‘‘‘
Author:?Zeping?Yu
Sliced?Recurrent?Neural?Network?（SRNN）.?
SRNN?is?able?to?get?much?faster?speed?than?standard?RNN?by?slicing?the?sequences?into?many?subsequences.
This?work?is?accepted?by?COLING?2018.
The?code?is?written?in?keras?using?tensorflow?backend.?We?implement?the?SRNN（82）?here?and?Yelp?2013?dataset?is?used.
If?you?have?any?question?please?contact?me?at?zepingyu@foxmail.com.
‘‘‘

import?pandas?as?pd
import?numpy?as?np

from?keras.utils.np_utils?import?to_categorical
from?keras.preprocessing.text?import?Tokenizer?text_to_word_sequence
from?keras.preprocessing.sequence?import?pad_sequences
from?keras.models?import?Model
from?keras.layers?import?Input?embedding?GRU?TimeDistributed?Dense

#load?data
df?=?pd.read_csv（“yelp_2013.csv“）
#df?=?df.sample（5000）

Y?=?df.stars.values-1



Y?=?to_categorical（Ynum_classes=5）
X?=?df.text.values






#set?hyper?parameters
MAX_NUM_WORDS?=?30000
embedDING_DIM?=?200
VALIDATION_SPLIT?=?0.1
TEST_SPLIT=0.1
NUM_FILTERS?=?50
MAX_LEN?=?512
Batch_size?=?100
EPOCHS?=?10

#shuffle?the?data


indices?=?np.arange（X.shape[0]）
np.random.seed（2018）
np.random.shuffle（indices）
X=X[indices]
Y=Y[indices]

#training?set?validation?set?and?testing?set
nb_validation_samples_val?=?int（（VALIDATION_SPLIT?+?TEST_SPLIT）?*?X.shape[0]）
nb_validation_samples_test?=?int（TEST_SPLIT?*?X.shape[0]）

x_train?=?X[:-nb_validation_samples_val]
y_train?=?Y[:-nb_validation_samples_val]
x_val?=??X[-nb_validation_samples_val:-nb_validation_samples_test]
y_val?=??Y[-nb_validation_samples_val:-nb_validation_samples_test]
x_test?=?X[-nb_validation_samples_test:]
y_test?=?Y[-nb_validation_samples_test:]

#use?tokenizer?to?build?vocab
tokenizer1?=?Tokenizer（num_words=MAX_NUM_WORDS）
tokenizer1.fit_on_texts（df.text）
vocab?=?tokenizer1.word_index

x_train_word_ids?=?tokenizer1.texts_to_sequences（x_train）
x_test_word_ids?=?tokenizer1.texts_to_sequences（x_test）
x_val_word_ids?=?tokenizer1.texts_to_sequences（x_val）

#pad?sequences?into?the?same?length
x_train_padded_seqs?=?pad_sequences（x_train_word_ids?maxlen=MAX_LEN）
x_test_padded_seqs?=?pad_sequences（x_test_word_ids?maxlen=MAX_LEN）
x_val_padded_seqs?=?pad_sequences（x_val_word_ids?maxlen=MAX_LEN）

#slice?sequences?into?many?subsequences
x_test_padded_seqs_split=[]
for?i?in?range（x_test_padded_seqs.shape[0]）:
????split1=np.split（x_test_padded_seqs[i]8）
????a=[]
????for?j?in?range（8）:
????????s=np.split（split1[j]8）
????????a.append（s）
????x_test_padded_seqs_split.append（a）

x_val_padded_seqs_split=[]
for?i?in?range（x_val_padded_seqs.shape[0]）:
????split1=np.split（x_val_padded_seqs[i]8）
????a=[]
????for?j?in?range（8）:
????????s=np.split（split1[j]8）
????????a.append（s）
????x_val_padded_seqs_split.append（a）
???
x_train_padded_seqs_split=[]
for?i?in?range（x_train_padded_seqs.shape[0]）:
????split1=np.split（x_train_padded_seqs[i]8）
????a=[]
????for?j?in?range（8）:
????????s=np.split（split1[j]8）
????