convNet.py 文件源码

def build_cnn_char_complex(input_dim, output_dim,nb_filter):
    randomEmbeddingLayer = Embedding(input_dim,32, input_length=maxlen,dropout=0.1)
    poolingLayer = Lambda(max_1d, output_shape=(nb_filter,))
    conv_filters = []
    for n_gram in range(2,4):
        ngramModel = Sequential()
        ngramModel.add(randomEmbeddingLayer)
        ngramModel.add(Convolution1D(nb_filter=nb_filter,
                                     filter_length=n_gram,
                                     border_mode="valid",
                                     activation="relu",
                                     subsample_length=1))
        ngramModel.add(poolingLayer)
        conv_filters.append(ngramModel)

    clf = Sequential()
    clf.add(Merge(conv_filters,mode="concat"))
    clf.add(Activation("relu"))
    clf.add(Dense(100))
    clf.add(Dropout(0.1))
    clf.add(Activation("tanh"))
    clf.add(Dense(output_dim=output_dim, activation='softmax'))

    clf.compile(optimizer='adagrad',
                     loss='categorical_crossentropy',
                     metrics=['accuracy'])
    return clf