將我的數據擬合到 VGG16 cnn 時出現形狀錯誤 --Keras

Question

我想使用 VGG16 作為 cnn 使用數據增強和遷移學習對狗品種進行分類。

首先，我使用 keras 中的 ImageDataGenerator 進行一些數據增強

train_datagen = ImageDataGenerator(rotation_range = 30,
                               width_shift_range = 0.2,
                               height_shift_range = 0.2,
                               rescale = 1./255,
                               shear_range = 0.2,
                               zoom_range = 0.2,
                               horizontal_flip = True,
                               fill_mode = 'nearest')

train_generator = train_datagen.flow_from_directory('../data/train/',
                                                target_size = (224, 224),
                                                batch_size = batch_size,
                                                class_mode = 'categorical')

flow_from_directory方法返回一個 DirectoryIterator 產生 (x, y) 的元組，其中 x 是 numpy 數組，其中包含一批形狀為 (batch_size, *target_size, channels) 的圖像，y 是對應標簽的 numpy 數組。 因為這里的 class_mode 是分類模式，所以它應該返回 y 的 2D one-hot 編碼標簽。

然后我進行遷移學習，只刪除最后一層，用一個帶有 softmax 激活的密集層替換它。

model = VGG16(weights="imagenet", include_top=False, input_shape=(224, 224, 3))

for layer in model.layers:
    layer.trainable = False

x = model.output

predictions = Dense(120, activation='softmax')(x)

new_model = Model(inputs=model.input, outputs=predictions)

然后我將我的數據擬合到 model：

new_model.fit_generator(train_generator,
                        steps_per_epoch = 6680 // batch_size,
                        epochs = 50,
                        validation_data = validation_generator,
                        validation_steps = 835 // batch_size,
                        verbose=2)

我得到錯誤： ValueError: Error when checks target: expected dense_3 to have 4 dimensions, but got array with shape (16, 120)

我不知道問題出在哪里:(

謝謝你的幫助 ！

Answer 1

VGG16的總結給出：

Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         [(None, 224, 224, 3)]     0         
_________________________________________________________________
block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      
_________________________________________________________________
block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     
_________________________________________________________________
block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         
_________________________________________________________________
block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     
_________________________________________________________________
block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    
_________________________________________________________________
block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         
_________________________________________________________________
block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    
_________________________________________________________________
block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    
_________________________________________________________________
block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    
_________________________________________________________________
block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         
_________________________________________________________________
block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   
_________________________________________________________________
block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   
_________________________________________________________________
block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   
_________________________________________________________________
block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         
_________________________________________________________________
block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         
=================================================================
Total params: 14,714,688
Trainable params: 14,714,688
Non-trainable params: 0
_________________________________________________________________

最后一層具有 3-d 特征，您需要在應用 Dense 和 softmax 之前將其展平。

在最后一個密集層之前添加一個Flatten() 。

x = model.output

x = Flatten()(x) # add this line

predictions = Dense(120, activation='softmax')(x)