models.py 文件源码

def _generate_model(self, lembedding, num_classes=2, ngrams=[1,2,3,4,5],
                        nfilters=64, train_vectors=True):

        def sub_ngram(n):
            return Sequential([
                Convolution1D(nfilters, n,
                      activation='relu',
                      input_shape=(lembedding.size, lembedding.vector_box.vector_dim)),
                Lambda(
                    lambda x: K.max(x, axis=1),
                    output_shape=(nfilters,)
                )
        ])

        doc = Input(shape=(lembedding.size, ), dtype='int32')
        embedded = Embedding(input_dim=lembedding.vector_box.size,
                             output_dim=lembedding.vector_box.vector_dim,
                             weights=[lembedding.vector_box.W])(doc)
        embedded.trainable = train_vectors

        rep = Dropout(0.5)(
            merge(
                [sub_ngram(n)(embedded) for n in ngrams],
                mode='concat',
                concat_axis=-1
            )
        )

        if num_classes == 2:
            out = Dense(1, activation='sigmoid')(rep)
            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')(rep)
            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