convNet.py 文件源码

def build_cnn_char_threeModels(input_dim, output_dim,nb_filter,filter_size=3):
    left = Sequential()
    left.add(Embedding(input_dim,
             32, # character embedding size
             input_length=L,
             dropout=0.2))
    left.add(Convolution1D(nb_filter=nb_filter,
                          filter_length=filter_size,border_mode="valid",activation="relu",subsample_length=1))
    left.add(GlobalMaxPooling1D())
    left.add(Dense(100))
    left.add(Dropout(0.2))
    left.add(Activation("tanh"))

    center = Sequential()
    center.add(Embedding(input_dim,
             32, # character embedding size
             input_length=M,
             dropout=0.2))
    center.add(Convolution1D(nb_filter=nb_filter,
                          filter_length=filter_size,border_mode="valid",activation="relu",subsample_length=1))
    center.add(GlobalMaxPooling1D())
    center.add(Dense(100))
    center.add(Dropout(0.2))
    center.add(Activation("tanh"))

    right = Sequential()
    right.add(Embedding(input_dim,
             32, # character embedding size
             input_length=R,
             dropout=0.2))
    right.add(Convolution1D(nb_filter=nb_filter,
                          filter_length=filter_size,border_mode="valid",activation="relu",subsample_length=1))
    right.add(GlobalMaxPooling1D())
    right.add(Dense(100))
    right.add(Dropout(0.2))
    right.add(Activation("tanh"))

    clf = Sequential()
    clf.add(Merge([left,center,right],mode="concat"))
    clf.add(Dense(output_dim=output_dim, activation='softmax'))

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