Sentiment.py 文件源码

def baseModel(self, nb_filter=250, filter_length=3, hidden_dims=125):
        model = Sequential()

        # we start off with an efficient embedding layer which maps
        # our vocab indices into embedding_dims dimensions
        model.add(Embedding(self.max_words + self.index_from,self.embedding_dims,
                            input_length=self.max_length))
        model.add(Dropout(0.25))

        # we add a Convolution1D, which will learn nb_filter
        # word group filters of size filter_length:

        # filter_length is like filter size, subsample_length is like step in 2D CNN.
        model.add(Convolution1D(filters=nb_filter,
                                kernel_size=filter_length,
                                padding='valid',
                                activation='relu',
                                strides=1))
        # we use standard max pooling (halving the output of the previous layer):
        model.add(MaxPooling1D(pool_size=2))

        # We flatten the output of the conv layer,
        # so that we can add a vanilla dense layer:
        model.add(Flatten())

        # We add a vanilla hidden layer:
        model.add(Dense(hidden_dims))
        model.add(Dropout(0.25))
        model.add(Activation('relu'))

        # We project onto a single unit output layer, and squash it with a sigmoid:
        model.add(Dense(1))
        model.add(Activation('sigmoid'))

        model.compile(loss='binary_crossentropy',
                      optimizer='rmsprop')

        return model