milCNN.py 文件源码

def set_cnn_model(ninstance=4, input_dim = 4, input_length = 107):
    nbfilter = 16
    model = Sequential() # #seqs * seqlen * 4
    #model.add(brnn)
    model.add(Conv2D(input_shape=(ninstance, input_length, input_dim),
                            filters=nbfilter,
                            kernel_size=(1,10),
                            padding="valid",
                            #activation="relu",
                            strides=1))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(1,3))) # 32 16
    # model.add(Dropout(0.25)) # will be better
    model.add(Conv2D(filters=nbfilter*2, kernel_size=(1,32), padding='valid', activation='relu', strides=1))
    # model.add(Flatten())
    #model.add(Softmax4D(axis=1))

    #model.add(MaxPooling1D(pool_length=3))
    #model.add(Flatten())
    #model.add(Recalc(axis=1))
    # model.add(Flatten())
    # model.add(Dense(nbfilter*2, activation='relu'))
    model.add(Dropout(0.25))
    model.add(Conv2D(filters=1, kernel_size=(1,1), padding='valid', activation='sigmoid', strides=1))
    return model