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LDA/04d-mmf_dsae.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 |
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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])) dsae_conf = json_conf["dsae"] hidden_size= dsae_conf["hidden_size"] input_activation=dsae_conf["input_activation"] output_activation=dsae_conf["output_activation"] loss=dsae_conf["loss"] epochs=dsae_conf["epochs"] batch_size=dsae_conf["batch"] patience=dsae_conf["patience"] do_do=dsae_conf["do"] try: k = dsae_conf["sgd"] if dsae_conf["sgd"]["name"] == "adam": sgd = Adam(lr=dsae_conf["sgd"]["lr"])#SGD(lr=0.00001,nesterov=False) #'rmsprop'# Adam(lr=0.00001)#SGD(lr=0.001, momentum=0.9, nesterov=True) elif dsae_conf["sgd"]["name"] == "sgd": sgd = SGD(lr=dsae_conf["sgd"]["lr"]) except: sgd = dsae_conf["sgd"] trans_conf = json_conf["dsae"]["transform"] trans_hidden_size=trans_conf["hidden_size"] trans_input_activation=trans_conf["input_activation"] trans_output_activation=trans_conf["output_activation"] trans_loss=trans_conf["loss"] trans_epochs=trans_conf["epochs"] trans_batch_size=trans_conf["batch"] trans_patience=trans_conf["patience"] trans_do=trans_conf["do"] try: k = trans_conf["sgd"] if trans_conf["sgd"]["name"] == "adam": trans_sgd = Adam(lr=trans_conf["sgd"]["lr"])#SGD(lr=0.00001,nesterov=False) #'rmsprop'# Adam(lr=0.00001)#SGD(lr=0.001, momentum=0.9, nesterov=True) elif trans_conf["sgd"]["name"] == "sgd": trans_sgd = SGD(lr=trans_conf["sgd"]["lr"]) except e : trans_sgd = trans_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: |
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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"] try: 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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# |
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keys = ["ASR","TRS"] db["DSAE"] = {} db["DSAEFT"] = {} mod = "ASR" res_tuple_ASR = train_ae(infer_model["LDA"][mod]["TRAIN"], infer_model["LDA"][mod]["DEV"], infer_model["LDA"][mod]["TEST"], hidden_size,dropouts=do_do, patience = patience,sgd=sgd, input_activation=input_activation, output_activation=output_activation,loss=loss,epochs=epochs, batch_size=batch_size,verbose=0,get_weights=True) mlp_res_list = [] for layer in res_tuple_ASR[0]: 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=mlp_input_activation, batch_size=mlp_batch_size,fit_verbose=0)) db["DSAE"][mod] = mlp_res_list mod = "TRS" |
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res_tuple_TRS = train_ae(infer_model["LDA"][mod]["TRAIN"], infer_model["LDA"][mod]["DEV"], infer_model["LDA"][mod]["TEST"], hidden_size,dropouts=do_do, sgd=sgd,input_activation=input_activation, output_activation=output_activation,loss=loss,epochs=epochs, batch_size=batch_size,patience=patience, verbose=0,get_weights=True) mlp_res_list = [] for layer in res_tuple_TRS[0]: 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=mlp_input_activation, batch_size=mlp_batch_size,fit_verbose=0)) db["DSAE"][mod] = mlp_res_list transfert = [] print " get weight trans" |
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#for asr_pred, trs_pred in zip(res_tuple_ASR[0], res_tuple_TRS[0]): # print "ASR", [ x.shape for x in asr_pred] |
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# print "TRS", [ x.shape for x in trs_pred] |
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for asr_pred, trs_pred in zip(res_tuple_ASR[0], res_tuple_TRS[0]): |
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# print "ASR", [ x.shape for x in asr_pred] |
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# print "TRS", [ x.shape for x in trs_pred] # print " TRANS SGD", trans_sgd |
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transfert.append( train_ae(asr_pred[0], asr_pred[1], asr_pred[2], trans_hidden_size, dropouts=trans_do, y_train = trs_pred[0], y_dev=trs_pred[1], y_test = trs_pred[2], patience = trans_patience,sgd=trans_sgd, input_activation=trans_input_activation, output_activation=trans_output_activation, loss=trans_loss, epochs=trans_epochs, batch_size=trans_batch_size,verbose=0,get_weights=True) ) mod = "ASR" mlp_res_bylvl = [] print " MLP on transfert " for level, w in transfert : mlp_res_list = [] for layer in level : 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=mlp_input_activation, batch_size=mlp_batch_size,fit_verbose=0)) mlp_res_bylvl.append(mlp_res_list) db["DSAE"]["transfert"] = mlp_res_bylvl print " FT " WA = res_tuple_ASR[1] |
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#print "WA", len(WA), [ len(x) for x in WA] |
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WT = res_tuple_TRS[1] |
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#print "WT", len(WT), [ len(x) for x in WT] |
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Wtr = [ x[1] for x in transfert] |
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#print "Wtr", len(Wtr), [ len(x) for x in Wtr],[ len(x[1]) for x in Wtr] |
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ft_res = ft_dsae(infer_model["LDA"]["ASR"]["TRAIN"], infer_model["LDA"]["ASR"]["DEV"], infer_model["LDA"]["ASR"]["TEST"], y_train=infer_model["LDA"]["TRS"]["TRAIN"], y_dev=infer_model["LDA"]["TRS"]["DEV"], y_test=infer_model["LDA"]["TRS"]["TEST"], ae_hidden = hidden_size, transfer_hidden = trans_hidden_size, start_weights = WA, transfer_weights = Wtr, end_weights = WT, input_activation = input_activation, output_activation = output_activation, ae_dropouts= do_do, transfer_do = trans_do, sgd = sgd, loss = loss , patience = patience, batch_size = batch_size, epochs= epochs) mlps_by_lvls= [] for level in ft_res : mlp_res_list = [] for layer in level : 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=mlp_input_activation, batch_size=mlp_batch_size,fit_verbose=0)) mlps_by_lvls.append(mlp_res_list) db["DSAEFT"]["transfert"] = mlps_by_lvls db.close() |