使用分类交叉熵的多类分类-ValueError：形状3，1和1，3不兼容

问题描述

我正在22500张图像上建立一个多分类器。

标签有3个类别-0,1,2

我热编码了y标签，如下所示：

y_train = tf.one_hot(y_train,3)
y_test = tf.one_hot(y_test,3)
y_val = tf.one_hot(y_val,3)

由于数据量很大，因此我正在使用tf.data.Dataset对象预处理数据。 我已经使用dataset.zip压缩了数据和标签

#creating zipped tuples ofdata and label
data_set_train = tf.data.Dataset.zip((X_train,y_train))
data_set_test = tf.data.Dataset.zip((X_test,y_test))
data_set_val = tf.data.Dataset.zip((X_val,y_val))

并应用了预处理功能

def pre_process(x,y):
     x_norm = (x - mean_Rot_MIP) / Var_Rot_MIP
     # Stacking along the last dimension to avoid having to move channel axis
     x_norm_3ch = tf.stack((x_norm,x_norm,x_norm),axis=-1)
     x_norm_3ch = tf.reshape(x_norm_3ch,[1,224,3])
     return x_norm_3ch,y


#creating dataset iterable with all transaformations
X_train1 = data_set_train.map(pre_process)
X_test1 = data_set_test.map(pre_process)
X_val1 = data_set_val.map(pre_process)

数据集对象包含一个数据张量和y标签张量的元组，例如：

(<tf.Tensor: shape=(1,3),dtype=float64,numpy=
array([[[[-1.02143877,-1.02143877,-1.02143877],[-1.02143877,...,-1.02143877]]]])>,<tf.Tensor: shape=(3,),dtype=float32,numpy=array([1.,0.,0.],dtype=float32)>)

每个输入的形状为：（1,3） y标签的形状为：（3，）

我正在使用带有少量其他头部层的RESNET50进行分类

baseModel = ResNet50(weights=None,include_top=False,input_tensor=Input(shape=(224,3)))
headModel = baseModel.output
headModel = AveragePooling2D(pool_size=(7,7))(headModel)
headModel = Flatten(name="flatten")(headModel)
headModel = Dense(256,activation="relu")(headModel)
headModel = Dropout(0.5)(headModel)
headModel = Dense(3,activation="softmax")(headModel)
model = Model(inputs=baseModel.input,outputs=headModel)

，并使用categorical_cross熵作为损失函数。

# compile the model
INIT_LR = 1e-4
BS = 16
NUM_EPOCHS = 20

opt = Adam(lr=INIT_LR,decay=INIT_LR / NUM_EPOCHS)
model.compile(loss="categorical_crossentropy",optimizer=opt,metrics=["accuracy"])

# train the model
H = model.fit(X_train1,batch_size = BS,validation_data=(X_val1),epochs = NUM_EPOCHS,shuffle =False)

当我拟合模型时，出现以下错误：

Traceback (most recent call last):

  File "<ipython-input-81-eda0da51ce9e>",line 1,in <module>
    H = model.fit(X_train1,shuffle =False)

  File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py",line 108,in _method_wrapper
    return method(self,*args,**kwargs)

  File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py",line 1098,in fit
    tmp_logs = train_function(iterator)

  File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py",line 780,in __call__
    result = self._call(*args,**kwds)

  File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py",line 814,in _call
    results = self._stateful_fn(*args,**kwds)

  File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\eager\function.py",line 2828,in __call__
    graph_function,args,kwargs = self._maybe_define_function(args,kwargs)

  File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\eager\function.py",line 3210,in _maybe_define_function
    return self._define_function_with_shape_relaxation(args,line 3142,in _define_function_with_shape_relaxation
    args,kwargs,override_flat_arg_shapes=relaxed_arg_shapes)

  File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\eager\function.py",line 3075,in _create_graph_function
    capture_by_value=self._capture_by_value),File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\framework\func_graph.py",line 986,in func_graph_from_py_func
    func_outputs = python_func(*func_args,**func_kwargs)

