Question

我正在训练一个模型来预测医学图像中的分割。在训练数据中，输入数据的类型为：numpy.float64，地面真理标签的类型为：numpy.uint8。问题是由于某种原因我的模型生成的输出类型为numpy.float32。

# 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))

模型定义：

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

在此先感谢您的帮助：）

Answer 1

Sigmoid返回实数

最后一层恰好是S型激活功能。它返回从0到1的实数，而不是整数。

此外，重要的是，误差度量（正确答案与计算值之间的差异）必须是连续且非离散的，因为这是可区分的，并允许通过反向传播正确学习神经网络权重。

只需转换并舍入

要训练网络，只需将真值标签转换为浮点值即可。

一旦您训练了网络并想使用其输出，只需将它们舍入即可将它们转换为整数-S型激活非常适合于此。

Keras模型输出是float32而不是uint8 ...尽管数据标签是uint8

1 个答案:

Sigmoid返回实数

只需转换并舍入