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LDA/04e-mm_vae.py
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# coding: utf-8 |
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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]: |
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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"] |
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try: |
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os.mkdir("{}/{}".format(in_dir,name)) |
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except: pass |
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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["VAE"] = {} db["LDA"] = {} for mod in keys : |
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#print mod |
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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, input_activation=input_activation, output_activation=mlp_output_activation, dropouts=mlp_dropouts, fit_verbose=0) res=train_vae(infer_model["LDA"][mod]["TRAIN"],infer_model["LDA"][mod]["DEV"],infer_model["LDA"][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 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["VAE"][mod]=mlp_res_list mod = "ASR" mod2= "TRS" mlp_res_list=[] res = train_vae(infer_model["LDA"][mod]["TRAIN"], infer_model["LDA"][mod]["DEV"], infer_model["LDA"][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["LDA"][mod2]["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["VAE"]["SPE"] = mlp_res_list db.sync() db.close() |