_dim=hidden_size, init='glorot_uniform', activation='relu'))
#model_TRANS.add(Dropout(0.5))
model_TRANS.add(Dense( w2_size,input_dim=w1_size, init='glorot_uniform', activation='relu'))
#model_TRANS.add(Dropout(0.5))
model_TRANS.add(Dense( hidden_size,input_dim=w2_size, init='glorot_uniform', activation='relu'))
sgd_TRANS = SGD(lr=0.01, decay=1e-4, momentum=0.9, nesterov=True)
#model_TRANS.compile(loss='mse', optimizer=sgd_TRANS)
model_TRANS.compile(loss='mse', optimizer=sgd)
# In[146]:
model_TRANS.fit(ASR_AE_H2["TRAIN"],TRS_AE_H2["TRAIN"],nb_epoch=epochs,batch_size=batch,
callbacks=[keras.callbacks.EarlyStopping(monitor='val_loss',
patience=patience, verbose=0)],
validation_data=(ASR_AE_H2["DEV"],TRS_AE_H2["DEV"]),verbose=1)
# In[140]:
print "make trans projection H2"
asr_transformer={}
for i in ASR_AE.keys():
asr_transformer[i]=model_TRANS.predict(ASR_AE_H2[i])
db["ASR_H2_TRANFORMED_TRSH2"]=asr_transformer
# In[ ]:
db.sync()
auto_decoder_trans=Sequential()
auto_decoder_trans.add(Dense(w1_size,input_dim=hidden_size,activation=input_activation,weights=model_TRANS.get_weights()[:2]))
auto_decoder_trans.compile(optimizer=sgd,loss=loss)
asr_trans_w1={}
for i in ASR_AE.keys():
asr_trans_w1[i]=auto_decoder_trans.predict(ASR_AE_H2[i])
db["ASR_H2_TRANSFORMED_W1"]=asr_trans_w1
print "shape",ASR_AE["TRAIN"].shape[1]
auto_decoder_trans=Sequential()
auto_decoder_trans.add(Dense(w1_size,input_dim=hidden_size,activation=input_activation,weights=model_TRANS.get_weights()[:2]))
auto_decoder_trans.add(Dense(w2_size,input_dim=w1_size,activation=input_activation,weights=model_TRANS.get_weights()[2:4]))
auto_decoder_trans.compile(optimizer=sgd,loss=loss)
asr_trans_w2={}
for i in ASR_AE.keys():
asr_trans_w2[i]=auto_decoder_trans.predict(ASR_AE_H2[i])
db["ASR_H2_TRANSFORMED_W2"]=asr_trans_w2
model_TRANS_H2_OUT = Sequential()
model_TRANS_H2_OUT.add(Dense(TRS["DEV"].todense().shape[1],input_dim=hidden_size,init="glorot_uniform",activation=out_activation,weights=autoencode_trs.get_weights()[-2:]))
sgd_out = SGD(lr=0.01, decay=1e-4, momentum=0.9, nesterov=True)
model_TRANS_H2_OUT.compile(loss='mse', optimizer=sgd)
asr_tranform_out={}
for i in ASR_AE.keys():
asr_tranform_out[i]=model_TRANS_H2_OUT.predict(asr_transformer[i])
db["ASR_H2_TRANFORMED_OUT"]=asr_tranform_out
db.sync()
print "learn transform ae H1({})".format(hidden_size2)
model_TRANS = Sequential()
model_TRANS.add(Dense( w1_size,input_dim=hidden_size2, init='glorot_uniform', activation=input_activation))
#model_TRANS.add(Dropout(0.5))
model_TRANS.add(Dense( w2_size,input_dim=w1_size, init='glorot_uniform', activation=input_activation))
#model_TRANS.add(Dropout(0.5))
model_TRANS.add(Dense( hidden_size2,input_dim=w2_size, init='glorot_uniform', activation=input_activation))
sgd_TRANS = SGD(lr=0.01, decay=1e-4, momentum=0.9, nesterov=True)
model_TRANS.compile(loss='mse', optimizer=sgd)
# In[146]:
model_TRANS.fit(ASR_AE_H1["TRAIN"],TRS_AE_H1["TRAIN"],nb_epoch=epochs,batch_size=batch,
callbacks=[keras.callbacks.EarlyStopping(monitor='val_loss',
patience=patience, verbose=0)],
validation_data=(ASR_AE_H1["DEV"],TRS_AE_H1["DEV"]),verbose=1)
print "make trans projection H1"
asr_transformer_H1={}
for i in ASR_AE.keys():
asr_transformer_H1[i]=model_TRANS.predict(ASR_AE_H1[i])
db["ASR_H1_TRANFORMED_TRSH2"]=asr_transformer_H1
# In[ ]:
db.sync()
auto_decoder_trans=Sequential()
auto_decoder_trans.add(Dense(w1_size,input_dim=hidden_size2,activation=input_activation,weights=model_TRANS.get_weights()[:2]))
auto_decoder_trans.compile(optimizer=sgd,loss=loss)
asr_trans_H1_w1={}
for i in ASR_AE.keys():
asr_trans_H1_w1[i]=auto_decoder_trans.predict(ASR_AE_H1[i])
db["ASR_H1_TRANSFORMED_W1"]=asr_trans_w1
auto_decoder_trans=Sequential()
auto_decoder_trans.add(Dense(w1_size,input_dim=hidden_size2,activation=input_activation,weights=model_TRANS.get_weights()[:2]))
auto_decoder_trans.add(Dense(w2_size,input_dim=w1_size,activation=input_activation,weights=model_TRANS.get_weights()[2:4]))
auto_decoder_trans.compile(optimizer=sgd,loss=loss)
asr_trans_H1_w2={}
for i in ASR_AE.keys():
asr_trans_H1_w2[i]=auto_decoder_trans.predict(ASR_AE_H1[i])
db["ASR_H1_TRANSFORMED_W2"]=asr_trans_H1_w2
model_TRANS_H2_OUT = Sequential()
model_TRANS_H2_OUT.add(Dense(hidden_size,input_dim=hidden_size2,init="glorot_uniform",activation=out_activation,weights=autoencode_trs.get_weights()[-4:-2]))
model_TRANS_H2_OUT.add(Dense(TRS["TRAIN"].shape[1],input_dim=hidden_size,init="glorot_uniform",activation=out_activation,weights=autoencode_trs.get_weights()[-2:]))
sgd_out = SGD(lr=0.01, decay=1e-4, momentum=0.9, nesterov=True)
model_TRANS_H2_OUT.compile(loss='mse', optimizer=sgd)
asr_tranform_H1_out={}
for i in ASR_AE.keys():
asr_tranform_H1_out[i]=model_TRANS_H2_OUT.predict(asr_transformer_H1[i])
db["ASR_H1_TRANFORMED_OUT"]=asr_tranform_out
db.sync()
db.close()
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