- 背景**
我是Python和机器学习的新手。我刚刚尝试用我在互联网上找到的代码建立一个UNet，并想让它适应我正在处理的情况。当尝试将UNet .fit到训练数据时，我收到了以下错误：
InvalidArgumentError: required broadcastable shapes at loc(unknown)
[[node Equal (defined at <ipython-input-68-f1422c6f17bb>:1) ]] [Op:__inference_train_function_3847]当我搜索它的时候，我得到了很多结果，但大多是不同的错误。
这意味着什么？更重要的是，我该如何修复它？
- 导致错误的代码**
此错误的上下文如下所示：我想分割图像并标记不同的类。我为训练、测试和验证数据设置了目录"trn"、"tst"和"val"。dir_dat()函数应用os.path.join()来获取相应data set的完整路径。3个文件夹中的每个文件夹都有用整数标记的每个类的子目录。在每个文件夹中，对于各个类存在一些.tif图像。
我定义了以下图像数据生成器（训练数据是稀疏的，因此需要增强）：
classes = np.array([ 0, 2, 4, 6, 8, 11, 16, 21, 29, 30, 38, 39, 51])
bs = 15 # batch size
augGen = ks.preprocessing.image.ImageDataGenerator(rotation_range = 365,
width_shift_range = 0.05,
height_shift_range = 0.05,
horizontal_flip = True,
vertical_flip = True,
fill_mode = "nearest") \
.flow_from_directory(directory = dir_dat("trn"),
classes = [str(x) for x in classes.tolist()],
class_mode = "categorical",
batch_size = bs, seed = 42)
tst_batches = ks.preprocessing.image.ImageDataGenerator() \
.flow_from_directory(directory = dir_dat("tst"),
classes = [str(x) for x in classes.tolist()],
class_mode = "categorical",
batch_size = bs, shuffle = False)
val_batches = ks.preprocessing.image.ImageDataGenerator() \
.flow_from_directory(directory = dir_dat("val"),
classes = [str(x) for x in classes.tolist()],
class_mode = "categorical",
batch_size = bs)然后我基于this example设置了UNet，在这里，我修改了一些参数以使UNet适应这种情况（多个类），即最后一层的激活和损失函数：
layer_in = ks.layers.Input(shape = (imgr, imgc, imgdim))
# convert pixel integer values to float
inVals = ks.layers.Lambda(lambda x: x / 255)(layer_in)
# Contraction path
c1 = ks.layers.Conv2D(16, (3, 3), activation = "relu",
kernel_initializer = "he_normal", padding = "same")(inVals)
c1 = ks.layers.Dropout(0.1)(c1)
c1 = ks.layers.Conv2D(16, (3, 3), activation = "relu",
kernel_initializer = "he_normal", padding = "same")(c1)
p1 = ks.layers.MaxPooling2D((2, 2))(c1)
c2 = ks.layers.Conv2D(32, (3, 3), activation = "relu",
kernel_initializer = "he_normal", padding = "same")(p1)
c2 = ks.layers.Dropout(0.1)(c2)
c2 = ks.layers.Conv2D(32, (3, 3), activation = "relu",
kernel_initializer = "he_normal", padding = "same")(c2)
p2 = ks.layers.MaxPooling2D((2, 2))(c2)
c3 = ks.layers.Conv2D(64, (3, 3), activation = "relu",
kernel_initializer = "he_normal", padding = "same")(p2)
c3 = ks.layers.Dropout(0.2)(c3)
c3 = ks.layers.Conv2D(64, (3, 3), activation = "relu",
kernel_initializer = "he_normal", padding = "same")(c3)
p3 = ks.layers.MaxPooling2D((2, 2))(c3)
c4 = ks.layers.Conv2D(128, (3, 3), activation = "relu",
kernel_initializer = "he_normal", padding = "same")(p3)
c4 = ks.layers.Dropout(0.2)(c4)
c4 = ks.layers.Conv2D(128, (3, 3), activation = "relu",
kernel_initializer = "he_normal", padding = "same")(c4)
p4 = ks.layers.MaxPooling2D(pool_size = (2, 2))(c4)
c5 = ks.layers.Conv2D(256, (3, 3), activation = "relu",
kernel_initializer = "he_normal", padding = "same")(p4)
c5 = ks.layers.Dropout(0.3)(c5)
c5 = ks.layers.Conv2D(256, (3, 3), activation = "relu",
kernel_initializer = "he_normal", padding = "same")(c5)
# Expansive path
u6 = ks.layers.Conv2DTranspose(128, (2, 2), strides = (2, 2), padding = "same")(c5)
u6 = ks.layers.concatenate([u6, c4])
c6 = ks.layers.Conv2D(128, (3, 3), activation = "relu",
kernel_initializer = "he_normal", padding = "same")(u6)
c6 = ks.layers.Dropout(0.2)(c6)
c6 = ks.layers.Conv2D(128, (3, 3), activation = "relu",
kernel_initializer = "he_normal", padding = "same")(c6)
u7 = ks.layers.Conv2DTranspose(64, (2, 2), strides = (2, 2), padding = "same")(c6)
u7 = ks.layers.concatenate([u7, c3])
c7 = ks.layers.Conv2D(64, (3, 3), activation = "relu",
kernel_initializer = "he_normal", padding = "same")(u7)
c7 = ks.layers.Dropout(0.2)(c7)
c7 = ks.layers.Conv2D(64, (3, 3), activation = "relu",
kernel_initializer = "he_normal", padding = "same")(c7)
