TF2.6：ValueError：模型无法保存，因为尚未设置输入形状

Posted 2023-02-16

【中文标题】TF2.6：ValueError：模型无法保存，因为尚未设置输入形状

【英文标题】：TF2.6: ValueError: Model cannot be saved because the input shapes have not been set

【发布时间】：2021-11-17 13:14:59

【问题描述】：

我想在 Google Colab 中使用迁移学习创建自定义模型。

import tensorflow as tf
from tensorflow.keras.layers import Conv2D
from tensorflow.python.keras.applications.xception import Xception

class MyModel(tf.keras.Model):

  def __init__(self, input_shape, num_classes=5, dropout_rate=0.5):
    super(MyModel, self).__init__()
    self.weight_dict = 
    self.weight_dict['backbone'] = Xception(input_shape=input_shape, weights='imagenet', include_top=False)
    self.weight_dict['outputs'] = Conv2D(num_classes, (1, 1), padding="same", activation="softmax")
    self.build((None,) + input_shape)

  def call(self, inputs, training=False):
    self.weight_dict['backbone'].trainable = False
    x = self.weight_dict['backbone'](inputs)
    x = self.weight_dict['outputs'](x)
    return x

model = MyModel(input_shape=(256, 256, 3))
model.save('./saved')

但是，我遇到了这个错误：

ValueError: Model `<__main__.MyModel object at 0x7fc66134bdd0>` cannot be saved because the input shapes have not been set. Usually, input shapes are automatically determined from calling `.fit()` or `.predict()`. To manually set the shapes, call `model.build(input_shape)`.

是的，没有呼叫.fit() 或.predict()。但是在类的__init__() 方法中有一个对.build 的调用。我该怎么办？

【问题讨论】：

你试过this，this吗？

@TFer2 非常感谢！第二个链接为我做了！如果您愿意，请发表您的评论作为答案，我会接受。

很高兴。

【参考方案1】：

如果该层尚未构建，compute_output_shape 将在该层上调用构建。这假定该层稍后将与与所提供的输入形状匹配的输入一起使用。

工作代码如下图

import tensorflow as tf
print(tf.__version__)
from tensorflow.keras.layers import Conv2D
from tensorflow.keras.applications.xception import Xception

class MyModel(tf.keras.Model):

  def __init__(self, input_shape, num_classes=5, dropout_rate=0.5):
    super(MyModel, self).__init__()
    self.weight_dict = 
    self.weight_dict['backbone'] = Xception(input_shape=input_shape, weights='imagenet', include_top=False)
    self.weight_dict['outputs'] = Conv2D(num_classes, (1, 1), padding="same", activation="softmax")
    self.build((None,) + input_shape)

  def call(self, inputs, training=False):
    self.weight_dict['backbone'].trainable = False
    x = self.weight_dict['backbone'](inputs)
    x = self.weight_dict['outputs'](x)
    return x

input_shape=(256, 256, 3)
model=MyModel(input_shape)

model.compute_output_shape(input_shape=(None, 256, 256, 3))
model.save('./saved')

输出：

2.6.0

Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/xception/xception_weights_tf_dim_ordering_tf_kernels_notop.h5
83689472/83683744 [==============================] - 1s 0us/step
INFO:tensorflow:Assets written to: ./saved/assets

更多信息可以参考here。