迁移学习示例
从 HDF5 加载预训练权重时，建议将权重加载到设置了检查点的原始模型中，然后将所需的权重/层提取到新模型中。
示例：
def create_functional_model():
inputs = keras.Input(shape=(784,), name="digits")
x = keras.layers.Dense(64, activation="relu", name="dense_1")(inputs)
x = keras.layers.Dense(64, activation="relu", name="dense_2")(x)
outputs = keras.layers.Dense(10, name="predictions")(x)
return keras.Model(inputs=inputs, outputs=outputs, name="3_layer_mlp")
functional_model = create_functional_model()
functional_model.save_weights("pretrained_weights.h5")
# In a separate program:
pretrained_model = create_functional_model()
pretrained_model.load_weights("pretrained_weights.h5")
# Create a new model by extracting layers from the original model:
extracted_layers = pretrained_model.layers[:-1]
extracted_layers.append(keras.layers.Dense(5, name="dense_3"))
model = keras.Sequential(extracted_layers)
model.summary()
Model: "sequential_6"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_1 (Dense) (None, 64) 50240
dense_2 (Dense) (None, 64) 4160
dense_3 (Dense) (None, 5) 325
=================================================================
Total params: 54,725
Trainable params: 54,725
Non-trainable params: 0
_________________________________________________________________
