【tensorflow2】ValueError: `labels.shape` must equal `logits.shape` except for the last dimension.

今天编写了一段CNN的代码在这里插入代码片

def cnnTrain(self, x_train, y_train, chnel):
    model = keras.models.Sequential()
    
    # 第一层卷积
    model.add(keras.layers.Conv2D(
                                    filters =  32,
                                    kernel_size = 3,
                                    padding = 'same',
                                    activation = 'selu',
                                    input_shape = (32, 32, 1)
                                                                 ))
    
    # 第二层卷积
    model.add(keras.layers.Conv2D(
                                    filters =  32,
                                    kernel_size = 3,
                                    padding = 'same',
                                    activation = 'selu'
                                                                ))
    
    # 池化层(最大池化)
    model.add(keras.layers.MaxPool2D(
                                        pool_size = 2
                                                        ))
    
    # 第三层卷积
    model.add(keras.layers.Conv2D(
                                    filters =  64,
                                    kernel_size = 3,
                                    padding = 'same',
                                    activation = 'selu'
                                                                ))
    
    # 第四层卷积
    model.add(keras.layers.Conv2D(
                                    filters =  64,
                                    kernel_size = 3,
                                    padding = 'same',
                                    activation = 'selu'
                                                                ))
    
    # 池化层(最大池化)
    model.add(keras.layers.MaxPool2D(
                                        pool_size = 2
                                                        ))
    
    # 第五层卷积
    model.add(keras.layers.Conv2D(
                                    filters =  128,
                                    kernel_size = 3,
                                    padding = 'same',
                                    activation = 'selu'
                                                                ))
    
    # 第六层卷积层
    model.add(keras.layers.Conv2D(
                                    filters =  128,
                                    kernel_size = 3,
                                    padding = 'same',
                                    activation = 'selu'
                                                                ))
    
    # 池化层(最大池化)
    model.add(keras.layers.MaxPool2D(
                                        pool_size = 2
                                                        ))
    
    # flatten层
    model.add(keras.layers.Flatten())
    
    # 全连接层
    model.add(keras.layers.Dense(128, activation = 'selu'))
    
    # softmax层(输出层)
    model.add(keras.layers.Dense(6, activation = 'softmax'))
    
    # 配置模型训练方法
    model.compile(
                    loss = 'sparse_categorical_crossentropy',
                    # optimizer = 'adam',
                    optimizer = tf.keras.optimizers.Adam(learning_rate = 0.001),
                    metrics = ['accuracy']
                                                                )
    
    # # 查看模型结构
    # model.summary()
    
    # 模型保存路径
    logdir = r'.\callbacks_cnn'
    
    # 文件夹是否存在
    if not os.path.exists(logdir):
          
        # # 若文件夹已存在先强制删除
        # shutil.rmtree(logdir) 
    
        # 创建文件夹
        os.mkdir(logdir) 
    
    # 模型保存路径
    global output_model_file
    
    output_model_file = os.path.join(logdir, 'feature_extract_model_'+ chnel +'.h5')
    
    # 创建callbacks数组
    callbacks = [
                    keras.callbacks.TensorBoard(logdir),
                    keras.callbacks.ModelCheckpoint(output_model_file, save_best_only = True), # 'save_best_only 表示只保存最优模型'
                    keras.callbacks.EarlyStopping(patience = 5, min_delta = 1e-3)          
                                                                                                ]
    
    # 模型训练
    history = model.fit(
                            x_train,
                            y_train,
                            batch_size = 32,
                            epochs = 30,
                            validation_split = 0.2,
                            # validation_data = (x_valid, y_valid) # validation_split 和 validation_data只能使用一个
                            callbacks = callbacks
                                                        )
    
    return history

数据标签是这样的：

将标签进行onehot编码：

y_train_oneHot = tf.keras.utils.to_categorical(y_train)

得到编码后的标签：

但是运行之后，代码报错：
ValueError: labels.shape must equal logits.shape except for the last dimension. Received: labels.shape=(192,) and logits.shape=(32, 6).
经过多次修改后后发现是代码中损失函数的问题：

当 **loss = ‘sparse_categorical_crossentropy’**时，数据的标签不能进行onehot编码，才能运行；
当数据标签进行了onehot编码后，改为 **loss = ‘categorical_crossentropy’**就能跑通了。