textcnn模型实践

对应的tensorflow版本：2.5.0+

textcnn模型如下：

import tensorflow as tfclass ConvMaxPooling1d(tf.keras.layers.Layer):def __init__(self, filters, kernel):super(ConvMaxPooling1d, self).__init__()self.kernel_size = kernel#(batch_size, step, embedding_size)->(batch_size,step-kernel_size+1,filter_size)self.conv = tf.keras.layers.Conv1D(filters=filters, kernel_size=kernel, activation='relu')# (batch_size,step-kernel_size+1,filter_size)->(batch_size,filter_size)self.pool = tf.keras.layers.GlobalMaxPool1D()tf.random.uniform()def call(self, inputs, masks=None):conv_out = self.conv(inputs)pool_out = self.pool(conv_out)return pool_outclass TextCNN(tf.keras.models.Model):def __init__(self, vocab, embedding_size, hidden_size, filters_list=[50 ,60, 70, 80], kernels=[2,3, 4, 5],dropout=0.5, sentence_length=20):super(TextCNN, self).__init__()ind = tf.feature_column.categorical_column_with_vocabulary_file("sentence_vocab", vocabulary_file=vocab,default_value=0)self.embedding_size = embedding_sizeself.sentence_length = sentence_lengthself.dense_feature_layer = tf.keras.layers.DenseFeatures([tf.feature_column.embedding_column(ind, dimension=embedding_size)])self.conv_maxs = [ConvMaxPooling1d(f, k) for f, k in zip(filters_list, kernels)]self.dropout = tf.keras.layers.Dropout(dropout)self.dense = tf.keras.layers.Dense(hidden_size, activation='relu')self.classifier = tf.keras.layers.Dense(1, activation='sigmoid')# @tf.function(input_signature=(tf.TensorSpec(shape=(None, None), dtype=tf.dtypes.string),))def call(self, inputs):# ***************word token embedding begin***************inputs = tf.convert_to_tensor(inputs)inputs_tensor = tf.reshape(inputs, (-1, 1))embed_word_vectors1 = self.dense_feature_layer({"sentence_vocab": inputs_tensor})embeddings = tf.reshape(embed_word_vectors1, (-1, self.sentence_length, self.embedding_size))# ***************word token embedding end***************#对于每一个layer来说，输入是：(batch_size,step,embedding_size)->(batch_size,step-kernel_size+1,filter_size)conv_outs = [layer(embeddings, None) for layer in self.conv_maxs]# 对于每一个layer来说，输入是：[(batch_size,step-kernel_size+1,filter_size)]->(batch_size,step-kernel_size+1,sum(filter_size))concat_out = tf.concat(conv_outs, axis=-1)dense_out = self.dense(concat_out)drop_out = self.dropout(dense_out)logits = self.classifier(drop_out)return logits

模型训练代码如下：

import tensorflow as tffrom model.TextCNN import TextCNN
from utils.path_utils import get_full_path
from utils.read_batch_data import get_dataclass SingleRNNModelTest:def __init__(self,epoch=3,batch_size=100,embedding_size=256,learning_rate=0.001,model_path="model_version",sentence_vocb_length=20,fill_vocab='TTTTTT',vocab_file_path="data/vocab_clean.txt"):self.optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)self.loss = tf.keras.losses.BinaryCrossentropy(reduction=tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE)self.epoch = epochself.batch_size = batch_sizeself.model_path = model_pathself.sentence_vocb_length = sentence_vocb_lengthself.fill_vocab = fill_vocabself.model = TextCNN(vocab=get_full_path(vocab_file_path), embedding_size=embedding_size, hidden_size=20,sentence_length=sentence_vocb_length)self.summary_writer = tf.summary.create_file_writer('./tensorboard/news_label_model/{}'.format(model_path))def train(self):# ========================== Create dataset =======================train_x,train_y = get_data("data/train_data/prepare/train_data_v2.txt", self.sentence_vocb_length, self.fill_vocab)self.model(train_x)board = tf.keras.callbacks.TensorBoard(log_dir=get_full_path("data/fit_log/graph"), write_graph=True)model_save = tf.keras.callbacks.ModelCheckpoint(get_full_path("data/fit_log/fit_model3"), monitor="val_loss",mode="min")self.model.compile(optimizer=self.optimizer, loss=self.loss,metrics=[tf.keras.metrics.BinaryAccuracy(threshold=0.5),tf.keras.metrics.AUC(curve='PR', name='p-r'),tf.keras.metrics.AUC(curve='ROC', name='ROC'),])self.model.fit(x=train_x,y=train_y,batch_size=self.batch_size, epochs=self.epoch,shuffle=True,callbacks=[board, model_save])if __name__ == '__main__':# =============================== GPU ==============================gpu = tf.config.experimental.list_physical_devices(device_type='GPU')print("gpu message:{}".format(gpu))# If you have GPU, and the value is GPU serial number.import osos.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"os.environ["CUDA_VISIBLE_DEVICES"] = "0"epoch = 20batch_size = 1000sentence_vocb_length = 25embedding_size = 216learning_rate = 0.001train_instance = SingleRNNModelTest(epoch=epoch, batch_size=batch_size, sentence_vocb_length=sentence_vocb_length,embedding_size=embedding_size, learning_rate=learning_rate)train_instance.train()

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textcnn模型实践

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