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nn.py
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nn.py
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#! /usr/bin/env python
#coding=utf-8
from __future__ import division
from functools import reduce
import re
import tarfile
import math
import numpy as np
np.random.seed(1337) # for reproducibility
from keras.preprocessing import sequence
from keras.layers import Input, Embedding, LSTM, Dense, merge, TimeDistributed
from keras.models import Model, Sequential
from sklearn.metrics import average_precision_score
from keras.layers.core import *
from keras.layers.convolutional import Convolution1D, MaxPooling1D, AveragePooling1D
from keras.layers.recurrent import GRU
EMBED_SIZE = 16
HIDDEN_SIZE = 16
MAX_LEN = 100
BATCH_SIZE = 32
EPOCHS = 3
nb_filter=250
filter_length=3
def get_embedding_input_output(part_name,vocab_size):
main_input = Input(shape=(MAX_LEN,), dtype='int32', name=part_name+'_input')
x = Embedding(output_dim=EMBED_SIZE, input_dim=vocab_size, input_length=MAX_LEN)(main_input)
return main_input,x
def lstm_train(X_train,y_train,vocab_size):
X_train = sequence.pad_sequences(X_train, maxlen=MAX_LEN)
main_input = Input(shape=(MAX_LEN,), dtype='int32')
x = Embedding(output_dim=EMBED_SIZE, input_dim=vocab_size, input_length=MAX_LEN)(main_input)
lstm_out = LSTM(HIDDEN_SIZE)(x)
main_loss = Dense(1, activation='sigmoid', name='main_output')(lstm_out)
model = Model(input=main_input, output=main_loss)
model.compile(loss='binary_crossentropy', optimizer='rmsprop')
model.fit(X_train, y_train, batch_size=BATCH_SIZE, nb_epoch=EPOCHS)
return model
def cnn_train(X_train,y_train,vocab_size):
X_train = sequence.pad_sequences(X_train, maxlen=MAX_LEN)
print('Build model...')
model = Sequential()
model.add(Embedding(vocab_size, EMBED_SIZE, input_length=MAX_LEN))
model.add(Dropout(0.25))
# we add a Convolution1D, which will learn nb_filter
# word group filters of size filter_length:
model.add(Convolution1D(nb_filter=nb_filter,
filter_length=filter_length,
border_mode='valid',
activation='relu',
subsample_length=1))
# we use standard max pooling (halving the output of the previous layer):
model.add(MaxPooling1D(pool_length=2))
# We flatten the output of the conv layer,
# so that we can add a vanilla dense layer:
model.add(Flatten())
# We add a vanilla hidden layer:
model.add(Dense(HIDDEN_SIZE))
model.add(Dropout(0.25))
model.add(Activation('relu'))
# We project onto a single unit output layer, and squash it with a sigmoid:
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='rmsprop')
model.fit(X_train, y_train, batch_size=BATCH_SIZE, nb_epoch=EPOCHS, show_accuracy=True)
return model
def cnn_combine_train(X_train_list,y_train,vocab_size):
N=len(X_train_list)
X_train_list = [sequence.pad_sequences(x_train, maxlen=MAX_LEN) for x_train in X_train_list]
input_list=[]
out_list=[]
for i in range(N):
input,out=get_embedding_input_output('f%d' %i,vocab_size)
input_list.append(input)
out_list.append(out)
x = merge(out_list,mode='concat')
x = Dropout(0.25)(x)
# we add a Convolution1D, which will learn nb_filter
# word group filters of size filter_length:
x = Convolution1D(nb_filter=nb_filter,
filter_length=filter_length,
border_mode='valid',
activation='relu',
subsample_length=1)(x)
# we use standard max pooling (halving the output of the previous layer):
x = MaxPooling1D(pool_length=2)(x)
