Python 在构建和训练3D Keras U-NET时获取值错误

在使用keras为3D Unet构建的模型进行训练时，我得到了值错误：层conv3d_46的输入0与层不兼容：预期ndim=5，发现ndim=6。收到完整形状：[无，2，256，256，120，4]。我的数据的形状大小为（2、256、256、120、4）。

型号：

data = Input(shape=inp_shape)
flt=32


conv1 = Conv3D(flt, (3, 3, 3), activation='relu', padding='same')(data)
conv1 = Conv3D(flt, (3, 3, 3), activation='relu', padding='same')(conv1)
pool1 = MaxPooling3D(pool_size=(2, 2, 2))(conv1)

conv2 = Conv3D(flt*2, (3, 3, 3), activation='relu', padding='same')(pool1)
conv2 = Conv3D(flt*2, (3, 3, 3), activation='relu', padding='same')(conv2)
pool2 = MaxPooling3D(pool_size=(2, 2, 2))(conv2)

conv3 = Conv3D(flt*4, (3, 3, 3), activation='relu', padding='same')(pool2)
conv3 = Conv3D(flt*4, (3, 3, 3), activation='relu', padding='same')(conv3)
pool3 = MaxPooling3D(pool_size=(2, 2, 2))(conv3)

conv4 = Conv3D(flt*8, (3, 3, 3), activation='relu', padding='same')(pool3)
conv4 = Conv3D(flt*8, (3, 3, 3), activation='relu', padding='same')(conv4)
pool4 = MaxPooling3D(pool_size=(2, 2, 2))(conv4)

conv5 = Conv3D(flt*16, (3, 3, 3), activation='relu', padding='same')(pool4)
conv5 = Conv3D(flt*8, (3, 3, 3), activation='relu', padding='same')(conv5)

up6 = concatenate([Conv3DTranspose(flt*8, (2, 2, 2), strides=(2, 2, 2), padding='same')(conv5), conv4], axis=-1)
conv6 = Conv3D(flt*8, (3, 3, 3), activation='relu', padding='same')(up6)
conv6 = Conv3D(flt*4, (3, 3, 3), activation='relu', padding='same')(conv6)

up7 = concatenate([Conv3DTranspose(flt*4, (2, 2, 2), strides=(2, 2, 2), padding='same')(conv6), conv3], axis=-1)
conv7 = Conv3D(flt*4, (3, 3, 3), activation='relu', padding='same')(up7)
conv7 = Conv3D(flt*2, (3, 3, 3), activation='relu', padding='same')(conv7)

up8 = concatenate([Conv3DTranspose(flt*2, (2, 2, 2), strides=(2, 2, 2), padding='same')(conv7), conv2], axis=4)
conv8 = Conv3D(flt*2, (3, 3, 3), activation='relu', padding='same')(up8)
conv8 = Conv3D(flt, (3, 3, 3), activation='relu', padding='same')(conv8)

up9 = concatenate([Conv3DTranspose(flt, (2, 2, 2), strides=(2, 2, 2), padding='same')(conv8), conv1], axis=4)
conv9 = Conv3D(flt, (3, 3, 3), activation='relu', padding='same')(up9)
conv9 = Conv3D(flt, (3, 3, 3), activation='relu', padding='same')(conv9)


conv10 = Conv3D(2, (1,1,1), activation='sigmoid')(conv9)

model = Model(inputs=[data], outputs=[conv10])

培训模型的代码如下所示：-

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['binary_accuracy'])

---

目标标签的最后一个尺寸标注为2。模型的输出的最后一个维度为1。感谢@Shubham Panchal

目标标签的最后尺寸为2。模型的输出的最后一个维度为1。感谢@Shubham Panchal

我们需要一个损失函数

二进制交叉熵

用于UNet和乙状结肠激活

categorical\u crossentropy

用于多类分类。我更改了，但仍然得到相同的错误。我认为这个模型有问题。我不明白。@ShubhamPanchal任务是执行分段目标标签的最后一个维度为2。模型的输出的最后一个维度为1。也许目标标签是一个热编码的？@ShubhamPanchal噢，让我来编辑一下test@ShubhamPanchal感谢更改为二维后，模型运行。我们需要一个损失函数

binary\u crossentropy

用于UNet和sigmoid激活

categorical\u crossentropy

用于多类分类。我更改了，但仍然得到相同的错误。我认为这个模型有问题。我不明白。@ShubhamPanchal任务是执行分段目标标签的最后一个维度为2。模型的输出的最后一个维度为1。也许目标标签是一个热编码的？@ShubhamPanchal噢，让我来编辑一下test@ShubhamPanchal感谢更改为二维后，模型将运行。