text_classifier.py 文件源码

def createmodel(self):
        """
        create cnn model structure
        :return: model structure
        """
        max_features = max(self.words.values()) + 1 # input dims
        model = Sequential()
        if self.W is None:
            model.add(Embedding(max_features, self.embedding_length, input_length=self.maxlen, dropout=0.2))
        else:
            model.add(Embedding(max_features, self.layer1_size, weights=[self.W], input_length=self.maxlen, dropout=0.2))

        model.add(Convolution1D(nb_filter=self.nb_filter,
                                filter_length=self.filter_length,
                                border_mode='valid',
                                activation='relu',
                                subsample_length=1))

        model.add(MaxPooling1D(pool_length=model.output_shape[1]))
        model.add(Flatten())
        model.add(Dense(self.hidden_dims))
        model.add(Dropout(0.2))
        model.add(Activation('relu'))
        model.add(Dense(self.nb_classes))
        model.add(Activation('softmax'))
        model.compile(loss='categorical_crossentropy', optimizer='adadelta', metrics=["accuracy"])
        return model