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TextCNNmodel.py
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TextCNNmodel.py
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# -*- coding: utf-8 -*-
from keras import Input, Model
from keras.layers import Embedding, Dense, Conv1D, GlobalMaxPooling1D, Concatenate, Dropout
from keras.callbacks import EarlyStopping
import logging
def train(x_train, y_train, x_test, y_test, maxlen, max_token, embedding_matrix, embedding_dims, batch_size, epochs,
logpath, modelpath, modelname):
print(modelname + 'Build model...')
sentence = Input((maxlen,))
embedding_layer = Embedding(max_token + 1,
embedding_dims,
input_length=maxlen,
weights=[embedding_matrix])
sentence_embedding = embedding_layer(sentence)
c2 = Conv1D(128, 3, activation='relu')(sentence_embedding)
p2 = GlobalMaxPooling1D()(c2)
c3 = Conv1D(128, 4, activation='relu')(sentence_embedding)
p3 = GlobalMaxPooling1D()(c3)
c4 = Conv1D(128, 5, activation='relu')(sentence_embedding)
p4 = GlobalMaxPooling1D()(c4)
x = Concatenate()([p2, p3, p4])
output = Dense(1, activation="sigmoid")(x)
model = Model(inputs=sentence, outputs=output)
model.compile(optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"])
# print(model.summary())
# patience经过几个epoch后loss不在变化停止训练
early_stopping = EarlyStopping(monitor='val_loss', patience=2)
# model.fit(X, y, validation_split=0.2, callbacks=[early_stopping])
print('Train...')
hist = model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
validation_data=(x_test, y_test), callbacks=[early_stopping])
# print(hist.history)
##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')
if __name__ == '__main__':
print('11')