如何将 keras 顺序模型恢复到构建之前的状态？

问题描述

我想重用相同的模型架构但具有不同的数据集，也就是说，以编程方式将输入层更改为不同的形状，并在需要时重置模型参数。

类似的东西

model = tf.keras.Sequential(
           tf.keras.layers.Dense(2)
)

optimizer = tf.optimizers.Adam()

losses=[tf.keras.losses.mean_absolute_percentage_error]

model.compile(optimizer=optimizer,loss=losses)

model.build(input_shape=(None,2))
# ... train model and evaluate

model.unbuild() # this doesn't exist
model.build(input_shape=(None,3))
# ... train model and evaluate on different dataset

有人知道执行此操作的简洁方法吗？

解决方法

您可以创建一个主干模型并重复使用它来构建任意数量的具有不同输入层的模型，主干模型的参数对于您创建的所有新模型将保持不变，如果您想重置参数，请构建新的主干模型，示例代码在这里：

import tensorflow as tf
from tensorflow.keras import layers,models
import numpy as np

input_shape_b = (16,)
# Backbone model
def build_backbone_model():
    inputs_b = layers.Input(shape=input_shape_b)
    h = layers.Dense(256,'relu')(inputs_b)
    outputs_b = layers.Dense(1,'sigmoid')(h)
    return models.Model(inputs_b,outputs_b,name="Backbone")
    
backbone_model = build_backbone_model()
backbone_model.summary()

def new_model_reuse_backbone(input_shape,name):
    inputs = layers.Input(shape=input_shape)
    h = layers.Dense(input_shape_b[0],'relu')(inputs)
    outputs = backbone_model(h)
    return models.Model(inputs,outputs,name=name)

# Will use backbone model we defined before
new_model_0 = new_model_reuse_backbone((32,),"new_model_0")
new_model_0.summary()

# Rebuild will reset backbone model's parameters
backbone_model = build_backbone_model()
new_model_1 = new_model_reuse_backbone((256,"new_model_1")
new_model_1.summary()

输出：

Model: "Backbone"
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
input_1 (InputLayer)         [(None,16)]              0
_________________________________________________________________
dense (Dense)                (None,256)               4352
_________________________________________________________________
dense_1 (Dense)              (None,1)                 257
=================================================================
Total params: 4,609
Trainable params: 4,609
Non-trainable params: 0
_________________________________________________________________
Model: "new_model_0"
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
input_2 (InputLayer)         [(None,32)]              0
_________________________________________________________________
dense_2 (Dense)              (None,16)                528
_________________________________________________________________
Backbone (Functional)        (None,1)                 4609
=================================================================
Total params: 5,137
Trainable params: 5,137
Non-trainable params: 0
_________________________________________________________________
Model: "new_model_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_3 (InputLayer)         [(None,256)]             0
_________________________________________________________________
dense_3 (Dense)              (None,16)                4112
_________________________________________________________________
Backbone (Functional)        (None,1)                 4609
=================================================================
Total params: 8,721
Trainable params: 8,721
Non-trainable params: 0
_________________________________________________________________