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mlp.py
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# -*- coding: utf-8 -*-
import keras
import numpy
from keras.layers.core import Dense, Dropout, Activation , AutoEncoder
from keras.optimizers import SGD,Adam
from keras.models import Sequential
import pandas
from collections import namedtuple
from sklearn.metrics import precision_recall_fscore_support as perf
save_tuple= namedtuple("save_tuple",["pred_train","pred_dev","pred_test"])
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="lecun_uniform",dropouts=None,sgd=None,epochs=1200,batch_size=16,fit_verbose=1,test_verbose=0,save_pred=False,keep_histo=False):
model = Sequential()
if len(hidden_size) != 0 :
if not dropouts:
dropouts=[0.25]*len(hidden_size)
previous_size=hidden_size.pop(0)
model.add(Dense(previous_size,input_dim=x_train.shape[1], init=init))
model.add(Activation(input_activation))
if dropouts:
drop_prob=dropouts.pop(0)
if drop_prob > 0:
model.add(Dropout(drop_prob))
for hidden in hidden_size :
model.add(Dense(hidden,input_dim=previous_size,init=init))
model.add(Activation(hidden_activation))
if dropouts:
drop_prob=dropouts.pop(0)
if drop_prob > 0:
model.add(Dropout(drop_prob))
previous_size=hidden
model.add(Dense( y_train.shape[1],input_dim=previous_size, init=init))
else :
model.add(Dense( y_train.shape[1],input_dim=x_train.shape[1], init=init))
if dropouts:
model.add(Dropout(dropouts.pop(0)))
model.add(Activation(output_activation))
if not sgd:
sgd = SGD(lr=0.01, decay=0, momentum=0.9)
model.compile(loss=loss, optimizer=sgd)
scores_dev=[]
scores_test=[]
save=None
for i in range(epochs):
hist=model.fit(x_train, y_train, nb_epoch=1, batch_size=batch_size,verbose=fit_verbose)
pred_train=model.predict(x_train)
pred_dev=model.predict(x_dev)
pred_test=model.predict(x_test)
scores_dev.append(perf(numpy.argmax(y_dev,axis=1)+1,numpy.argmax(pred_dev,axis=1)+1,average='micro'))
scores_test.append(perf(numpy.argmax(y_test,axis=1)+1,numpy.argmax(pred_test,axis=1)+1,average='micro'))
if save is None or (len(scores_dev)>2 and scores_dev[-1][0] > scores_dev[-2][0]):
print "Save {}".format(i)
save=save_tuple(pred_train,pred_dev,pred_test)
res=[scores_dev,scores_test]
if save_pred:
res.append(save)
if keep_histo:
res.append(hist)
return res
def autoencoder(train,dev,hidden_size,input_activation="tanh",out_activation="relu",loss="mse"):
sgd = SGD(lr=0.01,momentum=0.9)
autoencode=Sequential()
autoencode.add(Dense(hidden_size,input_dim=train.shape[1],init='uniform',activation=input_activation))
autoencode.add(Dense(train.shape[1],input_dim=hidden_size,init="uniform",activation=out_activation))
autoencode.compile(optimizer=sgd,loss=loss)
print "compiled"
autoencode.fit(train,train,nb_epoch=8,batch_size=1,callbacks=[keras.callbacks.EarlyStopping(monitor='val_loss', patience=20, verbose=0)],validation_data=(dev,dev),verbose=1)
print "fited"
auto_decoder=Sequential()
auto_decoder.add(Dense(hidden_size,input_dim=train.shape[1],init='uniform',activation=input_activation,weights=autoencode.get_weights()[:2]))
auto_decoder.compile(optimizer=sgd,loss=loss)
return auto_decoder
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