textRNN
# -*- coding: utf-8 -*-
# @Time : 2019/7/4 9:56
# @Author : hejipei
# @File : keras_sentiment_TextRNN.py
""" """
from keras import Input, Model
from keras.layers import Embedding, Dense, Dropout, CuDNNLSTM
class TextRNN(object):
def __init__(self, maxlen, max_features, embedding_dims,
class_num=1,
last_activation='sigmoid'):
self.maxlen = maxlen
self.max_features = max_features
self.embedding_dims = embedding_dims
self.class_num = class_num
self.last_activation = last_activation
def get_model(self):
input = Input((self.maxlen,))
embedding = Embedding(self.max_features, self.embedding_dims, input_length=self.maxlen)(input)
x = CuDNNLSTM(128)(embedding) # LSTM or GRU
output = Dense(self.class_num, activation=self.last_activation)(x)
model = Model(inputs=input, outputs=output)
return model
from keras.callbacks import EarlyStopping
from keras.datasets import imdb
from keras.preprocessing import sequence
# from text_rnn import TextRNN
max_features = 5000
maxlen = 400
batch_size = 32
embedding_dims = 50
epochs = 10
print('Loading data...')
(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)
print(len(x_train), 'train sequences')
print(len(x_test), 'test sequences')
print('Pad sequences (samples x time)...')
x_train = sequence.pad_sequences(x_train, maxlen=maxlen)
x_test = sequence.pad_sequences(x_test, maxlen=maxlen)
print('x_train shape:', x_train.shape)
print('x_test shape:', x_test.shape)
print('Build model...')
model = TextRNN(maxlen, max_features, embedding_dims).get_model()
model.compile('adam', 'binary_crossentropy', metrics=['accuracy'])
print('Train...')
early_stopping = EarlyStopping(monitor='val_acc', patience=3, mode='max')
model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
callbacks=[early_stopping],
validation_data=(x_test, y_test))
print('Test...')
result = model.predict(x_test)
FastText
# -*- coding: utf-8 -*-
# @Time : 2019/7/2 18:48
# @Author : hejipei
# @File : keras_sentiment_FastText.py
""" """
import numpy as np
from keras.preprocessing import sequence
from keras import Input, Model
from keras.layers import Embedding, GlobalAveragePooling1D, Dense
from keras.callbacks import EarlyStopping
from keras.datasets import imdb
class FastText(object):
def __init__(self, maxlen, max_features, embedding_dims,class_num=1,last_activation='sigmoid'):
self.maxlen = maxlen
self.max_features = max_features
self.embedding_dims = embedding_dims
self.class_num = class_num
self.last_activation = 'sigmoid'
def get_model(self):
input = Input((self.maxlen,))
embedding = Embedding(self.max_features, self.embedding_dims, input_length=self.maxlen)(input)
x = GlobalAveragePooling1D()(embedding)
output = Dense(self.class_num, activation=self.last_activation)(x)
model = Model(inputs=input, outputs=output)
return model
def create_ngram_set(input_list, ngram_value=2):
"""
Extract a set of n-grams from a list of integers.
# >>> create_ngram_set([1, 4, 9, 4, 1, 4], ngram_value=2)
{(1, 4), (4, 1), (4, 9), (9, 4)}
# >>> create_ngram_set([1, 4, 9, 4, 1, 4], ngram_value=3)
[(1, 4, 9), (4, 9, 4), (9, 4, 1), (4, 1, 4)]
"""
return set(zip(*[input_list[i:] for i in range(ngram_value)]))
def add_ngram(sequences, token_indice, ngram_range=2):
"""
Augment the input list of list (sequences) by appending n-grams values.
Example: adding bi-gram
# >>> sequences = [[1, 3, 4, 5], [1, 3, 7, 9, 2]]
# >>> token_indice = {(1, 3): 1337, (9, 2): 42, (4, 5): 2017}
# >>> add_ngram(sequences, token_indice, ngram_range=2)
[[1, 3, 4, 5, 1337, 2017], [1, 3, 7, 9, 2, 1337, 42]]
Example: adding tri-gram
# >>> sequences = [[1, 3, 4, 5], [1, 3, 7, 9, 2]]
# >>> token_indice = {(1, 3): 1337, (9, 2): 42, (4, 5): 2017, (7, 9, 2): 2018}
# >>> add_ngram(sequences, token_indice, ngram_range=3)
[[1, 3, 4, 5, 1337, 2017], [1, 3, 7, 9, 2, 1337, 42, 2018]]
"""
new_sequences = []
for input_list in sequences:
new_list = input_list[:]
for ngram_value in range(2, ngram_range + 1):
for i in range(len(new_list) - ngram_value + 1):
ngram = tuple(new_list[i:i + ngram_value])
if ngram in token_indice:
new_list.append(token_indice[ngram])
new_sequences.append(new_list)
return new_sequences
# Set parameters:
# ngram_range = 2 will add bi-grams features
ngram_range = 1
max_features = 5000
maxlen = 400
batch_size = 32
embedding_dims = 50
epochs = 10
print('Loading data...')
(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)
print(len(x_train), 'train sequences')
print(len(x_test), 'test sequences')
print('Average train sequence length: {}'.format(np.mean(list(map(len, x_train)), dtype=int)))
print('Average test sequence length: {}'.format(np.mean(list(map(len, x_test)), dtype=int)))
if ngram_range > 1:
print('Adding {}-gram features'.format(ngram_range))
# Create set of unique n-gram from the training set.
ngram_set = set()
for input_list in x_train:
for i in range(2, ngram_range + 1):
set_of_ngram = create_ngram_set(input_list, ngram_value=i)
ngram_set.update(set_of_ngram)
# Dictionary mapping n-gram token to a unique integer.
# Integer values are greater than max_features in order
# to avoid collision with existing features.
start_index = max_features + 1
token_indice = {v: k + start_index for k, v in enumerate(ngram_set)}
indice_token = {token_indice[k]: k for k in token_indice}
# max_features is the highest integer that could be found in the dataset.
max_features = np.max(list(indice_token.keys())) + 1
# Augmenting x_train and x_test with n-grams features
x_train = add_ngram(x_train, token_indice, ngram_range)
x_test = add_ngram(x_test, token_indice, ngram_range)
print('Average train sequence length: {}'.format(np.mean(list(map(len, x_train)), dtype=int)))
print('Average test sequence length: {}'.format(np.mean(list(map(len, x_test)), dtype=int)))
print('Pad sequences (samples x time)...')
x_train = sequence.pad_sequences(x_train, maxlen=maxlen)
x_test = sequence.pad_sequences(x_test, maxlen=maxlen)
print('x_train shape:', x_train.shape)
print('x_test shape:', x_test.shape)
print('Build model...')
model = FastText(maxlen, max_features, embedding_dims).get_model()
model.compile('adam', 'binary_crossentropy', metrics=['accuracy'])
print('Train...')
early_stopping = EarlyStopping(monitor='val_acc', patience=3, mode='max')
model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, callbacks=[early_stopping], validation_data=(x_test, y_test))
score, acc = model.evaluate(x_test, y_test, batch_size=128)
print('Test score:', score)
print('Test accuracy:', acc)
# print('Test...')
# result = model.predict(x_test)