convnet_model.py 文件源码

def create_model_4():
    inputs1 = Input((32, 32, 32, 1))
    inputs2 = Input((6,))

    #noise = GaussianNoise(sigma=0.1)(x)

    conv1 = Convolution3D(32, 3, 3, 3, activation='relu', border_mode='same')(inputs1)
    conv1 = SpatialDropout3D(0.1)(conv1)
    conv1 = Convolution3D(64, 3, 3, 3, activation='relu', border_mode='same')(conv1)
    pool1 = MaxPooling3D(pool_size=(2,2, 2))(conv1)

    conv2 = Convolution3D(128, 3, 3, 3, activation='relu', border_mode='same')(pool1)
    conv2 = SpatialDropout3D(0.1)(conv2)
    conv2 = Convolution3D(128, 3, 3, 3, activation='relu', border_mode='same')(conv2)
    pool2 = MaxPooling3D(pool_size=(2,2, 2))(conv2)

    x = Flatten()(pool2)
    x = merge([x, inputs2], mode='concat')
    x = Dense(64, init='normal')(x)
    x = Dropout(0.5)(x)
    predictions = Dense(1, init='normal', activation='sigmoid')(x)

    model = Model(input=[inputs1,inputs2], output=predictions)
    model.summary()
    optimizer = Adam()
    model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['binary_accuracy','precision','recall','mean_squared_error','accuracy'])

    return model