只需重新創建模型的第一部分,直到您希望輸出的圖層(在您的情況下只有第一個密集圖層)。之後,您可以在新創建的模型中加載第一部分的訓練重量並進行編譯。
使用這個新模型預測的輸出將是圖層的輸出(在您的情況下是第一個密集圖層)。
from keras.models import Sequential
from keras.layers import Dense, Activation
import numpy as np
model = Sequential([
Dense(32, input_dim=784), # first number is output_dim
Activation('relu'),
Dense(10), # output_dim, input_dim is taken for granted from above
Activation('softmax'),
])
model.compile(optimizer='adam', loss='categorical_crossentropy')
#create some random data
n_features = 5
samples = np.random.randint(0, 10, 784*n_features).reshape(-1,784)
labels = np.arange(10*n_features).reshape(-1, 10)
#train your sample model
model.fit(samples, labels)
#create new model
new_model= Sequential([
Dense(32, input_dim=784), # first number is output_dim
Activation('relu')])
#set weights of the first layer
new_model.set_weights(model.layers[0].get_weights())
#compile it after setting the weights
new_model.compile(optimizer='adam', loss='categorical_crossentropy')
#get output of the first dens layer
output = new_model.predict(samples)