Question

def create_keras_model():
model = Sequential([
    Conv2D(16, 3, padding='same', activation='relu'),
    MaxPooling2D(),
    Conv2D(32, 3, padding='same', activation='relu'),
    MaxPooling2D(),
    Conv2D(64, 3, padding='same', activation='relu'),
    MaxPooling2D(),
    Flatten(),
    Dense(512, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.001)),
    Dropout(0.5),
    Dense(1, activation='sigmoid')
])

model.load_weights('/content/drive/My Drive/localmodel/weights')
return model

在Colab中尝试过类似的操作，但我得到errno 21，它是一个目录。

然后我尝试了另一种方法，如下所示，

tff_model = create_keras_model() #now this function doesnt load weights, just returns a Sequential model   
tff.learning.assign_weights_to_keras_model(tff_model, model_with_weights)

就像assign_weights_to_keras_model（）将权重从tff_model转移到keras模型一样，我想将权重从keras模型转移到tff_model。该怎么办？

Answer 1

此处model_with_weights必须是代表模型权重的TFF值，例如：

def model_fn():

    keras_model = create_keras_model()

  return tff.learning.from_keras_model(keras_model)

fed_avg = tff.learning.build_federated_averaging_process(model_fn, ...)
state = fed_avg.initialize()
state = fed_avg.next(state, ...)
...
tff.learning.assign_weights_to_keras_model(keras_model, state.model)

Answer 2

我只是知道如何做到这一点。这个想法是使用：

tff.learning.state_with_new_model_weights(state, trainable_weights_numpy, non_trainable_weights_numpy)

Documentation here

可训练的权重是从基准模型中获取的，并转换为numpy格式。

trainable_weights = []

for weights in baseline_model.trainable_weights:
    trainable_weights.append(weights.numpy())

当服务器具有部分数据而客户端具有相似的数据时，这特别有用。可能这可以用于迁移学习。

如何将权重从基准模型转移到联合模型？

2 个答案: