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LDA/04b-mini_ae.py
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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]:
sparse_model=shelve.open("{}".format(sys.argv[2]))
in_dir = sys.argv[1]
infer_model=shelve.open("{}/infer.shelve".format(in_dir))
#['ASR', 'TRS', 'LABEL']
# In[6]:
ASR=sparse_model["ASR_wid"]
TRS=sparse_model["TRS_wid"]
LABEL=sparse_model["LABEL"]
hidden_size=40
input_activation="tanh"
out_activation="tanh"
loss="mse"
epochs=500
batch=1
patience=60
do_do=False
sgd = Adam(lr=0.00001)#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" : hidden_size,
"inside_activation" : input_activation,
"out_activation" : out_activation,
"do_dropout": 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("{}/{}".format(in_dir,name))
except:
pass
db = shelve.open("{}/{}/ae_model.shelve".format(in_dir,name),writeback=True)
db["params"] = params
db["LABEL"]=LABEL
#
json.dump(params,
open("{}/{}/ae_model.json".format(in_dir,name),"w"),
indent=4)
keys = ["ASR","TRS"]
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update LDA/.py
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mlp_h = [ 512 , 1024 , 2048] |
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mlp_loss ="categorical_crossentropy"
mlp_dropouts = [0,0,0,0]
mlp_sgd = Adam(0.0001)
mlp_epochs = 200
mlp_batch_size = 8
db["AE"] = {}
for mod in keys :
res=train_ae(infer_model["LDA"][mod]["TRAIN"],infer_model["LDA"][mod]["DEV"],infer_model["LDA"][mod]["TEST"],[params["h1"]],patience = params["patience"],sgd=sgd,in_activation="tanh",out_activation="tanh",loss=loss,epochs=epochs,batch_size=batch,verbose=0)
mlp_res_list=[]
for layer in res :
mlp_res_list.append(train_mlp(layer[0],LABEL["TRAIN"],layer[1],LABEL["DEV"],layer[2],LABEL["TEST"],mlp_h,loss=mlp_loss,dropouts=mlp_dropouts,sgd=mlp_sgd,epochs=mlp_epochs,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"],[params["h1"]],dropouts=[0],patience = params["patience"],sgd=sgd,in_activation="tanh",out_activation="tanh",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],LABEL["TRAIN"],layer[1],LABEL["DEV"],layer[2],LABEL["TEST"],mlp_h,loss=mlp_loss,dropouts=mlp_dropouts,sgd=mlp_sgd,epochs=mlp_epochs,batch_size=mlp_batch_size,fit_verbose=0))
db["AE"]["SPE"] = mlp_res_list
db.close()
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