ValueError：检查目标时出错：预期 block5_pool 有 4 个维度，但得到了形状为 (60000, 10) 的数组

Posted 2023-03-13

【中文标题】ValueError：检查目标时出错：预期 block5_pool 有 4 个维度，但得到了形状为 (60000, 10) 的数组

【英文标题】：ValueError: Error when checking target: expected block5_pool to have 4 dimensions, but got array with shape (60000, 10)

【发布时间】：2019-11-13 18:21:33

【问题描述】：

我正在尝试使用 vgg16 使用 keras 对 mnist 数字进行分类。 产生的错误是：

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-17-fd172601765f> in <module>()
      1 # Train the the model
----> 2 history=model.fit(train_features, train_labels, batch_size=128, epochs=100,callbacks=callback, verbose=0, validation_split=0.2)

~\Anaconda3\lib\site-packages\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)
    950             sample_weight=sample_weight,
    951             class_weight=class_weight,
--> 952             batch_size=batch_size)
    953         # Prepare validation data.
    954         do_validation = False

~\Anaconda3\lib\site-packages\keras\engine\training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size)
    787                 feed_output_shapes,
    788                 check_batch_axis=False,  # Don't enforce the batch size.
--> 789                 exception_prefix='target')
    790 
    791             # Generate sample-wise weight values given the `sample_weight` and

~\Anaconda3\lib\site-packages\keras\engine\training_utils.py in standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
    126                         ': expected ' + names[i] + ' to have ' +
    127                         str(len(shape)) + ' dimensions, but got array '
--> 128                         'with shape ' + str(data_shape))
    129                 if not check_batch_axis:
    130                     data_shape = data_shape[1:]

ValueError: Error when checking target: expected block5_pool to have 4 dimensions, but got array with shape (60000, 10)

这是包含我所做的所有预处理和调整大小的代码。 我通过简单地堆叠三次将 28x28 单通道图像调整为 48x48 3 通道图像。 由于我是这个领域的新手，所以我不明白我哪里出了问题。

train_features=np.stack([train_features]*3,axis = -1)
test_features=np.stack([test_features]*3,axis = -1)

# Reshape images as per the tensor format required by tensorflow
train_features = train_features.reshape(-1, 28,28,3)
test_features= test_features.reshape (-1,28,28,3)

# Resize the images 48*48 as required by VGG16
from keras.preprocessing.image import img_to_array, array_to_img
train_features = np.asarray([img_to_array(array_to_img(im, scale=False).resize((48,48))) for im in train_features])
test_features = np.asarray([img_to_array(array_to_img(im, scale=False).resize((48,48))) for im in test_features])
train_features.shape, test_features.shape

#normalising the training and testing features 
train_features = train_features.astype('float32')
test_features = test_features .astype('float32')
train_features /= 255
test_features  /= 255

# Converting Labels to one hot encoded format
test_labels = to_categorical(test_labels,10)
train_labels = to_categorical(train_labels,10)

# Preprocessing the input 
train_features = preprocess_input(train_features)
test_features  = preprocess_input (test_features)

model = VGG16(weights=None, include_top=False)
input = Input(shape=(48,48,3),name = 'image_input')
#Use the generated model 
output = model(input)

#Add the fully-connected layers 
x = Flatten(name='flatten')(output)
x = Dense(4096, activation='relu', name='fc1')(x)
x = Dense(4096, activation='relu', name='fc2')(x)
x = Dense(10, activation='softmax', name='predictions')(x)

#Create your own model 
vgg16_model = Model(input=input, output=x)

model.compile(optimizer='adam',loss='categorical_crossentropy', metrics=['accuracy'])

# Train the the model
history=model.fit(train_features, train_labels, batch_size=128, epochs=100,callbacks=callback, verbose=0, validation_split=0.2)

