[英]Output layer for binary classification using keras ResNet50 model
[英]Intermediate Output of let' s say Resnet50 from Keras Model
import keras
print(keras.__version__)
#2.3.0
from keras.models import Sequential
from keras.layers import Input, Dense,TimeDistributed
from keras.models import Model
model = Sequential()
resnet = ResNet50(include_top = False, pooling = 'avg', weights = 'imagenet')
model.add(resnet)
model.add(Dense(10, activation = 'relu'))
model.add(Dense(6, activation = 'sigmoid'))
model.summary()
// 訓練 // model.fit(..) 完成
現在如何從圖層中提取 output?
model.layers[0]._name='resnet50'
print(model.layers[0].name) # prints resnet50
layer_output = model.get_layer("resnet50").output
intermediate_model = Model(inputs=[model.input, resnet.input], outputs=[layer_output])
result = intermediate_model.predict([x, x])
print(result.shape)
print(result[0].shape)
出錯了
AttributeError:層 re.net50 有多個入站節點,因此“層輸出”的概念定義不明確。 請改用
get_output_at(node_index)
。 添加代碼添加 Markdown
請再次嘗試使用tf.keras
導入 model 和圖層。
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Input, Dense,TimeDistributed
from tensorflow.keras.models import Model
然后運行相同的:
model.layers[0]._name='resnet50'
print(model.layers[0].name) # prints resnet50
layer_output = model.get_layer("resnet50").output
intermediate_model = Model(inputs=[model.input, resnet.input], outputs=[layer_output])
x = tf.ones((1, 250, 250, 3))
result = intermediate_model.predict([x, x])
print(result.shape)
print(result[0].shape)
Output:
resnet50
(1, 2048)
(2048,)
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