我想训练一个深度学习模型，输入图像形状为（224,224,3）。我想把它们喂成一个u-net模型。
训练后我得到错误：检查目标时出错：预期conv2d_29有4个维度，但得到了有形状的数组（1255,12）
我很困惑因为我确定图像数组和标签没有问题。模型中的问题是什么？我该如何解决这个问题？
模型如下：
#def unet(pretrained_weights = None, input_size = (224,224,3)):
concat_axis = 3
input_size= Input((224,224,3))
conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(input_size)
conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
#flat1 = Flatten()(pool1)
conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool1)
conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool2)
conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool3)
conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv4)
drop4 = Dropout(0.5)(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)
conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool4)
conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv5)
drop5 = Dropout(0.5)(conv5)
up_conv5 = UpSampling2D(size=(2, 2), data_format="channels_last")(conv5)
ch, cw = get_crop_shape(conv4, up_conv5)
crop_conv4 = Cropping2D(cropping=(ch,cw), data_format="channels_last")(conv4)
up6 = concatenate([up_conv5, crop_conv4], axis=concat_axis)
conv6 = Conv2D(256, (3, 3), padding="same", activation="relu", kernel_initializer = 'he_normal')(up6)
conv6 = Conv2D(256, (3, 3), padding="same", activation="relu", kernel_initializer = 'he_normal')(conv6)
up_conv6 = UpSampling2D(size=(2, 2), data_format="channels_last")(conv6)
ch, cw = get_crop_shape(conv3, up_conv6)
crop_conv3 = Cropping2D(cropping=(ch,cw), data_format="channels_last")(conv3)
up7 = concatenate([up_conv6, crop_conv3], axis=concat_axis)
conv7 = Conv2D(128, (3, 3), padding="same", activation="relu", kernel_initializer = 'he_normal')(up7)
conv7 = Conv2D(128, (3, 3), padding="same", activation="relu", kernel_initializer = 'he_normal')(conv7)
up_conv7 = UpSampling2D(size=(2, 2), data_format="channels_last")(conv7)
ch, cw = get_crop_shape(conv2, up_conv7)
crop_conv2 = Cropping2D(cropping=(ch,cw), data_format="channels_last")(conv2)
up8 = concatenate([up_conv7, crop_conv2], axis=concat_axis)
conv8 = Conv2D(64, (3, 3), padding="same", activation="relu", kernel_initializer = 'he_normal')(up8)
conv8 = Conv2D(64, (3, 3), padding="same", activation="relu", kernel_initializer = 'he_normal')(conv8)
up_conv8 = UpSampling2D(size=(2, 2), data_format="channels_last")(conv8)
ch, cw = get_crop_shape(conv1, up_conv8)
crop_conv1 = Cropping2D(cropping=(ch,cw), data_format="channels_last")(conv1)
up9 = concatenate([up_conv8, crop_conv1], axis=concat_axis)
conv9 = Conv2D(32, (3, 3), padding="same", activation="relu", kernel_initializer = 'he_normal')(up9)
conv9 = Conv2D(32, (3, 3), padding="same", activation="relu", kernel_initializer = 'he_normal')(conv9)
model = Model(inputs = input_size, outputs = conv9)
由于模型输出的图层是conv层，因此输出形状有4个维度（Batch_size，width，height，channels）。但是你正在喂养一排形状（1255,12）。如果目标标签的形状为（Batch_size，num_features），则最后一层的输出应具有（None，12）或（Batch_size，12）形状。您有两种方法可以处理这种情况。
选择取决于您正在处理的问题。如果问题是分类，则可以使用选项1来添加softmax激活。使用选项1，对代码的修改将是，
conv9 = Conv2D(32, (3, 3), padding="same", activation="relu", kernel_initializer = 'he_normal')(conv9)
flatten1 = Flatten()(conv9)
dense1 = Dense(12, activation="softmax")(flatten1) # The choice of the activation depends on the problem you are dealing with.
model = Model(inputs = input_size, outputs = dense1)
使用选项2，修改将是
conv9 = Conv2D(32, (3, 3), padding="same", activation="relu", kernel_initializer = 'he_normal')(conv9)
reshape1 = Reshape((12,)(conv9) # The choice of the activation depends on the problem you are dealing with.
model = Model(inputs = input_size, outputs = reshape1)
N.B：当Reshape层用于将张量重塑为（None，12）形状时，请确保前一层的输出形状的乘积应该可被12整除。