卷积神经网络，以RGB图像作为输入，每像素输出10个元素的矢量

Question

我有一个CNN的Keras实现，特别是一个UNet，我通过提供一个256x256x3（RGB）视网膜图像和一个相同大小的图像掩模作为输入来训练：

面具是我的基本真理。掩模中的每个像素是10种独特颜色（白色、黑色、蓝色等）中的一种，映射到原始视网膜图像中10个生物层中的一个位置。你知道吗

UNet输出为256x256x3图像，其中每个像素的颜色值应与图像遮罩中的相应颜色相同。然而，我想要的输出是一个256x256x10的数组，其中每个像素拥有占据该像素位置的10种颜色中的一种的概率（0.0到1.0）。你知道吗

以下是我的Unet代码：

# --------------------------------------------------------------------------------------
# CONV 2D BLOCK
# --------------------------------------------------------------------------------------
def conv2d_block(input_tensor, n_filters, kernel_size = 3, batchnorm = True):
    """Function to add 2 convolutional layers with the parameters passed to it"""

    # first layer
    x = Conv2D(filters = n_filters, kernel_size = kernel_size, data_format="channels_last", \
              kernel_initializer = 'he_normal', padding = 'same')(input_tensor)

    if batchnorm:
        x = BatchNormalization()(x)

    x = Activation('relu')(x)

    # second layer
    x = Conv2D(filters = n_filters, kernel_size = kernel_size, data_format="channels_last", \
              kernel_initializer = 'he_normal', padding = 'same')(input_tensor)

    if batchnorm:
        x = BatchNormalization()(x)

    x = Activation('relu')(x)

    return x

# --------------------------------------------------------------------------------------
# GET THE U-NET ARCHITECTURE 
# --------------------------------------------------------------------------------------
def get_unet(input_img, n_filters = 16, dropout = 0.1, batchnorm = True):

    # Contracting Path (256 x 256 x 3)
    c1 = conv2d_block(input_img, n_filters * 1, kernel_size = (3, 3), batchnorm = batchnorm)
    p1 = MaxPooling2D((2, 2))(c1)
    p1 = Dropout(dropout)(p1)

    c2 = conv2d_block(p1, n_filters * 2, kernel_size = (3, 3), batchnorm = batchnorm)
    p2 = MaxPooling2D((2, 2))(c2)
    p2 = Dropout(dropout)(p2)

    c3 = conv2d_block(p2, n_filters * 4, kernel_size = (3, 3), batchnorm = batchnorm)
    p3 = MaxPooling2D((2, 2))(c3)
    p3 = Dropout(dropout)(p3)

    c4 = conv2d_block(p3, n_filters * 8, kernel_size = (3, 3), batchnorm = batchnorm)
    p4 = MaxPooling2D((2, 2))(c4)
    p4 = Dropout(dropout)(p4)

    c5 = conv2d_block(p4, n_filters = n_filters * 16, kernel_size = (3, 3), batchnorm = batchnorm)

    # Expansive Path
    u6 = Conv2DTranspose(n_filters * 8, 3, strides = (2, 2), padding = 'same')(c5)
    u6 = concatenate([u6, c4])
    u6 = Dropout(dropout)(u6)
    c6 = conv2d_block(u6, n_filters * 8, kernel_size = 3, batchnorm = batchnorm)

    u7 = Conv2DTranspose(n_filters * 4, 3, strides = (2, 2), padding = 'same')(c6)
    u7 = concatenate([u7, c3])
    u7 = Dropout(dropout)(u7)
    c7 = conv2d_block(u7, n_filters * 4, kernel_size = 3, batchnorm = batchnorm)

    u8 = Conv2DTranspose(n_filters * 2, 3, strides = (2, 2), padding = 'same')(c7)
    u8 = concatenate([u8, c2])
    u8 = Dropout(dropout)(u8)
    c8 = conv2d_block(u8, n_filters * 2, kernel_size = 3, batchnorm = batchnorm)

    u9 = Conv2DTranspose(n_filters * 1, 3, strides = (2, 2), padding = 'same')(c8)    
    u9 = concatenate([u9, c1])
    u9 = Dropout(dropout)(u9)
    c9 = conv2d_block(u9, n_filters * 1, kernel_size = 3, batchnorm = batchnorm)

    outputs = Conv2D(3, 1, activation='sigmoid')(c9)    

    model = Model(inputs=[input_img], outputs=[outputs])
    return model

我的问题是，如何改变网络的设计，使其采用相同的输入，但对相应输入图像和掩模的每个像素产生256x256x10的预测？你知道吗

Answer 1

outputs = Conv2D(10, 1, activation='softmax')(c9)

outputs是形状为[?,256,256,10]的张量，softmax激活沿着最后一个轴(axis=-1)。你知道吗