# We flatten the output of the conv layer,
# so that we can add a vanilla dense layer:
x = Flatten()(x)
# We add a vanilla hidden layer:
x = Dense(HIDDEN_SIZE)(x)
x = Dropout(0.25)(x)
x = Activation('relu')(x)
# We project onto a single unit output layer, and squash it with a sigmoid:
x = Dense(1)(x)
x = Activation('sigmoid')(x)
model = Model(input=input_list, output=x)
model.compile(loss='binary_crossentropy', optimizer='rmsprop')
model.fit(X_train_list, y_train, batch_size=BATCH_SIZE, nb_epoch=EPOCHS)
return model
def lstm_combine_train(X_train_list,y_train,vocab_size):
N=len(X_train_list)
X_train_list = [sequence.pad_sequences(x_train, maxlen=MAX_LEN) for x_train in X_train_list]
input_list=[]
out_list=[]
for i in range(N):
input,out=get_embedding_input_output('f%d' %i,vocab_size)
input_list.append(input)
out_list.append(out)
x = merge(out_list,mode='concat')
x = LSTM(HIDDEN_SIZE)(x)
# We project onto a single unit output layer, and squash it with a sigmoid:
x = Dense(1)(x)
x = Activation('sigmoid')(x)
model = Model(input=input_list, output=x)
model.compile(loss='binary_crossentropy', optimizer='rmsprop')
model.fit(X_train_list, y_train, batch_size=BATCH_SIZE, nb_epoch=EPOCHS)
return model
def lstm_attention_combine_train(X_train_list,y_train,vocab_size):
N=len(X_train_list)
X_train_list = [sequence.pad_sequences(x_train, maxlen=MAX_LEN) for x_train in X_train_list]
input_list=[]
out_list=[]
for i in range(N):
input,out=get_embedding_input_output('f%d' %i,vocab_size)
input_list.append(input)
out_list.append(out)
x = merge(out_list,mode='concat')
lstm_out = LSTM(HIDDEN_SIZE, return_sequences=True)(x)
x = lstm_out
for i in range(10):
att = TimeDistributed(Dense(1))(x)
att = Flatten()(att)
att = Activation(activation="softmax")(att)
att = RepeatVector(HIDDEN_SIZE)(att)
att = Permute((2,1))(att)
x = att
mer = merge([att, lstm_out], "mul")
mer = merge([mer, out_list[-1]], 'mul')
hid = AveragePooling1D(pool_length=2)(mer)
hid = Flatten()(hid)
#hid = merge([hid,out_list[-1]], mode='concat')
main_loss = Dense(1, activation='sigmoid', name='main_output')(hid)
model = Model(input=input_list, output=main_loss)
model.compile(loss='binary_crossentropy', optimizer='rmsprop')
model.fit(X_train_list, y_train, batch_size=BATCH_SIZE, nb_epoch=EPOCHS)
return model
def lstm_memory_train(X_train_list,y_train,vocab_size):
N=len(X_train_list)
X_train_list = [sequence.pad_sequences(x_train, maxlen=MAX_LEN) for x_train in X_train_list]
input_list=[]
out_list=[]
for i in range(N):
input,out=get_embedding_input_output('f%d' %i,vocab_size)
input_list.append(input)
out_list.append(out)
x = merge(out_list,mode='concat')
lstm_out = LSTM(HIDDEN_SIZE, return_sequences=True)(x)
lstm_share=GRU(HIDDEN_SIZE, return_sequences=True)
x = lstm_out
for i in range(2):
att = TimeDistributed(Dense(1))(x)
att = Flatten()(att)
att = Activation(activation="softmax")(att)
att = RepeatVector(HIDDEN_SIZE)(att)
att = Permute((2,1))(att)
mer = merge([att, lstm_out], "mul")
mer = merge([mer, out_list[-1]], 'mul')
z = merge([lstm_out,mer],'sum')
z = lstm_share(z)
x = z
hid = AveragePooling1D(pool_length=2)(x)
hid = Flatten()(hid)
#hid = merge([hid,out_list[-1]], mode='concat')
main_loss = Dense(1, activation='sigmoid', name='main_output')(hid)
model = Model(input=input_list, output=main_loss)
model.compile(loss='binary_crossentropy', optimizer='rmsprop')
model.fit(X_train_list, y_train, batch_size=BATCH_SIZE, nb_epoch=EPOCHS)
return model