u8 = ks.layers.Conv2DTranspose(32, (2, 2), strides = (2, 2), padding = "same")(c7)
u8 = ks.layers.concatenate([u8, c2])
c8 = ks.layers.Conv2D(32, (3, 3), activation = "relu",
kernel_initializer = "he_normal", padding = "same")(u8)
c8 = ks.layers.Dropout(0.1)(c8)
c8 = ks.layers.Conv2D(32, (3, 3), activation = "relu",
kernel_initializer = "he_normal", padding = "same")(c8)
u9 = ks.layers.Conv2DTranspose(16, (2, 2), strides = (2, 2), padding = "same")(c8)
u9 = ks.layers.concatenate([u9, c1], axis = 3)
c9 = ks.layers.Conv2D(16, (3, 3), activation = "relu",
kernel_initializer = "he_normal", padding = "same")(u9)
c9 = ks.layers.Dropout(0.1)(c9)
c9 = ks.layers.Conv2D(16, (3, 3), activation = "relu",
kernel_initializer = "he_normal", padding = "same")(c9)
out = ks.layers.Conv2D(1, (1, 1), activation = "softmax")(c9)
model = ks.Model(inputs = layer_in, outputs = out)
model.compile(optimizer = "adam", loss = "sparse_categorical_crossentropy", metrics = ["accuracy"])
model.summary()最后，我定义了回调并运行了训练，结果产生了错误：
cllbs = [
ks.callbacks.EarlyStopping(patience = 4),
ks.callbacks.ModelCheckpoint(dir_out("Checkpoint.h5"), save_best_only = True),
ks.callbacks.TensorBoard(log_dir = './logs'),# log events for TensorBoard
]
model.fit(augGen, epochs = 5, validation_data = val_batches, callbacks = cllbs)- 完整的控制台输出**
这是运行最后一行时的完整输出（如果它有助于解决问题）：
trained = model.fit(augGen, epochs = 5, validation_data = val_batches, callbacks = cllbs)
Epoch 1/5
Traceback (most recent call last):
File "<ipython-input-68-f1422c6f17bb>", line 1, in <module>
trained = model.fit(augGen, epochs = 5, validation_data = val_batches, callbacks = cllbs)
File "c:\users\manuel\python\lib\site-packages\tensorflow\python\keras\engine\training.py", line 1183, in fit
tmp_logs = self.train_function(iterator)
File "c:\users\manuel\python\lib\site-packages\tensorflow\python\eager\def_function.py", line 889, in __call__
result = self._call(*args, **kwds)
File "c:\users\manuel\python\lib\site-packages\tensorflow\python\eager\def_function.py", line 950, in _call
return self._stateless_fn(*args, **kwds)
File "c:\users\manuel\python\lib\site-packages\tensorflow\python\eager\function.py", line 3023, in __call__
return graph_function._call_flat(
File "c:\users\manuel\python\lib\site-packages\tensorflow\python\eager\function.py", line 1960, in _call_flat
return self._build_call_outputs(self._inference_function.call(
File "c:\users\manuel\python\lib\site-packages\tensorflow\python\eager\function.py", line 591, in call
outputs = execute.execute(
File "c:\users\manuel\python\lib\site-packages\tensorflow\python\eager\execute.py", line 59, in quick_execute
tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
InvalidArgumentError: required broadcastable shapes at loc(unknown)
[[node Equal (defined at <ipython-input-68-f1422c6f17bb>:1) ]] [Op:__inference_train_function_3847]
Function call stack:
train_function
6条答案
按热度按时间2admgd591#
当类标签的数量与输出层的输出形状不匹配时，我就遇到了这个问题。
例如，如果有10个类标签，并且我们将输出层定义为：
由于类标签的数量（
10）不等于输出形状（5）。那么，我们将得到这个错误。确保类标签的数量与输出图层的输出形状匹配。
k2fxgqgv2#
我在这里发现了几个问题，这个模型本来是要用于多个类的语义分割的（这就是为什么我将输出层激活更改为
"softmax"并设置"sparse_categorical_crossentropy"丢失）。因此，在ImageDataGenerators中，必须将class_mode设置为None。不提供classes。相反，我需要将手动分类的图像插入为y。我猜初学者会犯很多初学者的错误。mf98qq943#
只需在“完全连接”层之前添加***展平（）***层。
zpjtge224#
我遇到了同样的问题，因为我在模型中使用的n_classes数（对于输出层）与labels/masks数组中的实际类数不同。您有13个类，但您的输出层只有1个。最好的方法是避免硬编码类的数量，并且只在模型中传递一个变量（如n_classes），然后在调用模型之前声明此变量。例如n_classes = y_Train.shape[-1]或n_classes = len（np.unique（y_Train））
bybem2ql5#
试着检查
ks.layers.concatenate layers的输入是否是等维的。例如ks.layers.concatenate([u7, c3])，这里检查u7和c3Tensor是否是相同的形状，除了轴输入到函数ks.layers.concatenate。Axis = -1默认值是最后一个维度。为了说明如果你给出ks.layers.concatenate([u7,c3],axis=0)，则除了u7和c3两者的第一个轴之外，所有其它轴的维度应当精确匹配，例如u7.shape = [3,4,5], c3.shape = [6,4,5].wz8daaqr6#
即使我面临着同样的错误，请检查您所有的Concat层和乘法层，或任何层有类似的操作发生。
当尺寸不匹配且模型无法执行该操作时，此错误可见。