Keras Model Output是float32而不是uint8 ......尽管数据标签是uint8

Question

I am training a model to predict a segmentation in medical images. In the training data, the input data is of type: numpy.float64 and the ground truth labels are of type: numpy.uint8. The problem is for some reason my model is producing an output type of numpy.float32.

Image showing: example of data types

# Defining the model
segmenter = Model(input_img, segmenter(input_img))

# Training the model (type of train_ground is numpy.uint8)
segmenter_train = segmenter.fit(train_X, train_ground, batch_size=batch_size,epochs=epochs,verbose=1,validation_data=(valid_X, valid_ground))

Model definition:

def segmenter(input_img):
    #encoder
    #input = 28 x 28 x 1 (wide and thin)
    conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(input_img) #28 x 28 x 32
    conv1 = BatchNormalization()(conv1)
    conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv1)
    conv1 = BatchNormalization()(conv1)
    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1) #14 x 14 x 32
    conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(pool1) #14 x 14 x 64
    conv2 = BatchNormalization()(conv2)
    conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv2)
    conv2 = BatchNormalization()(conv2)
    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2) #7 x 7 x 64
    conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(pool2) #7 x 7 x 128 (small and thick)
    conv3 = BatchNormalization()(conv3)
    conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv3)
    conv3 = BatchNormalization()(conv3)


    #decoder
    conv4 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv3) #7 x 7 x 128
    conv4 = BatchNormalization()(conv4)
    conv4 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv4)
    conv4 = BatchNormalization()(conv4)
    up1 = UpSampling2D((2,2))(conv4) # 14 x 14 x 128
    conv5 = Conv2D(32, (3, 3), activation='relu', padding='same')(up1) # 14 x 14 x 64
    conv5 = BatchNormalization()(conv5)
    conv5 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv5)
    conv5 = BatchNormalization()(conv5)
    up2 = UpSampling2D((2,2))(conv5) # 28 x 28 x 64

    conv6 = Conv2D(64, (3, 3), activation='relu', padding='same')(up2) #7 x 7 x 128
    conv6 = BatchNormalization()(conv6)
    conv6 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv6)
    conv6 = BatchNormalization()(conv6)
    up3 = UpSampling2D((2,2))(conv6) # 14 x 14 x 128

    conv7 = Conv2D(64, (3, 3), activation='relu', padding='same')(up3) #7 x 7 x 128
    conv7 = BatchNormalization()(conv7)
    conv7 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv7)
    conv7 = BatchNormalization()(conv7)
    up4 = UpSampling2D((2,2))(conv7) # 14 x 14 x 128

    decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(up4) # 28 x 28 x 1
    return decoded

Thanks in advance for help on this :)

Answer 1

Sigmoid returns a real number

The last layer happens to be the sigmoid activation function. It returns a real number from 0 and 1, not an integer.

Furthermore, it's important that the error metric, the difference between the correct answer and the calculated value, is continuous and not discrete, because that's differentiable and allows proper learning of the neural network weights with backpropagation.

Just convert and round

For training the network, just convert the truth labels to floating point values.

Once you've trained the network and want to use its outputs, just round them to convert them to integers - sigmoid activation is well suited for that.