def set_training_model(self):
"""
继承RNNOIE的网络构建函数，添加transformer层
"""
logging.debug("Setting TransformerLSTMOIE model")
# 输入层三个
word_inputs = Input(shape = (self.sent_maxlen,),dtype="int32",name = "word_inputs")
predicate_inputs = Input(shape = (self.sent_maxlen,),dtype="int32",name = "predicate_inputs")
postags_inputs = Input(shape = (self.sent_maxlen,),dtype="int32",name = "postags_inputs")
#dropout
dropout = lambda: Dropout(self.pred_dropout)
# 嵌入层
word_embedding_layer = self.embed_word()
pos_embedding_layer = self.embed_pos()
# 时序特征转换层
bilstm_layers = self.stack_latent_layers(1)
position_embedding = Position_Embedding()
# Transformer层
mulit_head_layers= self.stack_attention_layers(8,16,self.num_of_latent_layers)
self_attention = Attention(8,16)
# 全连接层
predict_layer = self.predict_classes()
# 构建
emb_output = concatenate([dropout()(word_embedding_layer(word_inputs)),dropout()(word_embedding_layer(predicate_inputs)),pos_embedding_layer(postags_inputs)])
# emb_output = concatenate([dropout()(word_embedding_layer(word_inputs)),pos_embedding_layer(postags_inputs)])
bilstm_output = dropout()(bilstm_layers(emb_output))
# transformer_output = self_attention([bilstm_output,bilstm_output,bilstm_output])
transformer_output = mulit_head_layers(bilstm_output)
# emb_output = concatenate([dropout(word_embedding_layer(word_inputs)),dropout(word_embedding_layer(predicate_inputs)),pos_embedding_layer(postags_inputs)])
# bilstm_output = dropout(bilstm_layers(dropout(word_embedding_layer(word_inputs))))
# conect_output = concatenate([bilstm_output,dropout(word_embedding_layer(predicate_inputs)),pos_embedding_layer(postags_inputs)])
# transformer_output = dropout(mulit_head_layers(conect_output))
# transformer_output = dropout(self_attention([bilstm_output,bilstm_output,bilstm_output]))
#pos_output = dropout(mulit_head_layers(position_embedding(emb_output)))
# output=predict_layer(concatenate([bilstm_output,pos_output,emb_output]))
output=predict_layer(BatchNormalization()(concatenate([dropout()(transformer_output),emb_output])))
# output=predict_layer(bilstm_output)
# Build model
self.model = Model(inputs = [word_inputs,predicate_inputs,postags_inputs], outputs = [output])
# Loss
self.model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['categorical_accuracy'])
self.model.summary()
# Save model json to file
self.save_model_to_file(os.path.join(self.model_dir, "model.json"))
epoch = 2000
batch_size = 32
head_num = 8
head_size = 16
# Model Architecture
# Text data
# define text input and shape
text_input = Input(shape=(30, ))
# word embedding
em_text = Embedding(len(gakki.word_dic) + 1,
200,
weights=[gakki.get_embed_matrix()],
trainable=True)(text_input)
x = Position_Embedding()(em_text)
x = Attention(head_num, head_size)([x, x, x])
x = BatchNormalization()(x)
x = Dropout(0.25)(x)
x = Attention(head_num, head_size)([x, x, x])
x = BatchNormalization()(x)
x = Dropout(0.25)(x)
x = Attention(head_num, head_size)([x, x, x])
x = BatchNormalization()(x)
x = Dropout(0.25)(x)
x = Attention(head_num, head_size)([x, x, x])
x = BatchNormalization()(x)
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 (sample 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)
K.clear_session()
callbacks = [TensorBoard("log/")]
S_inputs = Input(shape=(None, ), dtype='int32')
embeddings = Embedding(max_features, 128)(S_inputs)
embeddings = Position_Embedding()(embeddings)
O_seq = Attension(8, 16)([embeddings, embeddings, embeddings])
O_seq = GlobalAveragePooling1D()(O_seq)
O_seq = Dropout(0.5)(O_seq)
outputs = Dense(1, activation='sigmoid')(O_seq)
model = Model(inputs=S_inputs, outputs=outputs)
model.summary
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
model.fit(x_train,
y_train,
validation_data=(x_test, y_test),
epochs=50,
| np.any(np.isnan(train_label)) | np.any(np.isnan(train_label))):
print('Having NaN data!')
return train_label, train_audio, test_label, test_audio
num_class = 7
epoch = 20
batch_size = 32
head_num = 8
head_size = 16
# Model Architecture
# Audio feature vector
audio_input = Input(shape=(1200, 92))
x = Masking(mask_value=0.)(audio_input)
x = Position_Embedding()(audio_input)
x = Attention(head_num, head_size)([x, x, x])
x = BatchNormalization()(x)
x = Dropout(0.15)(x)
x = Attention(head_num, head_size)([x, x, x])
x = BatchNormalization()(x)
x = Dropout(0.15)(x)
#
#x = Attention(head_num, head_size)([x, x, x])
#x = BatchNormalization()(x)
#x = Dropout(0.15)(x)
#
#x = Attention(head_num, head_size)([x, x, x])
#x = BatchNormalization()(x)
gakki.unclear_lbl.append('Monitor Vital Signs')
gakki.auto_process(merge_unclear=True)
gakki.label_mode = 'lower_10'
num_class = 11
epoch = 20000
batch_size = 16
head_num = 8
head_size = 16
# Model Architecture
# Left audio feature vector
left_input = Input(shape=(602, 64))
#x = Masking(mask_value=0.)(left_input)
x = Position_Embedding()(left_input)
x = Attention(head_num, head_size)([x, x, x])
x = BatchNormalization()(x)
x = Dropout(0.15)(x)
x = Attention(head_num, head_size)([x, x, x])
x = BatchNormalization()(x)
x = Dropout(0.15)(x)
x = Attention(head_num, head_size)([x, x, x])
x = BatchNormalization()(x)
x = Dropout(0.15)(x)
x = Attention(head_num, head_size)([x, x, x])
x = BatchNormalization()(x)