  File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py",line 600,in wrapped_fn
    return weak_wrapped_fn().__wrapped__(*args,**kwds)

  File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\framework\func_graph.py",line 973,in wrapper
    raise e.ag_error_metadata.to_exception(e)

ValueError: in user code:

    C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:806 train_function  *
        return step_function(self,iterator)
    C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:796 step_function  **
        outputs = model.distribute_strategy.run(run_step,args=(data,))
    C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:1211 run
        return self._extended.call_for_each_replica(fn,args=args,kwargs=kwargs)
    C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2585 call_for_each_replica
        return self._call_for_each_replica(fn,kwargs)
    C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2945 _call_for_each_replica
        return fn(*args,**kwargs)
    C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:789 run_step  **
        outputs = model.train_step(data)
    C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:749 train_step
        y,y_pred,sample_weight,regularization_losses=self.losses)
    C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\compile_utils.py:204 __call__
        loss_value = loss_obj(y_t,y_p,sample_weight=sw)
    C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\keras\losses.py:149 __call__
        losses = ag_call(y_true,y_pred)
    C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\keras\losses.py:253 call  **
        return ag_fn(y_true,**self._fn_kwargs)
    C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\util\dispatch.py:201 wrapper
        return target(*args,**kwargs)
    C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\keras\losses.py:1535 categorical_crossentropy
        return K.categorical_crossentropy(y_true,from_logits=from_logits)
    C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\util\dispatch.py:201 wrapper
        return target(*args,**kwargs)
    C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\keras\backend.py:4687 categorical_crossentropy
        target.shape.assert_is_compatible_with(output.shape)
    C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\framework\tensor_shape.py:1134 assert_is_compatible_with
        raise ValueError("Shapes %s and %s are incompatible" % (self,other))

    ValueError: Shapes (3,1) and (1,3) are incompatible

我在做什么错，如何解决？

解决方法

我认为使用zip()是解决这一问题的怪异方法。为什么不使用from_tensor_slices并进行批处理：

data_set_train = tf.data.Dataset.from_tensor_slices((X_train,y_train)).batch(4)

这应该有效。您的输出标签形状应为(4,3)。

更正的示例：

import tensorflow as tf

x = tf.random.uniform(minval=0,maxval=1,shape=(100,224,3),dtype=tf.float32)
y = tf.random.uniform(minval=0,maxval=3,),dtype=tf.int32)

y = tf.keras.utils.to_categorical(y,num_classes=3)

BS = 16
ds = tf.data.Dataset.from_tensor_slices((x,y)).batch(BS)

baseModel = tf.keras.applications.ResNet50(weights=None,include_top=False,input_tensor=tf.keras.Input(shape=(224,3)))
headModel = baseModel.output
headModel = tf.keras.layers.AveragePooling2D(pool_size=(7,7))(headModel)
headModel = tf.keras.layers.Flatten(name="flatten")(headModel)
headModel = tf.keras.layers.Dense(256,activation="relu")(headModel)
headModel = tf.keras.layers.Dropout(0.5)(headModel)
headModel = tf.keras.layers.Dense(3,activation="softmax")(headModel)
model = tf.keras.Model(inputs=baseModel.input,outputs=headModel)

INIT_LR = 1e-4
NUM_EPOCHS = 1

opt = tf.keras.optimizers.Adam(lr=INIT_LR,decay=INIT_LR / NUM_EPOCHS)
model.compile(loss="categorical_crossentropy",optimizer=opt,metrics=["accuracy"])

H = model.fit(ds,epochs = NUM_EPOCHS,shuffle=False)

,

问题在于y标签的形状。我使用classic重塑了它 我所做的唯一更改是在pre_process函数中。

tf.reshape(y,[1,3])

我敢肯定，还有其他方法可以实现我的目标，这一方法的改动很小。