# -*- coding: utf-8 -*-
"""
@author:XuMing（xuming624@qq.com)
@description:
"""
from keras.layers import Dense, Dropout, Flatten, Input, MaxPooling1D, Convolution1D
from keras.layers import Embedding
from keras.layers import GlobalAveragePooling1D
from keras.layers import LSTM, Bidirectional, TimeDistributed
from keras.layers.merge import Concatenate
from keras.models import Model
from keras.models import Sequential
def fasttext_model(max_len=300,
vocabulary_size=20000,
embedding_dim=128,
num_classes=4):
model = Sequential()
# embed layer by maps vocab index into emb dimensions
model.add(Embedding(input_dim=vocabulary_size, output_dim=embedding_dim, input_length=max_len))
# pooling the embedding
model.add(GlobalAveragePooling1D())
# output multi classification of num_classes
model.add(Dense(num_classes, activation='softmax'))
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()
return model
def cnn_model(max_len=400,
vocabulary_size=20000,
embedding_dim=128,
hidden_dim=128,
num_filters=512,
filter_sizes="3,4,5",
num_classses=4,
dropout=0.5):
print("Creating text CNN Model...")
# a tensor
inputs = Input(shape=(max_len,), dtype='int32')
# emb
embedding = Embedding(input_dim=vocabulary_size, output_dim=embedding_dim,
input_length=max_len, name="embedding")(inputs)
# convolution block
if "," in filter_sizes:
filter_sizes = filter_sizes.split(",")
else:
filter_sizes = [3, 4, 5]
conv_blocks = []
for sz in filter_sizes:
conv = Convolution1D(filters=num_filters,
kernel_size=int(sz),
strides=1,
padding='valid',
activation='relu')(embedding)
conv = MaxPooling1D()(conv)
conv = Flatten()(conv)
conv_blocks.append(conv)
conv_concate = Concatenate()(conv_blocks) if len(conv_blocks) > 1 else conv_blocks[0]
dropout_layer = Dropout(dropout)(conv_concate)
output = Dense(hidden_dim, activation='relu')(dropout_layer)
output = Dense(num_classses, activation='softmax')(output)
# model
model = Model(inputs=inputs, outputs=output)
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()
return model
def rnn_model(max_len=400,
vocabulary_size=20000,
embedding_dim=128,
hidden_dim=128,
num_classes=4):
print("Bidirectional LSTM...")
inputs = Input(shape=(max_len,), dtype='int32')
embedding = Embedding(input_dim=vocabulary_size, output_dim=embedding_dim,
input_length=max_len, name="embedding")(inputs)
lstm_layer = Bidirectional(LSTM(hidden_dim))(embedding)
output = Dense(num_classes, activation='softmax')(lstm_layer)
model = Model(inputs, output)
model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
model.summary()
return model
def han_model(max_len=400,
vocabulary_size=20000,
embedding_dim=128,
hidden_dim=128,
max_sentences=16,
num_classes=4):
"""
Implementation of document classification model described in
`Hierarchical Attention Networks for Document Classification (Yang et al., 2016)`
(https://www.cs.cmu.edu/~diyiy/docs/naacl16.pdf)
:param max_len:
:param vocabulary_size:
:param embedding_dim:
:param hidden_dim:
:param max_sentences:
:param num_classes:
:return:
"""
print("Hierarchical Attention Network...")
inputs = Input(shape=(max_len,), dtype='int32')
embedding = Embedding(input_dim=vocabulary_size, output_dim=embedding_dim,
input_length=max_len, name="embedding")(inputs)
lstm_layer = Bidirectional(LSTM(hidden_dim))(embedding)
# lstm_layer_att = AttLayer(hidden_dim)(lstm_layer)
sent_encoder = Model(inputs, lstm_layer)
doc_inputs = Input(shape=(max_sentences, max_len), dtype='int32', name='doc_input')
doc_encoder = TimeDistributed(sent_encoder)(doc_inputs)
doc_layer = Bidirectional(LSTM(hidden_dim))(doc_encoder)
# doc_layer_att = AttLayer(hidden_dim)(doc_layer)
output = Dense(num_classes, activation='softmax')(doc_layer)
model = Model(doc_inputs, output)
model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
model.summary()
return model
