使用您自己保存的模型在Keras中转移学习

Question

I have seen some examples of transfer learning where one can use pre-trained models from keras.application (Xception, VGG16, VGG19, ResNet50 etc) but what I want is to transfer the learning from the model I saved using model.save('model.h5')

This is my current model:

model = Sequential()
model.add(Embedding(max_words, embedding_dim, input_length=maxlen))
model.add(LSTM(32))
model.add(Dropout(0.6))
model.add(Dense(2, activation='sigmoid'))

model.compile(optimizer='rmsprop', loss='binary_crossentropy',metrics=['acc'])

model.fit(sequences, labels, epochs=10, batch_size=32, validation_split=0.2)

Now, Instead of saying

model_base = keras.applications.vgg16.VGG16(include_top=False, weights='imagenet')

I want to load the saved model probably with load_model('model.h5') and add it as a layer to my current model.

Answer 1

try this

model = Sequential()
model.add(Embedding(max_words, embedding_dim, input_length=maxlen))
model.add(LSTM(32))
model.add(Dropout(0.6))
model.add(Dense(2, activation='sigmoid'))

model.layers[0].set_weights([embedding_matrix])
model.layers[0].trainable = False

model.load_weights('model.h5')
model.compile(optimizer='rmsprop', loss='binary_crossentropy',metrics=['acc'])

model_base = model

Dont forget to remove your classifier