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model.py
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from keras.models import Model
from keras.layers import Input, Dense, Embedding, concatenate
from keras.layers import CuDNNGRU, Bidirectional, GlobalAveragePooling1D, GlobalMaxPooling1D, Conv1D
from keras.layers import Add, BatchNormalization, Activation, CuDNNLSTM, Dropout
from keras.layers import *
from keras.models import *
from keras import backend as K
from keras.engine.topology import Layer, InputSpec
from keras import initializers
from keras.preprocessing import text, sequence
from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
import gc
from sklearn import metrics
maxlen = 70
max_features = 50000
embed_size = 300
def attention_3d_block(inputs): # inouts shape=[?, ? , 256]
# inputs.shape = (batch_size, time_steps, input_dim)
TIME_STEPS = inputs.shape[1].value # None
SINGLE_ATTENTION_VECTOR = False
input_dim = int(inputs.shape[2]) # 256
a = Permute((2, 1))(inputs) # shape=[?, 256, 70]
a = Reshape((input_dim, TIME_STEPS))(a) # this line is not useful. It's just to know which dimension is what.
a = Dense(TIME_STEPS, activation='softmax')(a)
if SINGLE_ATTENTION_VECTOR:
a = Lambda(lambda x: K.mean(x, axis=1))(a)
a = RepeatVector(input_dim)(a)
a_probs = Permute((2, 1))(a)
output_attention_mul = Multiply()([inputs, a_probs])
return output_attention_mul
class AttLayer(Layer):
def __init__(self, attention_dim):
self.init = initializers.get('normal')
self.supports_masking = True
self.attention_dim = attention_dim
super(AttLayer, self).__init__()
def build(self, input_shape):
assert len(input_shape) == 3
self.W = K.variable(self.init((input_shape[-1], self.attention_dim)))
self.b = K.variable(self.init((self.attention_dim, )))
self.u = K.variable(self.init((self.attention_dim, 1)))
self.trainable_weights = [self.W, self.b, self.u]
super(AttLayer, self).build(input_shape)
def compute_mask(self, inputs, mask=None):
return mask
def call(self, x, mask=None):
# size of x :[batch_size, sel_len, attention_dim]
# size of u :[batch_size, attention_dim]
# uit = tanh(xW+b)
uit = K.tanh(K.bias_add(K.dot(x, self.W), self.b))
ait = K.dot(uit, self.u)
ait = K.squeeze(ait, -1)
ait = K.exp(ait)
if mask is not None:
# Cast the mask to floatX to avoid float64 upcasting in theano
ait *= K.cast(mask, K.floatx())
ait /= K.cast(K.sum(ait, axis=1, keepdims=True) + K.epsilon(), K.floatx())
ait = K.expand_dims(ait)
weighted_input = x * ait
output = K.sum(weighted_input, axis=1)
return output
def compute_output_shape(self, input_shape):
return (input_shape[0], input_shape[-1])
def get_embedding_matrix(tokenizer):
EMBEDDING_FILE = '/home/pczero/embeddings/embeddings/glove.840B.300d/glove.840B.300d.txt'
def get_coefs(word,*arr):
return word, np.asarray(arr, dtype='float32')
embeddings_index = dict(get_coefs(*o.split(" ")) for o in open(EMBEDDING_FILE))
all_embs = np.stack(embeddings_index.values())
emb_mean= all_embs.mean() # 总体的平均值，一个数值
emb_std = all_embs.std()
embed_size = all_embs.shape[1]
word_index = tokenizer.word_index # X_train与X_test中所有单词的编号,编号按频率由高到低
nb_words = min(max_features, len(word_index))
embedding_matrix_1 = np.random.normal(emb_mean, emb_std, (nb_words, embed_size))
for word, i in word_index.items():
if i >= max_features:
continue
embedding_vector = embeddings_index.get(word)
if embedding_vector is not None:
embedding_matrix_1[i] = embedding_vector
del embeddings_index; gc.collect() # 两句结合清理内存
EMBEDDING_FILE = '/home/pczero/embeddings/embeddings/wiki-news-300d-1M/wiki-news-300d-1M.vec'
def get_coefs(word,*arr):
return word, np.asarray(arr, dtype='float32')
embeddings_index = dict(get_coefs(*o.split(" ")) for o in open(EMBEDDING_FILE) if len(o)>100)
all_embs = np.stack(embeddings_index.values())
emb_mean,emb_std = all_embs.mean(), all_embs.std()
embed_size = all_embs.shape[1]
word_index = tokenizer.word_index
nb_words = min(max_features, len(word_index))
embedding_matrix_2 = np.random.normal(emb_mean, emb_std, (nb_words, embed_size))
for word, i in word_index.items():
if i >= max_features: continue
embedding_vector = embeddings_index.get(word)
if embedding_vector is not None: embedding_matrix_2[i] = embedding_vector
del embeddings_index; gc.collect()
EMBEDDING_FILE = '/home/pczero/embeddings/embeddings/paragram_300_sl999/paragram_300_sl999.txt'
def get_coefs(word, *arr):
return word, np.asarray(arr, dtype='float32')
embeddings_index = dict(
get_coefs(*o.split(" ")) for o in open(EMBEDDING_FILE, encoding="utf8", errors='ignore') if len(o) > 100)
all_embs = np.stack(embeddings_index.values())
emb_mean, emb_std = all_embs.mean(), all_embs.std()
embed_size = all_embs.shape[1]
word_index = tokenizer.word_index
nb_words = min(max_features, len(word_index))
embedding_matrix_3 = np.random.normal(emb_mean, emb_std, (nb_words, embed_size))
for word, i in word_index.items():
if i >= max_features: continue
embedding_vector = embeddings_index.get(word)
if embedding_vector is not None: embedding_matrix_3[i] = embedding_vector
del embeddings_index; gc.collect()
embedding_matrix = np.concatenate((embedding_matrix_1, embedding_matrix_2, embedding_matrix_3), axis=1)
del embedding_matrix_1, embedding_matrix_2, embedding_matrix_3
gc.collect()
np.shape(embedding_matrix)
return embedding_matrix
def lstm_model(tokenizer):
inp = Input(shape=(maxlen,)) # [?, 70]
embed = Embedding(max_features, embed_size * 3, weights=[get_embedding_matrix(tokenizer)], trainable=False)(inp)
x = embed # [?, 70, 900]
# x = Bidirectional(CuDNNLSTM(128, return_sequences=True))(x) # 基于CuDNN的快速LSTM实现，只能在GPU上运行，只能使用tensoflow为后端
x = Bidirectional(GRU(128, unroll=True, return_sequences=True))(x) # 修改 x.shape=(?, 70, 256)
# 可选GRU，LSTM 注意：一定要使用unroll=True
x = attention_3d_block(x)
# x = Bidirectional(CuDNNLSTM(128, return_sequences=True))(x)
x = Bidirectional(GRU(128, unroll=True, return_sequences=True))(x) # 修改
x = AttLayer(64)(x)
x = Dropout(0.3)(x)
x = Dense(128, activation='relu')(x)
outp = Dense(1, activation="sigmoid")(x)
model = Model(inputs=inp, outputs=outp)
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
return model