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LDA/mlp.py
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# -*- coding: utf-8 -*-
import keras
import numpy as np
#from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import SGD,Adam
from keras.models import Sequential
from keras.layers import Input, Dense, Dropout
from keras.models import Model
from keras.utils.layer_utils import layer_from_config
from itertools import izip_longest
import pandas
from collections import namedtuple
from sklearn.metrics import accuracy_score as perf
save_tuple= namedtuple("save_tuple",["pred_train","pred_dev","pred_test"])
def ft_dsae(train,dev,test,
y_train=None,y_dev=None,y_test=None,
ae_hidden=[20],transfer_hidden=[20],
start_weights=None,transfer_weights=None,end_weights=None,
input_activation="tanh", output_activation="tanh",
init="glorot_uniform",
ae_dropouts=[None], transfer_do=[None],
sgd="sgd", loss="mse", patience=5, verbose=0, epochs=5, batch_size=8):
if not start_weights :
start_weights = [ None ] * len(ae_hidden)
if not transfer_weights :
transfer_weights = [None ] * len(transfer_hidden)
if not end_weights :
end_weights = [ None ] * len(end_weights)
if not transfer_do :
transfer_do = [0] * len(transfer_hidden)
predict_y = True
if y_train is None or y_dev is None or y_test is None :
y_train = train
y_dev = dev
y_test = test
predict_y = False
param_predict = [ train, dev, test ]
if predict_y :
param_predict += [ y_train, y_dev ,y_test ]
pred_by_level = [] # Contient les prediction par niveaux de transfert
layers = [Input(shape=(train.shape[1],))]
#for w in transfer_weights:
#print "TW",[ [ y.shape for y in x ] for x in w]
#print "SW",[ [ y.shape for y in x] for x in start_weights]
#print "EW",[ [ y.shape for y in x ] for x in end_weights]
for cpt in range(1,len(ae_hidden)):
#print ae_hidden,cpt
#print cpt, "before"
#print "before2", [ [ x.shape for x in y] for y in start_weights[:cpt] ]
#print "before3", [ [ x.shape for x in y] for y in transfer_weights[cpt]]
#print "before4", [ [ x.shape for x in y] for y in end_weights[cpt:]]
sizes = ae_hidden[:cpt] + transfer_hidden + ae_hidden[cpt:]
weights = start_weights[:cpt] + transfer_weights[(cpt-1)] + end_weights[cpt:]
#print "SIZES", sizes
#print "AW",[ [ y.shape for y in x ] for x in weights]
#print "WEI", len(weights) , [ len(x) for x in weights ]
if len(ae_dropouts) == len(ae_hidden):
do = ae_dropouts[:cpt] + transfer_do + ae_dropouts[cpt:]
else :
do = [ 0 ] * (len(ae_hidden) + len(transfer_hidden))
for w in weights[:-1]:
#print "STEP", size
layers.append(Dense(w[1].shape[0],activation=input_activation,init=init,weights=w)(layers[-1]))
if do :
d = do.pop(0)
if d > 0 :
layers.append(Dropout(d)(layers[-1]))
layers.append(Dense(y_train.shape[1],activation=output_activation)(layers[-1]))
models = [Model(input=layers[0] , output=x) for x in layers[1:]]
models[-1].compile(optimizer=sgd,loss=loss)
models[-1].fit(train,y_train,nb_epoch=epochs,batch_size=batch_size,callbacks=[keras.callbacks.EarlyStopping(monitor='val_loss', patience=patience, verbose=0)],validation_data=(dev,dev),verbose=verbose)
predictions = [ [x.predict(y) for y in param_predict ] for x in models ]
pred_by_level.append(predictions)
return pred_by_level
def train_mlp(x_train,y_train,x_dev,y_dev,x_test,y_test,hidden_size,input_activation="relu",hidden_activation="relu",output_activation="softmax",loss="mse",init="glorot_uniform",dropouts=None,sgd=None,epochs=1200,batch_size=16,fit_verbose=1,test_verbose=0,save_pred=False,keep_histo=False):
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layers = [Input(shape=(x_train.shape[1],))]
for h in hidden_size:
if dropouts:
d = dropouts.pop(0)
if d > 0 :
layers.append(Dropout(d)(layers[-1]))
layers.append(Dense(h,init=init,activation=input_activation)(layers[-1]))
#if dropouts:
# drop_prob=dropouts.pop(0)
# if drop_prob > 0:
# model.add(Dropout(drop_prob))
#if dropouts:
# drop_prob=dropouts.pop(0)
# if drop_prob > 0:
# model.add(Dropout(drop_prob))
#if dropouts:
# model.add(Dropout(dropouts.pop(0)))
if dropouts:
d = dropouts.pop(0)
if d > 0 :
layers.append(Dropout(d)(layers[-1]))
layers.append(Dense( y_train.shape[1],activation=output_activation,init=init)(layers[-1]))
model = Model(layers[0] , layers[-1])
