# -*- coding: utf-8 -*-
'''
Run on GPU: THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python
'''
from __future__ import print_function
import numpy as np
# np.random.seed(3435) # for reproducibility, should be first
import keras
from keras.preprocessing import sequence
from keras.models import Sequential
from keras.layers import Dropout, Activation, Flatten, \
Embedding, Conv1D, MaxPooling1D, AveragePooling1D, \
Input, Dense, merge
from keras.regularizers import l2
from keras.layers.recurrent import LSTM, GRU, SimpleRNN
from keras.constraints import maxnorm
from keras.datasets import imdb
from keras import callbacks
from keras.utils import generic_utils
from keras.models import Model
from keras.optimizers import Adadelta, Adagrad
import time
batch_size = 50
nb_filter = 200
filter_length = 4
hidden_dims = nb_filter * 2
nb_epoch = 60
RNN = GRU
rnn_output_size = 100
folds = 10
print('Loading data...')
from process_sst2_data import SentimentPhrase
from sst2_data import load_data
X_train, y_train, X_test, y_test, W, W2 = load_data()
maxlen = X_train.shape[1]
max_features = len(W)
embedding_dims = len(W[0])
print('Train...')
accs = []
first_run = True
print(len(X_train), 'train sequences')
print(len(X_test), 'test sequences')
print('X_train shape:', X_train.shape)
print('X_test shape:', X_test.shape)
rand_idx = np.random.permutation(range(len(X_train)))
X_train = X_train[rand_idx]
y_train = y_train[rand_idx]
def build_model():
main_input = Input(shape=(maxlen, ), dtype='int32', name='main_input')
embedding = Embedding(max_features, embedding_dims,
weights=[np.matrix(W)], input_length=maxlen,
name='embedding')(main_input)
embedding = Dropout(0.50)(embedding)
conv4 = Conv1D(filters=nb_filter,
kernel_size=4,
padding='valid',
activation='relu',
strides=1,
name='conv4')(embedding)
maxConv4 = MaxPooling1D(pool_size=2,
name='maxConv4')(conv4)
conv5 = Conv1D(filters=nb_filter,
kernel_size=5,
padding='valid',
activation='relu',
strides=1,
name='conv5')(embedding)
maxConv5 = MaxPooling1D(pool_size=2,
name='maxConv5')(conv5)
# x = merge([maxConv4, maxConv5], mode='concat')
x = keras.layers.concatenate([maxConv4, maxConv5])
x = Dropout(0.15)(x)
x = RNN(rnn_output_size)(x)
x = Dense(hidden_dims, activation='relu', kernel_initializer='he_normal',
kernel_constraint = maxnorm(3), bias_constraint=maxnorm(3),
name='mlp')(x)
x = Dropout(0.10, name='drop')(x)
output = Dense(1, kernel_initializer='he_normal',
activation='sigmoid', name='output')(x)
model = Model(inputs=main_input, outputs=output)
model.compile(loss='binary_crossentropy',
# optimizer=Adadelta(lr=0.95, epsilon=1e-06),
# optimizer=Adadelta(lr=1.0, rho=0.95, epsilon=1e-08, decay=0.0),
# optimizer=Adagrad(lr=0.01, epsilon=1e-08, decay=1e-4),
metrics=["accuracy"])
return model
model = build_model()
if first_run:
first_run = False
print(model.summary())
best_val_acc = 0
best_test_acc = 0
for j in xrange(nb_epoch):
a = time.time()
his = model.fit(X_train, y_train,
batch_size=batch_size,
validation_data=[X_test, y_test],
shuffle=True,
epochs=1, verbose=1)
print('Epoch %d/%d\t%s' % (j + 1, nb_epoch, str(his.history)))
if his.history['val_acc'][0] >= best_val_acc:
score, acc = model.evaluate(X_test, y_test,
batch_size=batch_size,
verbose=2)
best_val_acc = his.history['val_acc'][0]
best_test_acc = acc
print('Got best epoch best val acc is %f test acc is %f' %
(best_val_acc, best_test_acc))
if len(accs) > 0:
print('Current avg test acc:', str(np.mean(accs)))
b = time.time()
cost = b - a
left = (nb_epoch - j - 1)
print('One round cost %ds, %d round %ds %dmin left' % (cost, left,
cost * left,
cost * left / 60.0))
accs.append(best_test_acc)
print('Avg test acc:', str(np.mean(accs)))
