models.py 文件源码

def _generate_model(self, lembedding, num_classes=2, rnn_dim=32):

        WORD_PER_SENTENCES = lembedding.size_level1
        SENTENCES_PER_DOCUMENT = lembedding.size_level2
        EMBEDDING_DIM = lembedding.vector_box.vector_dim

        INPUT_SHAPE = (WORD_PER_SENTENCES * SENTENCES_PER_DOCUMENT, )
        EMBEDDING_SHAPE = (SENTENCES_PER_DOCUMENT, WORD_PER_SENTENCES, EMBEDDING_DIM)

        doc = Input(shape=(INPUT_SHAPE[0], ), dtype='int32')

        embedded = Sequential([
            Embedding(
                input_dim=lembedding.vector_box.size,
                output_dim=EMBEDDING_DIM,
                input_length=INPUT_SHAPE[0]
            ),
            Reshape(EMBEDDING_SHAPE)
        ])(doc)

        out = TimeDistributed(GRU(rnn_dim))(embedded)
        next = Dropout(0.5)(out)
        out = GRU(rnn_dim)(next)
        out = Dropout(0.5)(out)

        mapping = [
            Dense(64, activation='relu'),  # Maybe add more layers
        ]

        for f in mapping:
            out = f(out)

        if num_classes == 2:
            out = Dense(1, activation='sigmoid')(out)
            model = Model(input=doc, output=out)
            if self.optimizer is None:
                self.optimizer = 'rmsprop'
            model.compile(loss='binary_crossentropy', optimizer=self.optimizer, metrics=["accuracy"])
        else:
            out = Dense(num_classes, activation='softmax')(out)
            model = Model(input=doc, output=out)
            if self.optimizer is None:
                self.optimizer = 'adam'
            model.compile(loss='categorical_crossentropy', optimizer=self.optimizer, metrics=["accuracy"])

        return model