embedding_dim,
input_length=max_sequence_length,
trainable=True)
inputs = Input(shape=(max_sequence_length, ), dtype='int32', name='input')
embeddings_sequences = embeddings_layer(inputs)
graphconv = GraphConv(filters=64,
neighbors_ix_mat=q_mat_layer1,
num_neighbors=12,
activation='relu')
selfattention = SeqSelfAttention(
attention_activation='relu')(embeddings_sequences)
ind_rnn_layer_1 = Bidirectional(
IndRNN(250,
recurrent_clip_min=-1,
recurrent_clip_max=-1,
dropout=0.0,
recurrent_dropout=0.0,
return_sequences=True))(selfattention)
output = Bidirectional(
IndRNN(250,
recurrent_clip_min=-1,
recurrent_clip_max=-1,
dropout=0.0,
recurrent_dropout=0.0,
return_sequences=False))(ind_rnn_layer_1)
print(output)
output = Dense(512, activation='sigmoid')(output)
output = Dense(256, activation='sigmoid')(output)
output = Dense(128, activation='sigmoid')(output)
saveBestModel = ModelCheckpoint(save_best_model_file,
monitor='val_loss',
verbose=0,
save_best_only=True,
save_weights_only=True)
embeddings_layer = Embedding(len(word_index) + 1,
embedding_dim,
input_length=max_sequence_length,
trainable=True)
inputs = Input(shape=(max_sequence_length, ), dtype='int32', name='input')
embeddings_sequences = embeddings_layer(inputs)
ind_rnn_layer_1 = IndRNN(250,
recurrent_clip_min=-1,
recurrent_clip_max=-1,
dropout=0.35,
recurrent_dropout=0.0,
return_sequences=True)(embeddings_sequences)
output = IndRNN(125,
recurrent_clip_min=-1,
recurrent_clip_max=-1,
dropout=0.35,
recurrent_dropout=0.0,
return_sequences=False)(ind_rnn_layer_1)
# output=Dense(256,activation='sigmoid')(ind_rnn_layer_1)
# output=Dense(128,activation='sigmoid')(output)
# output=Dense(64,activation='sigmoid')(output)
output = Dense(1, activation='sigmoid')(output)
model = Model(inputs=inputs, outputs=[output])
model.summary()
model.compile(loss='binary_crossentropy',
activation='relu')(conv1)
conv1 = MaxPooling1D()(conv1)
conv1 = BatchNormalization()(conv1)
texture = Dropout(0.3)(conv1)
print()
texture = Dense(100, activation='relu')(texture)
sequence_input4 = Input(shape=(max_sequence_length1, ), dtype='int32')
wo_ind = Embedding(len(word_index4) + 1,
embedding_dim,
input_length=max_sequence_length1,
trainable=True)(sequence_input4)
wo_ind = IndRNN(64,
recurrent_clip_min=-1,
recurrent_clip_max=-1,
dropout=0.0,
recurrent_dropout=0.0,
return_sequences=True)(wo_ind) #97%
wo_ind = Dense(100, activation='relu')(wo_ind)
k = 12
output = concatenate([texture, wo_ind])
print("output:", output)
"""
自注意力机制的加载
"""
from keras_self_attention import SeqSelfAttention
output = SeqSelfAttention(attention_activation='relu',
name='self_attention')(output)
conv1 = MaxPooling1D()(conv1)
conv1 = BatchNormalization()(conv1)
texture = Dropout(0.3)(conv1)
print()
texture = Dense(100, activation='relu')(texture)
sequence_input4 = Input(shape=(max_sequence_length1, ), dtype='int32')
wo_ind = Embedding(len(word_index4) + 1,
embedding_dim,
input_length=max_sequence_length1,
trainable=True)(sequence_input4)
wo_ind = Bidirectional(
IndRNN(32,
recurrent_clip_min=-1,
recurrent_clip_max=-1,
dropout=0.0,
recurrent_dropout=0.0,
return_sequences=True))(wo_ind) #97%
wo_ind = Bidirectional(
IndRNN(32,
recurrent_clip_min=-1,
recurrent_clip_max=-1,
dropout=0.0,
recurrent_dropout=0.0,
return_sequences=True))(wo_ind) #97%
wo_ind = Bidirectional(
IndRNN(32,
recurrent_clip_min=-1,
recurrent_clip_max=-1,
dropout=0.0,
recurrent_dropout=0.0,
embeddings_sequences = embeddings_layer(inputs)
graphconv = GraphConv(filters=64,
neighbors_ix_mat=q_mat_layer1,
num_neighbors=12,
activation='relu')
self_attention = SeqSelfAttention(attention_activation='relu',
name='self_attention')(embeddings_sequences)
conv1 = Conv1D(filters=128,
kernel_size=3,
strides=1,
padding='same',
activation='relu',
name='conv1')(self_attention)
ind_rnn_layer_1 = IndRNN(200,
recurrent_clip_min=-1,
recurrent_clip_max=-1,
dropout=0.7,
recurrent_dropout=0.0,
return_sequences=True)(conv1)
output = IndRNN(100,
recurrent_clip_min=-1,
recurrent_clip_max=-1,
dropout=0.7,
recurrent_dropout=0.0,
return_sequences=True)(ind_rnn_layer_1)
print(output)
dropout = Dropout(0.5)(output)
primary_caps = PrimaryCap(dropout,
dim_vector=dim_capsule1,
n_channels=3,
trainable=True)
inputs = Input(shape=(max_sequence_length, ), dtype='int32', name='input')
embeddings_sequences = embeddings_layer(inputs)
selfattention = SeqSelfAttention(
attention_activation='relu')(embeddings_sequences)
# ind_rnn_layer_1 = Bidirectional(IndRNN(250,
# recurrent_clip_min=-1,
# recurrent_clip_max=-1,
# dropout=0.0,
# recurrent_dropout=0.0,
# return_sequences=True))(selfattention)
output = Bidirectional(
IndRNN(125,
recurrent_clip_min=-1,
recurrent_clip_max=-1,
dropout=0.0,
recurrent_dropout=0.0,
return_sequences=False))(embeddings_sequences)
# ind_rnn_layer_1 = Bidirectional(IndRNN(250,)
print(output)
# output=Dense(128,activation='sigmoid')(output)
#
# output=Dense(64,activation='sigmoid')(output)
# output=Dense(32,activation='sigmoid')(output)
output = Dense(1, activation='sigmoid')(output)
model = Model(inputs=inputs, outputs=[output])
model.summary()
model.compile(loss='binary_crossentropy',
optimizer=Adam(0.0001),
nb_filter=nb_filter,
filter_length=filter_length,
border_mode='valid',
activation='relu',
subsample_length=1))
left_branch = Sequential()
left_branch.add(Embedding(len(word_index)+1,
EMBEDDING_DIM,
input_length=MAX_SEQUENCE_LENGTH
))
# left_branch.add(LSTM(250,return_sequences=False))
left_branch.add(IndRNN(250,
recurrent_clip_min=-1,
recurrent_clip_max=-1,
dropout=0.0,
recurrent_dropout=0.0,
return_sequences=True))
left_branch.add(IndRNN(250,
recurrent_clip_min=-1,
recurrent_clip_max=-1,
dropout=0.0,
recurrent_dropout=0.0,
return_sequences=False))
merged = Merge([left_branch,right_branch], mode='dot',output_shape=lambda x: x[0])
final_model = Sequential()
final_model.add(merged)
final_model.add(Dense(1))