blob: d88a8e699420c177c4f193fee7e9b16bbe998a27 [file] [log] [blame]
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
import sys
import os
import mxnet as mx
import numpy as np
import argparse
import logging
import data_helpers
logging.basicConfig(level=logging.DEBUG)
parser = argparse.ArgumentParser(description="CNN for text classification",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--pretrained-embedding', type=bool, default=False,
help='use pre-trained word2vec')
parser.add_argument('--num-embed', type=int, default=300,
help='embedding layer size')
parser.add_argument('--gpus', type=str, default='',
help='list of gpus to run, e.g. 0 or 0,2,5. empty means using cpu. ')
parser.add_argument('--kv-store', type=str, default='local',
help='key-value store type')
parser.add_argument('--num-epochs', type=int, default=200,
help='max num of epochs')
parser.add_argument('--batch-size', type=int, default=50,
help='the batch size.')
parser.add_argument('--optimizer', type=str, default='rmsprop',
help='the optimizer type')
parser.add_argument('--lr', type=float, default=0.0005,
help='initial learning rate')
parser.add_argument('--dropout', type=float, default=0.0,
help='dropout rate')
parser.add_argument('--disp-batches', type=int, default=50,
help='show progress for every n batches')
parser.add_argument('--save-period', type=int, default=10,
help='save checkpoint for every n epochs')
def save_model():
if not os.path.exists("checkpoint"):
os.mkdir("checkpoint")
return mx.callback.do_checkpoint("checkpoint/checkpoint", args.save_period)
def data_iter(batch_size, num_embed, pre_trained_word2vec=False):
print('Loading data...')
if pre_trained_word2vec:
word2vec = data_helpers.load_pretrained_word2vec('data/rt.vec')
x, y = data_helpers.load_data_with_word2vec(word2vec)
# reshpae for convolution input
x = np.reshape(x, (x.shape[0], 1, x.shape[1], x.shape[2]))
embed_size = x.shape[-1]
sentence_size = x.shape[2]
vocab_size = -1
else:
x, y, vocab, vocab_inv = data_helpers.load_data()
embed_size = num_embed
sentence_size = x.shape[1]
vocab_size = len(vocab)
# randomly shuffle data
np.random.seed(10)
shuffle_indices = np.random.permutation(np.arange(len(y)))
x_shuffled = x[shuffle_indices]
y_shuffled = y[shuffle_indices]
# split train/valid set
x_train, x_dev = x_shuffled[:-1000], x_shuffled[-1000:]
y_train, y_dev = y_shuffled[:-1000], y_shuffled[-1000:]
print('Train/Valid split: %d/%d' % (len(y_train), len(y_dev)))
print('train shape:', x_train.shape)
print('valid shape:', x_dev.shape)
print('sentence max words', sentence_size)
print('embedding size', embed_size)
print('vocab size', vocab_size)
train = mx.io.NDArrayIter(
x_train, y_train, batch_size, shuffle=True)
valid = mx.io.NDArrayIter(
x_dev, y_dev, batch_size)
return (train, valid, sentence_size, embed_size, vocab_size)
def sym_gen(batch_size, sentence_size, num_embed, vocab_size,
num_label=2, filter_list=[3, 4, 5], num_filter=100,
dropout=0.0, pre_trained_word2vec=False):
input_x = mx.sym.Variable('data')
input_y = mx.sym.Variable('softmax_label')
# embedding layer
if not pre_trained_word2vec:
embed_layer = mx.sym.Embedding(data=input_x, input_dim=vocab_size, output_dim=num_embed, name='vocab_embed')
conv_input = mx.sym.Reshape(data=embed_layer, target_shape=(batch_size, 1, sentence_size, num_embed))
else:
conv_input = input_x
# create convolution + (max) pooling layer for each filter operation
pooled_outputs = []
for i, filter_size in enumerate(filter_list):
convi = mx.sym.Convolution(data=conv_input, kernel=(filter_size, num_embed), num_filter=num_filter)
relui = mx.sym.Activation(data=convi, act_type='relu')
pooli = mx.sym.Pooling(data=relui, pool_type='max', kernel=(sentence_size - filter_size + 1, 1), stride=(1,1))
pooled_outputs.append(pooli)
# combine all pooled outputs
total_filters = num_filter * len(filter_list)
concat = mx.sym.Concat(*pooled_outputs, dim=1)
h_pool = mx.sym.Reshape(data=concat, target_shape=(batch_size, total_filters))
# dropout layer
if dropout > 0.0:
h_drop = mx.sym.Dropout(data=h_pool, p=dropout)
else:
h_drop = h_pool
# fully connected
cls_weight = mx.sym.Variable('cls_weight')
cls_bias = mx.sym.Variable('cls_bias')
fc = mx.sym.FullyConnected(data=h_drop, weight=cls_weight, bias=cls_bias, num_hidden=num_label)
# softmax output
sm = mx.sym.SoftmaxOutput(data=fc, label=input_y, name='softmax')
return sm, ('data',), ('softmax_label',)
def train(symbol, train_iter, valid_iter, data_names, label_names):
devs = mx.cpu() if args.gpus is None or args.gpus is '' else [
mx.gpu(int(i)) for i in args.gpus.split(',')]
module = mx.mod.Module(symbol, data_names=data_names, label_names=label_names, context=devs)
module.fit(train_data = train_iter,
eval_data = valid_iter,
eval_metric = 'acc',
kvstore = args.kv_store,
optimizer = args.optimizer,
optimizer_params = { 'learning_rate': args.lr },
initializer = mx.initializer.Uniform(0.1),
num_epoch = args.num_epochs,
batch_end_callback = mx.callback.Speedometer(args.batch_size, args.disp_batches),
epoch_end_callback = save_model())
if __name__ == '__main__':
# parse args
args = parser.parse_args()
# data iter
train_iter, valid_iter, sentence_size, embed_size, vocab_size = data_iter(args.batch_size,
args.num_embed,
args.pretrained_embedding)
# network symbol
symbol, data_names, label_names = sym_gen(args.batch_size,
sentence_size,
embed_size,
vocab_size,
num_label=2, filter_list=[3, 4, 5], num_filter=100,
dropout=args.dropout, pre_trained_word2vec=args.pretrained_embedding)
# train cnn model
train(symbol, train_iter, valid_iter, data_names, label_names)