[英]How can I use the Keras.applications' ResNeXt in TensorFlow's eager execution?
[英]How to use models from keras.applications for transfer learnig?
我想在Keras中获得预先训练的VGG16模型,删除它的输出层,然后放置一个新的输出层,其中包含适合我的问题的类数,然后将其放在新数据上。 出于这个原因,我试图在这里使用该模型: https : model.pop()
,但由于它不是Sequential,我不能只是model.pop()
。 从图层弹出并添加它也不起作用,因为在预测中它仍然期望旧的形状。 我该怎么办? 有没有办法将这种类型的模型转换为Sequential
?
您可以在model.layers
上使用pop()
,然后使用model.layers[-1].output
创建新图层。
例:
from keras.models import Model
from keras.layers import Dense,Flatten
from keras.applications import vgg16
from keras import backend as K
model = vgg16.VGG16(weights='imagenet', include_top=True)
model.input
model.summary(line_length=150)
model.layers.pop()
model.layers.pop()
model.summary(line_length=150)
new_layer = Dense(10, activation='softmax', name='my_dense')
inp = model.input
out = new_layer(model.layers[-1].output)
model2 = Model(inp, out)
model2.summary(line_length=150)
或者,您可以使用这些模型的include_top=False
选项。 在这种情况下,如果您需要使用展平图层,那么您还需要传递input_shape
。
model3 = vgg16.VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
model3.summary(line_length=150)
flatten = Flatten()
new_layer2 = Dense(10, activation='softmax', name='my_dense_2')
inp2 = model3.input
out2 = new_layer2(flatten(model3.output))
model4 = Model(inp2, out2)
model4.summary(line_length=150)
我们可以将VGG模型转换为Sequential:
# Create VGG model
vgg_model = keras.applications.vgg16.VGG16(weights='imagenet')
# Created model is of type Model
type(vgg_model)
>> keras.engine.training.Model
# Convert it to Sequential
model = Sequential()
for layer in vgg_model.layers:
model.add(layer)
# Now, check the model type, its Sequential!
type(model)
>> keras.models.Sequential
# Verify the model details
model.summary()
>>
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_15 (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
_________________________________________________________________
flatten (Flatten) (None, 25088) 0
_________________________________________________________________
fc1 (Dense) (None, 4096) 102764544
_________________________________________________________________
fc2 (Dense) (None, 4096) 16781312
_________________________________________________________________
predictions (Dense) (None, 1000) 4097000
=================================================================
Total params: 138,357,544
Trainable params: 138,357,544
Non-trainable params: 0
_________________________________________________________________
# Now, that its sequential, we can perform usual operations.
model.layers.pop()
# Freeze the layers
for layer in model.layers:
layer.trainable = False
# Add 'softmax' instead of earlier 'prediction' layer.
model.add(Dense(2, activation='softmax'))
# Check the summary, and yes new layer has been added.
model.summary()
Layer (type) Output Shape Param #
=================================================================
input_15 (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
_________________________________________________________________
flatten (Flatten) (None, 25088) 0
_________________________________________________________________
fc1 (Dense) (None, 4096) 102764544
_________________________________________________________________
fc2 (Dense) (None, 4096) 16781312
_________________________________________________________________
dense_4 (Dense) (None, 2) 2002
=================================================================
Total params: 134,262,546
Trainable params: 2,002
Non-trainable params: 134,260,544
_________________________________________________________________
声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.