MyModel.py

# -*- coding: utf-8 -*-
"""
1、自定义模型 Conv-BiGRU 卷积和循环并行
2、自定义模型 卷积和循环串行
"""
from keras.layers import Dense, Input, Flatten,concatenate
from keras.layers import Conv1D, MaxPooling1D, Embedding, Dropout, LSTM, GRU, Bidirectional
from keras.models import Model
import logging
from keras.callbacks import EarlyStopping

def train(x_train, y_train, x_test, y_test,maxlen,max_token,embedding_matrix,embedding_dims,batch_size,epochs,logpath,modelpath,modelname):
    sentence = Input(shape=(None,), dtype="int32")
    embedding_layer = Embedding(max_token + 1,
                                embedding_dims,
                                input_length=maxlen,
                                weights=[embedding_matrix],
                                trainable=False)
    sentence_embedding = embedding_layer(sentence)
    c2 = Conv1D(2, 2, activation='relu')(sentence_embedding)
    p2 = MaxPooling1D(27)(c2)
    p2 = Flatten()(p2)

    c3 = Conv1D(2, 3, activation='relu')(sentence_embedding)
    p3 = MaxPooling1D(26)(c3)
    p3 = Flatten()(p3)

    c4 = Conv1D(2, 4, activation='relu')(sentence_embedding)
    p4 = MaxPooling1D(25)(c4)
    p4 = Flatten()(p4)

    g1 = Bidirectional(GRU(128))(sentence_embedding)

    x = concatenate([p2, p3, p4, g1])
    output = Dense(1, activation="sigmoid")(x)
    model = Model(inputs=sentence, outputs=output)
    model.compile(optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"])

    # patience经过几个epoch后loss不在变化停止训练
    early_stopping = EarlyStopping(monitor='val_loss', patience=2)
    hist = model.fit(x_train, y_train,
                     batch_size=batch_size,
                     epochs=epochs,
                     validation_data=(x_test, y_test), callbacks=[early_stopping])
    ##输出loss与acc到日志文件
    log_format = "%(asctime)s - %(message)s"
    logging.basicConfig(filename=logpath, level=logging.DEBUG, format=log_format)
    logging.warning(modelname)
    for i in range(len(hist.history["acc"])):
        strlog=str(i+1)+" Epoch "+"-loss: "+str(hist.history["loss"][i])+" -acc: "+str(hist.history["acc"][i])+" -val_loss: "+str(hist.history["val_loss"][i])+" -val_acc: "+str(hist.history["val_acc"][i])
        logging.warning(strlog)

    model.save(modelpath + modelname + '.h5')