ValueError：检查目标时出错：预期activation_6 的形状为（70，）但得到的数组形状为（71，）

Posted 2023-03-16

【中文标题】ValueError：检查目标时出错：预期activation_6 的形状为（70，）但得到的数组形状为（71，）

【英文标题】：ValueError: Error when checking target: expected activation_6 to have shape (70,) but got array with shape (71,)

【发布时间】：2021-08-12 02:24:04

【问题描述】：

我正在使用 CNN 创建人脸识别。我正在关注一个教程。我正在使用 Tensorflow==1.15。

该程序将拍摄用户面部的 70 张快照并将其保存在“数据集”文件夹中

我不断收到错误：

ValueError: 检查目标时出错：预期 activation_6 的形状为 (70,) 但得到的数组的形状为 (71,)

输入形状 - (32,32,1)

类(n_classes) - 70

K.clear_session()
n_faces = len(set(ids))

model = model((32,32,1),n_faces) #Calling Model given in next code block
faces = np.asarray(faces)
faces = np.array([downsample_image(ab) for ab in faces])
ids = np.asarray(ids)
faces = faces[:,:,:,np.newaxis]
print("Shape of Data: " + str(faces.shape))
print("Number of unique faces : " + str(n_faces))


ids = to_categorical(ids)

faces = faces.astype('float32')
faces /= 255.


x_train, x_test, y_train, y_test = train_test_split(faces,ids, test_size = 0.2, random_state = 0)

print(x_train.shape)
print(y_train.shape)
print(x_test.shape)
print(y_test.shape)

checkpoint = callbacks.ModelCheckpoint('trained_model.h5', monitor='val_acc',
                                           save_best_only=True, save_weights_only=True, verbose=1)
                                    
model.fit(x_train, y_train,
             batch_size=32,
             epochs=10,
             validation_data=(x_test, y_test),
             shuffle=True,callbacks=[checkpoint])

def model(input_shape,num_classes):    

    model = Sequential()

    model.add(Conv2D(32, (3, 3), input_shape=input_shape))
    model.add(Activation("relu"))

    model.add(Conv2D(64, (3, 3)))
    model.add(BatchNormalization())
    model.add(Activation("relu"))

    model.add(Conv2D(64, (1, 1)))
    model.add(Dropout(0.5))
    model.add(BatchNormalization())
    model.add(Activation("relu"))

    model.add(Conv2D(128, (3, 3)))
    model.add(Dropout(0.5))
    model.add(Activation("relu"))

    model.add(MaxPooling2D(pool_size=(2,2)))

    model.add(Conv2D(64, (1, 1)))
    model.add(Activation("relu"))

    model.add(Flatten())
    model.add(Dense(32))
    model.add(Dense(num_classes))
    model.add(Activation("softmax"))
    
    model.compile(loss='categorical_crossentropy',
              optimizer='sgd',
              metrics=['accuracy'])

    model.summary()
    return model

输出

Model: "sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_1 (Conv2D)            (None, 30, 30, 32)        320       
_________________________________________________________________
activation_1 (Activation)    (None, 30, 30, 32)        0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 28, 28, 64)        18496     
_________________________________________________________________
batch_normalization_1 (Batch (None, 28, 28, 64)        256       
_________________________________________________________________
activation_2 (Activation)    (None, 28, 28, 64)        0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 28, 28, 64)        4160      
_________________________________________________________________
dropout_1 (Dropout)          (None, 28, 28, 64)        0         
_________________________________________________________________
batch_normalization_2 (Batch (None, 28, 28, 64)        256       
_________________________________________________________________
activation_3 (Activation)    (None, 28, 28, 64)        0         
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 26, 26, 128)       73856     
_________________________________________________________________
dropout_2 (Dropout)          (None, 26, 26, 128)       0         
_________________________________________________________________
activation_4 (Activation)    (None, 26, 26, 128)       0         
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 13, 13, 128)       0         
_________________________________________________________________
conv2d_5 (Conv2D)            (None, 13, 13, 64)        8256      
_________________________________________________________________
activation_5 (Activation)    (None, 13, 13, 64)        0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 10816)             0         
_________________________________________________________________
dense_1 (Dense)              (None, 32)                346144    
_________________________________________________________________
dense_2 (Dense)              (None, 70)                2310      
_________________________________________________________________
activation_6 (Activation)    (None, 70)                0         
=================================================================
Total params: 454,054
Trainable params: 453,798
Non-trainable params: 256
_________________________________________________________________
Shape of Data: (70, 32, 32, 1)
Number of unique faces : 70

我正在计算 x_train, x_test, y_train, y_test 如下图所示

x_train, x_test, y_train, y_test = train_test_split(faces,ids, test_size = 0.2, random_state = 0)

输出

x_train - (56, 32, 32, 1)

y_train - (56, 71)

x_test - (14, 32, 32, 1)

y_test - (14, 71)

我对 CNN 层的尺寸做错了什么？ 请帮忙

【参考方案1】：

在您的 model.summary() 输出中，您会看到最终的密集层具有形状（无，70）。 None 代表您的批量大小，目前未知。那么 70 就是每个图像的输出维度。

从您的 y_train 和 y_pred 看来，您想要输出 71 个类，而不是 70 个，因此尺寸不匹配。您可以尝试将最后一个密集层更改为

model.add(Dense(num_classes+1))

这应该可行。我不知道为什么您的 y 值的长度与您的班级数量不同。一个原因可能是，有一个“无”类，所以应该在其他类中选择的类是正确的。这可以解释为什么如果你有 70 个类，你需要一个 71 维的输出。