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LDA/04b-mmf_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 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]:
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json_conf =json.load(open(sys.argv[3]))
ae_conf = json_conf["ae"]
hidden_size= ae_conf["hidden_size"]
input_activation=ae_conf["input_activation"]
output_activation=ae_conf["output_activation"]
loss=ae_conf["loss"]
epochs=ae_conf["epochs"]
batch=ae_conf["batch"]
patience=ae_conf["patience"]
do_do=ae_conf["do"]
try:
k = ae_conf["sgd"]
if ae_conf["sgd"]["name"] == "adam":
sgd = Adam(lr=ae_conf["sgd"]["lr"])#SGD(lr=0.00001,nesterov=False) #'rmsprop'# Adam(lr=0.00001)#SGD(lr=0.001, momentum=0.9, nesterov=True)
elif ae_conf["sgd"]["name"] == "sgd":
sgd = SGD(lr=ae_conf["sgd"]["lr"])
except:
sgd = ae_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"]
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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"]
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try:
os.mkdir("{}/{}".format(in_dir,name))
except:
pass
db = shelve.open("{}/{}/ae_model.shelve".format(in_dir,name),writeback=True)
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db["LABEL"]=infer_model["LABEL"] # |
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keys = ["ASR","TRS"]
db["AE"] = {}
db["LDA"] = {}
for mod in keys :
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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,
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input_activation=mlp_input_activation, |
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output_activation=mlp_output_activation,
dropouts=mlp_dropouts,
fit_verbose=0)
res=train_ae(infer_model["LDA"][mod]["TRAIN"],infer_model["LDA"][mod]["DEV"],infer_model["LDA"][mod]["TEST"],
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hidden_size,patience = patience,sgd=sgd, |
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dropouts=do_do,input_activation=input_activation,output_activation=output_activation,
loss=loss,epochs=epochs,batch_size=batch,verbose=0)
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,
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input_activation=mlp_input_activation, |
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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"],
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hidden_size,dropouts=do_do,patience = patience, |
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sgd=sgd,input_activation=input_activation,output_activation=output_activation,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],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["AE"]["SPE"] = mlp_res_list
db.sync()
db.close()
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