模型总结如下：

Layer (type)                 Output Shape              Param #   
=================================================================
input_2 (InputLayer)         (None, None, None, 3)     0         
_________________________________________________________________
block1_conv1 (Conv2D)        (None, None, None, 64)    1792      
_________________________________________________________________
block1_conv2 (Conv2D)        (None, None, None, 64)    36928     
_________________________________________________________________
block1_pool (MaxPooling2D)   (None, None, None, 64)    0         
_________________________________________________________________
block2_conv1 (Conv2D)        (None, None, None, 128)   73856     
_________________________________________________________________
block2_conv2 (Conv2D)        (None, None, None, 128)   147584    
_________________________________________________________________
block2_pool (MaxPooling2D)   (None, None, None, 128)   0         
_________________________________________________________________
block3_conv1 (Conv2D)        (None, None, None, 256)   295168    
_________________________________________________________________
block3_conv2 (Conv2D)        (None, None, None, 256)   590080    
_________________________________________________________________
block3_conv3 (Conv2D)        (None, None, None, 256)   590080    
_________________________________________________________________
block3_pool (MaxPooling2D)   (None, None, None, 256)   0         
_________________________________________________________________
block4_conv1 (Conv2D)        (None, None, None, 512)   1180160   
_________________________________________________________________
block4_conv2 (Conv2D)        (None, None, None, 512)   2359808   
_________________________________________________________________
block4_conv3 (Conv2D)        (None, None, None, 512)   2359808   
_________________________________________________________________
block4_pool (MaxPooling2D)   (None, None, None, 512)   0         
_________________________________________________________________
block5_conv1 (Conv2D)        (None, None, None, 512)   2359808   
_________________________________________________________________
block5_conv2 (Conv2D)        (None, None, None, 512)   2359808   
_________________________________________________________________
block5_conv3 (Conv2D)        (None, None, None, 512)   2359808   
_________________________________________________________________
block5_pool (MaxPooling2D)   (None, None, None, 512)   0         
=================================================================
Total params: 14,714,688
Trainable params: 14,714,688
Non-trainable params: 0
_________________________________________________________________
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
image_input (InputLayer)     (None, 48, 48, 3)         0         
_________________________________________________________________
vgg16 (Model)                multiple                  14714688  
_________________________________________________________________
flatten (Flatten)            (None, 512)               0         
_________________________________________________________________
fc1 (Dense)                  (None, 4096)              2101248   
_________________________________________________________________
fc2 (Dense)                  (None, 4096)              16781312  
_________________________________________________________________
predictions (Dense)          (None, 10)                40970     
=================================================================
Total params: 33,638,218
Trainable params: 33,638,218
Non-trainable params: 0
_________________________________________________________________

我们将不胜感激。

【参考方案1】：

Keras 抱怨你的目标，这是因为你的模型的输出形状不正确，因为它没有分类 (Dense) 层，试试这个：

model = VGG16(weights=None, include_top=False,input_shape=(48,48,3))
model.add(Dense(64, activation='relu'))
model.add(Dense(10, activation='softmax'))

model.compile(optimizer='adam',loss='categorical_crossentropy', metrics=['accuracy'])

【讨论】：

#Add the fully-connected layers x = Flatten(name='flatten')(output) x = Dense(4096, activation='relu', name='fc1')(x) x = Dense(4096, activation='relu', name='fc2')(x) x = Dense(10, activation='softmax', name='predictions')(x) 我确实添加了这段代码，但错误仍然存​​在。

@ShreyaPandita 您确定错误是相同的，而不是看起来相似的不同错误吗？最好包含新错误。

ValueError: Error when checking target: expected block5_pool to have 4 dimensions, but got array with shape (60000, 10) 我相信这是同样的错误。如果这也有帮助的话，我确实添加了模型摘要。

@ShreyaPandita 啊我现在看到了问题，错误是一样的，因为您正在拟合旧模型（模型）而不是新模型（vgg16_model）。请注意，如果您遵循我的回答中的建议，就不会发生这种情况。

我真的不敢相信这是一个愚蠢的错误。非常感谢