chrom_hmm_cnn.py 文件源码

def build_small_chrom_label(args):
    model = Sequential()
    model.add(Convolution1D(input_dim=len(args.inputs), 
        input_length=args.window_size, 
        nb_filter=40,
        filter_length=16,
        border_mode='valid',
        activation="relu",
        init='normal'))

    model.add(MaxPooling1D(pool_length=3, stride=3))
    model.add(Convolution1D(nb_filter=64, filter_length=16, activation="relu", init='normal', border_mode='valid'))
    model.add(Dropout(0.2)) 
    model.add(MaxPooling1D(pool_length=3, stride=3))
    model.add(Flatten())

    model.add(Dense(output_dim=32, init='normal'))
    model.add(Activation('relu'))

    model.add( Dense(output_dim=len(args.labels), init='normal') )
    model.add( Activation('softmax'))

    sgd = SGD(lr=0.001, decay=1e-6, momentum=0.9, nesterov=True, clipnorm=0.5)
    adamo = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, clipnorm=1.)
    classes = args.labels.keys()
    my_metrics = [metrics.categorical_accuracy, precision, recall ]

    model.compile(loss='categorical_crossentropy', optimizer=adamo, metrics=my_metrics)
    print('model summary:\n', model.summary())

    return model