if not sgd:
sgd = SGD(lr=0.01, decay=0, momentum=0.9)
model.compile(loss=loss, optimizer=sgd,metrics=['accuracy'])
scores_dev=[]
scores_test=[]
scores_train=[]
save=None
for i in range(epochs):
hist=model.fit(x_train, y_train, nb_epoch=1, batch_size=batch_size,verbose=fit_verbose,validation_data=(x_dev,y_dev))
pred_train=model.predict(x_train)
pred_dev=model.predict(x_dev)
pred_test=model.predict(x_test)
scores_train.append(perf(np.argmax(y_train,axis=1),np.argmax(pred_train,axis=1)))
scores_dev.append(perf(np.argmax(y_dev,axis=1),np.argmax(pred_dev,axis=1)))
scores_test.append(perf(np.argmax(y_test,axis=1),np.argmax(pred_test,axis=1)))
if fit_verbose :
print "{} {} {} {}".format(i,scores_train[-1],scores_dev[-1],scores_test[-1])
if save is None or (len(scores_dev)>2 and scores_dev[-1] > scores_dev[-2]):
save=save_tuple(pred_train,pred_dev,pred_test)
arg_dev = np.argmax(scores_dev)
best_dev=scores_dev[arg_dev]
best_test=scores_test[arg_dev]
max_test=np.max(scores_test)
if fit_verbose:
print " res : {} {} {}".format(best_dev,best_test,max_test)
res=[scores_train,scores_dev,scores_test]
if save_pred:
res.append(save)
if keep_histo:
res.append(hist)
return res
def train_ae(train,dev,test,hidden_sizes,y_train=None,y_dev=None,y_test=None,dropouts=None,input_activation="tanh",output_activation="tanh",loss="mse",sgd=None,epochs=500,batch_size=8,verbose=1,patience=20,get_weights=False,set_weights=[]):
input_vect = Input(shape=(train.shape[1],))
previous = [input_vect]
if dropouts is None:
dropouts = [ 0 ] * (len(hidden_sizes) +1)
if sgd is None :
sgd = SGD(lr=0.01, decay=0, momentum=0.9)
did_do = False
if dropouts :
d = dropouts.pop(0)
if d :
previous.append(Dropout(d)(previous[-1]))
did_do = True
for h_layer,weight_layer in izip_longest(hidden_sizes,set_weights,fillvalue=None) :
# ,weights=w
if weight_layer :
w = weight_layer[0]
else :
w = None
#print "ADD SIZE" , h_layer
if did_do :
p = previous.pop()
did_do = False
else :
p = previous[-1]
previous.append(Dense(h_layer,activation=input_activation,weights=w)(previous[-1]))
if dropouts:
d = dropouts.pop(0)
if d :
previous.append(Dropout(d)(previous[-1]))
did_do = True
predict_y = True
if y_train is None or y_dev is None or y_test is None :
y_train = train
y_dev = dev
y_test = test
predict_y = False
previous.append(Dense(y_train.shape[1],activation=output_activation)(previous[-1]))
models = [Model(input=previous[0] , output=x) for x in previous[1:]]
print "MLP", sgd, loss
models[-1].compile(optimizer=sgd,loss=loss)
models[-1].fit(train,y_train,nb_epoch=epochs,batch_size=batch_size,callbacks=[keras.callbacks.EarlyStopping(monitor='val_loss', patience=patience, verbose=0)],validation_data=(dev,dev),verbose=verbose)
param_predict = [ train, dev, test ]
if predict_y :
param_predict += [ y_train, y_dev ,y_test ]
predictions = [ [x.predict(y) for y in param_predict ] for x in models ]
if get_weights :
weights = [ x.get_weights() for x in models[-1].layers if x.get_weights() ]
return ( predictions , weights )
else :
return predictions
def train_sae(train,dev,test,hidden_sizes,y_train=None,y_dev=None,y_test=None,dropouts=None,input_activation="tanh",output_activation="tanh",loss="mse",sgd=None,epochs=500,batch_size=8,verbose=1,patience=20):
weights = []
predictions = [[(train,dev,test),()]]
ft_pred = []
past_sizes = []
for size in hidden_sizes :
#print "DO size " , size , "FROM" , hidden_sizes
res_pred, res_wght = train_ae(predictions[-1][-2][0], predictions[-1][-2][1],predictions[-1][-2][2],[size],
dropouts=dropouts, input_activation=input_activation,
output_activation=output_activation, loss=loss, sgd=sgd,
epochs=epochs, batch_size=batch_size, verbose=verbose,
patience=patience,get_weights=True)
past_sizes.append(size)
weights.append(res_wght)
predictions.append(res_pred)
#print "FINE TUNE "
res_ftpred = train_ae(train,dev,test,past_sizes,y_train=y_train,y_dev=y_dev,y_test=y_test,
dropouts=dropouts,
input_activation=input_activation,
output_activation=output_activation,
loss=loss,sgd=sgd,epochs=epochs,
batch_size=batch_size,verbose=verbose,patience=patience,
set_weights=weights)
ft_pred.append(res_ftpred)
return ( predictions[1:] , ft_pred)
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