我想使用VGG
模型(tensorflow或keras预训练模型)作为特征提取器;我加载了VGG16 model
:
IMG_SHAPE = (224, 224, 3)
vgg16 = tf.keras.applications.VGG16(input_shape = IMG_SHAPE,
include_top=False,
weights='imagenet')
现在,如果我有一批图像
image_batch =np.ones((5,224,224,3),np.float32)
我可以通过以下方式获得VGG16的最后一层
last_layer = vgg16(image_batch)
有人知道在给定输入图像image_batch的情况下获取中间层特征吗?那就是我想提取给定图像的低级特征。非常感谢你!
答案 0 :(得分:1)
您可以执行以下操作:
IMG_SHAPE = (224, 224, 3)
model = tf.keras.applications.VGG16(input_shape = IMG_SHAPE,
include_top=False,
weights=None)
pretrain_model_path = "weights/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5"
model.load_weights(pretrain_model_path)
# print(model.summary())
image_batch = np.ones((5,224,224,3),np.float32)
last_layer = tf.keras.models.Model(inputs=model.input, outputs=model.get_layer('block5_pool').output)
res = last_layer.predict(image_batch)
但是,您如何知道在model.get_layer()
中传递什么?
回答-通过model.summary()
如果打印model.summary()
的输出,您将获得不同的图层名称,可以将其传递到model.get_layer()
中并获取该图层的输出。
Layer (type) Output Shape Param #
=================================================================
input_17 (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